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"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :int = """philschmid/bart-large-cnn-samsum"""
_SCREAMING_SNAKE_CASE :Dict = (
"""This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
"""and returns a summary of the text."""
)
_SCREAMING_SNAKE_CASE :Any = """summarizer"""
_SCREAMING_SNAKE_CASE :str = AutoTokenizer
_SCREAMING_SNAKE_CASE :str = AutoModelForSeqaSeqLM
_SCREAMING_SNAKE_CASE :str = ["""text"""]
_SCREAMING_SNAKE_CASE :str = ["""text"""]
def _a ( self , _a ) -> str:
"""simple docstring"""
return self.pre_processor(_a , return_tensors="""pt""" , truncation=_a )
def _a ( self , _a ) -> Any:
"""simple docstring"""
return self.model.generate(**_a )[0]
def _a ( self , _a ) -> int:
"""simple docstring"""
return self.pre_processor.decode(_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a )
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __a (unittest.TestCase):
'''simple docstring'''
@property
def _a ( self ) -> Tuple:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = ort.SessionOptions()
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
return options
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE__ : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE__ : Dict = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE__ : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = pipe(
prompt=_a , image=_a , mask_image=_a , guidance_scale=7.5 , num_inference_steps=10 , generator=_a , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[str] = output.images
SCREAMING_SNAKE_CASE__ : Optional[Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : int = np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE__ : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
SCREAMING_SNAKE_CASE__ : Dict = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=_a , safety_checker=_a , feature_extractor=_a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Any = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe(
prompt=_a , image=_a , mask_image=_a , guidance_scale=7.5 , num_inference_steps=20 , generator=_a , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[str] = output.images
SCREAMING_SNAKE_CASE__ : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : int = np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __a :
'''simple docstring'''
def __init__( self , _a , _a=14 , _a=7 , _a=True , _a=True , _a=False , _a=True , _a=99 , _a=32 , _a=4 , _a=4 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=0.02 , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = parent
SCREAMING_SNAKE_CASE__ : List[str] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = seq_length
SCREAMING_SNAKE_CASE__ : Dict = is_training
SCREAMING_SNAKE_CASE__ : Tuple = use_input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Any = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = rotary_dim
SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = vocab_size - 1
SCREAMING_SNAKE_CASE__ : str = vocab_size - 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size - 1
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : List[Any] = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=_a , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def _a ( self , _a , _a , _a , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 20
SCREAMING_SNAKE_CASE__ : int = model_class_name(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = model.init_cache(input_ids.shape[0] , _a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE__ : Any = model(
input_ids[:, :-1] , attention_mask=_a , past_key_values=_a , position_ids=_a , )
SCREAMING_SNAKE_CASE__ : str = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ : str = model(
input_ids[:, -1:] , attention_mask=_a , past_key_values=outputs_cache.past_key_values , position_ids=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a )
SCREAMING_SNAKE_CASE__ : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
def _a ( self , _a , _a , _a , _a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = 20
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_class_name(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
SCREAMING_SNAKE_CASE__ : Any = model.init_cache(input_ids.shape[0] , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids[:, :-1] , attention_mask=_a , past_key_values=_a , position_ids=_a , )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=_a , position_ids=_a , )
SCREAMING_SNAKE_CASE__ : Dict = model(_a , attention_mask=_a )
SCREAMING_SNAKE_CASE__ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
_SCREAMING_SNAKE_CASE :Tuple = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = FlaxGPTJModelTester(self )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(_a , _a , _a , _a )
def _a ( self ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
_a , _a , _a , _a )
@tooslow
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=_a , truncation=_a )
SCREAMING_SNAKE_CASE__ : Tuple = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = False
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model.config.eos_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.jit(model.generate )
SCREAMING_SNAKE_CASE__ : Optional[int] = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.batch_decode(_a , skip_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(_a , _a )
@is_pt_flax_cross_test
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE__ : Dict = self._prepare_for_class(_a , _a )
SCREAMING_SNAKE_CASE__ : int = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE__ : int = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE__ : Union[str, Any] = getattr(_a , _a )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE__ : Any = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_a ):
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 1
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : Tuple = pt_model_class(_a ).eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_class(_a , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _a )
SCREAMING_SNAKE_CASE__ : List[str] = fx_state
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = pt_model(**_a ).to_tuple()
SCREAMING_SNAKE_CASE__ : Optional[Any] = fx_model(**_a ).to_tuple()
self.assertEqual(len(_a ) , len(_a ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(_a , _a ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_a )
SCREAMING_SNAKE_CASE__ : str = model_class.from_pretrained(_a , from_pt=_a )
SCREAMING_SNAKE_CASE__ : List[str] = fx_model_loaded(**_a ).to_tuple()
self.assertEqual(
len(_a ) , len(_a ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(_a , _a ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE__ : List[Any] = self._prepare_for_class(_a , _a )
SCREAMING_SNAKE_CASE__ : List[str] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE__ : Dict = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE__ : Union[str, Any] = getattr(_a , _a )
SCREAMING_SNAKE_CASE__ : int = pt_model_class(_a ).eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_class(_a , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = load_flax_weights_in_pytorch_model(_a , fx_model.params )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE__ : Tuple = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_a ):
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
SCREAMING_SNAKE_CASE__ : int = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Tuple = pt_model(**_a ).to_tuple()
SCREAMING_SNAKE_CASE__ : Any = fx_model(**_a ).to_tuple()
self.assertEqual(len(_a ) , len(_a ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(_a , _a ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pt_model_class.from_pretrained(_a , from_flax=_a )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = pt_model_loaded(**_a ).to_tuple()
self.assertEqual(
len(_a ) , len(_a ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(_a , _a ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def _a ( self ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[str] = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import numpy as np
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1E-12 , __lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(__lowerCAmelCase )[0] == np.shape(__lowerCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(__lowerCAmelCase )[0] == np.shape(__lowerCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(__lowerCAmelCase ) == np.iscomplexobj(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = np.iscomplexobj(__lowerCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(__lowerCAmelCase , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
SCREAMING_SNAKE_CASE__ : Any = False
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : int = 1E12
while not convergence:
# Multiple matrix by the vector.
SCREAMING_SNAKE_CASE__ : str = np.dot(__lowerCAmelCase , __lowerCAmelCase )
# Normalize the resulting output vector.
SCREAMING_SNAKE_CASE__ : Tuple = w / np.linalg.norm(__lowerCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
SCREAMING_SNAKE_CASE__ : List[Any] = vector.conj().T if is_complex else vector.T
SCREAMING_SNAKE_CASE__ : Optional[int] = np.dot(__lowerCAmelCase , np.dot(__lowerCAmelCase , __lowerCAmelCase ) )
# Check convergence.
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : List[str] = lambda_
if is_complex:
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.real(lambda_ )
return lambda_, vector
def _lowercase ( ) -> None:
SCREAMING_SNAKE_CASE__ : Tuple = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
SCREAMING_SNAKE_CASE__ : Any = np.array([41, 4, 20] )
SCREAMING_SNAKE_CASE__ : Dict = real_input_matrix.astype(np.complexaaa )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
SCREAMING_SNAKE_CASE__ : Optional[Any] = real_input_matrix
SCREAMING_SNAKE_CASE__ : List[str] = real_vector
elif problem_type == "complex":
SCREAMING_SNAKE_CASE__ : int = complex_input_matrix
SCREAMING_SNAKE_CASE__ : Optional[Any] = complex_vector
# Our implementation.
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = power_iteration(__lowerCAmelCase , __lowerCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = np.linalg.eigh(__lowerCAmelCase )
# Last eigenvalue is the maximum one.
SCREAMING_SNAKE_CASE__ : Optional[int] = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
SCREAMING_SNAKE_CASE__ : Dict = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(__lowerCAmelCase ) - np.abs(__lowerCAmelCase ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase = 50 ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
a :List[str] = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) -> None:
"""simple docstring"""
warnings.warn(
"""The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use FlavaImageProcessor instead.""" , _a , )
super().__init__(*_a , **_a )
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_)
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(default="""audio-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True})
_SCREAMING_SNAKE_CASE :ClassVar[Features] = Features({"""audio""": Audio()})
_SCREAMING_SNAKE_CASE :ClassVar[Features] = Features({"""labels""": ClassLabel})
_SCREAMING_SNAKE_CASE :str = "audio"
_SCREAMING_SNAKE_CASE :str = "labels"
def _a ( self , _a ) -> Tuple:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , _a ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
SCREAMING_SNAKE_CASE__ : Tuple = copy.deepcopy(self )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.label_schema.copy()
SCREAMING_SNAKE_CASE__ : Optional[Any] = features[self.label_column]
SCREAMING_SNAKE_CASE__ : Tuple = label_schema
return task_template
@property
def _a ( self ) -> Dict[str, str]:
"""simple docstring"""
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :BigBirdConfig
_SCREAMING_SNAKE_CASE :jnp.dtype = jnp.floataa
_SCREAMING_SNAKE_CASE :bool = True
def _a ( self ) -> Dict:
"""simple docstring"""
super().setup()
SCREAMING_SNAKE_CASE__ : str = nn.Dense(5 , dtype=self.dtype )
def __call__( self , *_a , **_a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = super().__call__(*_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = FlaxBigBirdForNaturalQuestionsModule
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
def cross_entropy(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ):
SCREAMING_SNAKE_CASE__ : Dict = logits.shape[-1]
SCREAMING_SNAKE_CASE__ : str = (labels[..., None] == jnp.arange(__lowerCAmelCase )[None]).astype("""f4""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.nn.log_softmax(__lowerCAmelCase , axis=-1 )
SCREAMING_SNAKE_CASE__ : int = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
SCREAMING_SNAKE_CASE__ : str = reduction(__lowerCAmelCase )
return loss
SCREAMING_SNAKE_CASE__ : List[str] = partial(__lowerCAmelCase , reduction=jnp.mean )
SCREAMING_SNAKE_CASE__ : str = cross_entropy(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cross_entropy(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = cross_entropy(__lowerCAmelCase , __lowerCAmelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = "google/bigbird-roberta-base"
_SCREAMING_SNAKE_CASE :int = 30_00
_SCREAMING_SNAKE_CASE :int = 1_05_00
_SCREAMING_SNAKE_CASE :int = 1_28
_SCREAMING_SNAKE_CASE :int = 3
_SCREAMING_SNAKE_CASE :int = 1
_SCREAMING_SNAKE_CASE :int = 5
# tx_args
_SCREAMING_SNAKE_CASE :float = 3E-5
_SCREAMING_SNAKE_CASE :float = 0.0
_SCREAMING_SNAKE_CASE :int = 2_00_00
_SCREAMING_SNAKE_CASE :float = 0.00_95
_SCREAMING_SNAKE_CASE :str = "bigbird-roberta-natural-questions"
_SCREAMING_SNAKE_CASE :str = "training-expt"
_SCREAMING_SNAKE_CASE :str = "data/nq-training.jsonl"
_SCREAMING_SNAKE_CASE :str = "data/nq-validation.jsonl"
def _a ( self ) -> Optional[int]:
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=_a )
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.base_dir , self.save_dir )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.batch_size_per_device * jax.device_count()
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :int
_SCREAMING_SNAKE_CASE :int = 40_96 # no dynamic padding on TPUs
def __call__( self , _a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.collate_fn(_a )
SCREAMING_SNAKE_CASE__ : List[str] = jax.tree_util.tree_map(_a , _a )
return batch
def _a ( self , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.fetch_inputs(features["""input_ids"""] )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""input_ids""": jnp.array(_a , dtype=jnp.intaa ),
"""attention_mask""": jnp.array(_a , dtype=jnp.intaa ),
"""start_labels""": jnp.array(features["""start_token"""] , dtype=jnp.intaa ),
"""end_labels""": jnp.array(features["""end_token"""] , dtype=jnp.intaa ),
"""pooled_labels""": jnp.array(features["""category"""] , dtype=jnp.intaa ),
}
return batch
def _a ( self , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self._fetch_inputs(_a ) for ids in input_ids]
return zip(*_a )
def _a ( self , _a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = [1 for _ in range(len(_a ) )]
while len(_a ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> List[str]:
if seed is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = dataset.shuffle(seed=__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) // batch_size ):
SCREAMING_SNAKE_CASE__ : Optional[int] = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(__lowerCAmelCase )
@partial(jax.pmap , axis_name="""batch""" )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ) -> Tuple:
def loss_fn(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Any = model_inputs.pop("""start_labels""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = model_inputs.pop("""end_labels""" )
SCREAMING_SNAKE_CASE__ : str = model_inputs.pop("""pooled_labels""" )
SCREAMING_SNAKE_CASE__ : Dict = state.apply_fn(**__lowerCAmelCase , params=__lowerCAmelCase , dropout_rng=__lowerCAmelCase , train=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = outputs
return state.loss_fn(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = jax.random.split(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = jax.value_and_grad(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = grad_fn(state.params )
SCREAMING_SNAKE_CASE__ : Optional[int] = jax.lax.pmean({"""loss""": loss} , axis_name="""batch""" )
SCREAMING_SNAKE_CASE__ : int = jax.lax.pmean(__lowerCAmelCase , """batch""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = state.apply_gradients(grads=__lowerCAmelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="""batch""" )
def _lowercase ( __lowerCAmelCase , **__lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Dict = model_inputs.pop("""start_labels""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = model_inputs.pop("""end_labels""" )
SCREAMING_SNAKE_CASE__ : List[str] = model_inputs.pop("""pooled_labels""" )
SCREAMING_SNAKE_CASE__ : Dict = state.apply_fn(**__lowerCAmelCase , params=state.params , train=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = outputs
SCREAMING_SNAKE_CASE__ : Any = state.loss_fn(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = jax.lax.pmean({"""loss""": loss} , axis_name="""batch""" )
return metrics
class __a (train_state.TrainState):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Callable = struct.field(pytree_node=UpperCamelCase_)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Args
_SCREAMING_SNAKE_CASE :Callable
_SCREAMING_SNAKE_CASE :Callable
_SCREAMING_SNAKE_CASE :Callable
_SCREAMING_SNAKE_CASE :Callable
_SCREAMING_SNAKE_CASE :wandb
_SCREAMING_SNAKE_CASE :Callable = None
def _a ( self , _a , _a , _a , _a=None ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = model.params
SCREAMING_SNAKE_CASE__ : Any = TrainState.create(
apply_fn=model.__call__ , params=_a , tx=_a , loss_fn=_a , )
if ckpt_dir is not None:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = restore_checkpoint(_a , _a )
SCREAMING_SNAKE_CASE__ : str = {
"""lr""": args.lr,
"""init_lr""": args.init_lr,
"""warmup_steps""": args.warmup_steps,
"""num_train_steps""": num_train_steps,
"""weight_decay""": args.weight_decay,
}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = build_tx(**_a )
SCREAMING_SNAKE_CASE__ : int = train_state.TrainState(
step=_a , apply_fn=model.__call__ , params=_a , tx=_a , opt_state=_a , )
SCREAMING_SNAKE_CASE__ : int = args
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_collator
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lr
SCREAMING_SNAKE_CASE__ : int = params
SCREAMING_SNAKE_CASE__ : str = jax_utils.replicate(_a )
return state
def _a ( self , _a , _a , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.args
SCREAMING_SNAKE_CASE__ : int = len(_a ) // args.batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jax.random.split(_a , jax.device_count() )
for epoch in range(args.max_epochs ):
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.array(0 , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : int = get_batched_dataset(_a , args.batch_size , seed=_a )
SCREAMING_SNAKE_CASE__ : int = 0
for batch in tqdm(_a , total=_a , desc=f'''Running EPOCH-{epoch}''' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.data_collator(_a )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.train_step_fn(_a , _a , **_a )
running_loss += jax_utils.unreplicate(metrics["""loss"""] )
i += 1
if i % args.logging_steps == 0:
SCREAMING_SNAKE_CASE__ : Any = jax_utils.unreplicate(state.step )
SCREAMING_SNAKE_CASE__ : Optional[Any] = running_loss.item() / i
SCREAMING_SNAKE_CASE__ : Dict = self.scheduler_fn(state_step - 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.evaluate(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""step""": state_step.item(),
"""eval_loss""": eval_loss.item(),
"""tr_loss""": tr_loss,
"""lr""": lr.item(),
}
tqdm.write(str(_a ) )
self.logger.log(_a , commit=_a )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}''' , state=_a )
def _a ( self , _a , _a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_batched_dataset(_a , self.args.batch_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(_a ) // self.args.batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(0 , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
for batch in tqdm(_a , total=_a , desc="""Evaluating ... """ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.data_collator(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.val_step_fn(_a , **_a )
running_loss += jax_utils.unreplicate(metrics["""loss"""] )
i += 1
return running_loss / i
def _a ( self , _a , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = jax_utils.unreplicate(_a )
print(f'''SAVING CHECKPOINT IN {save_dir}''' , end=""" ... """ )
self.model_save_fn(_a , params=state.params )
with open(os.path.join(_a , """opt_state.msgpack""" ) , """wb""" ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(_a , """args.joblib""" ) )
joblib.dump(self.data_collator , os.path.join(_a , """data_collator.joblib""" ) )
with open(os.path.join(_a , """training_state.json""" ) , """w""" ) as f:
json.dump({"""step""": state.step.item()} , _a )
print("""DONE""" )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
print(F'''RESTORING CHECKPOINT FROM {save_dir}''' , end=""" ... """ )
with open(os.path.join(__lowerCAmelCase , """flax_model.msgpack""" ) , """rb""" ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] = from_bytes(state.params , f.read() )
with open(os.path.join(__lowerCAmelCase , """opt_state.msgpack""" ) , """rb""" ) as f:
SCREAMING_SNAKE_CASE__ : Dict = from_bytes(state.opt_state , f.read() )
SCREAMING_SNAKE_CASE__ : Any = joblib.load(os.path.join(__lowerCAmelCase , """args.joblib""" ) )
SCREAMING_SNAKE_CASE__ : Dict = joblib.load(os.path.join(__lowerCAmelCase , """data_collator.joblib""" ) )
with open(os.path.join(__lowerCAmelCase , """training_state.json""" ) , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : str = json.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = training_state["""step"""]
print("""DONE""" )
return params, opt_state, step, args, data_collator
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : int = num_train_steps - warmup_steps
SCREAMING_SNAKE_CASE__ : Union[str, Any] = optax.linear_schedule(init_value=__lowerCAmelCase , end_value=__lowerCAmelCase , transition_steps=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = optax.linear_schedule(init_value=__lowerCAmelCase , end_value=1E-7 , transition_steps=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
def weight_decay_mask(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[str] = traverse_util.flatten_dict(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
return traverse_util.unflatten_dict(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler_fn(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = optax.adamw(learning_rate=__lowerCAmelCase , weight_decay=__lowerCAmelCase , mask=__lowerCAmelCase )
return tx, lr
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :str = logging.get_logger(__name__)
a :Dict = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = """ctrl"""
_SCREAMING_SNAKE_CASE :Optional[int] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Dict = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _a=246_534 , _a=256 , _a=1_280 , _a=8_192 , _a=48 , _a=16 , _a=0.1 , _a=0.1 , _a=1E-6 , _a=0.02 , _a=True , **_a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = vocab_size
SCREAMING_SNAKE_CASE__ : Dict = n_positions
SCREAMING_SNAKE_CASE__ : Optional[Any] = n_embd
SCREAMING_SNAKE_CASE__ : List[str] = n_layer
SCREAMING_SNAKE_CASE__ : List[str] = n_head
SCREAMING_SNAKE_CASE__ : Optional[Any] = dff
SCREAMING_SNAKE_CASE__ : Dict = resid_pdrop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embd_pdrop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = use_cache
super().__init__(**_a )
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a :Dict = logging.get_logger(__name__)
a :Tuple = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
a :Any = {
"vocab_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli": (
"https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
),
},
}
a :Tuple = {
"squeezebert/squeezebert-uncased": 512,
"squeezebert/squeezebert-mnli": 512,
"squeezebert/squeezebert-mnli-headless": 512,
}
a :Dict = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Dict = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE :Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :List[Any] = SqueezeBertTokenizer
def __init__( self , _a=None , _a=None , _a=True , _a="[UNK]" , _a="[SEP]" , _a="[PAD]" , _a="[CLS]" , _a="[MASK]" , _a=True , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
_a , tokenizer_file=_a , do_lower_case=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , tokenize_chinese_chars=_a , strip_accents=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , _a ) != do_lower_case
or normalizer_state.get("""strip_accents""" , _a ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , _a ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : Dict = getattr(_a , normalizer_state.pop("""type""" ) )
SCREAMING_SNAKE_CASE__ : Tuple = do_lower_case
SCREAMING_SNAKE_CASE__ : Union[str, Any] = strip_accents
SCREAMING_SNAKE_CASE__ : Dict = tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Optional[int] = normalizer_class(**_a )
SCREAMING_SNAKE_CASE__ : int = do_lower_case
def _a ( self , _a , _a=None ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from manim import *
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE__ : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE__ : List[str] = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : List[str] = VGroup(*_a ).arrange(_a , buff=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = VGroup(*_a ).arrange(_a , buff=0 )
SCREAMING_SNAKE_CASE__ : Tuple = VGroup(_a , _a ).arrange(_a , buff=0 )
SCREAMING_SNAKE_CASE__ : int = Text("""CPU""" , font_size=24 )
SCREAMING_SNAKE_CASE__ : List[str] = Group(_a , _a ).arrange(_a , buff=0.5 , aligned_edge=_a )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = [mem.copy() for i in range(1 )]
SCREAMING_SNAKE_CASE__ : str = VGroup(*_a ).arrange(_a , buff=0 )
SCREAMING_SNAKE_CASE__ : List[str] = Text("""GPU""" , font_size=24 )
SCREAMING_SNAKE_CASE__ : List[Any] = Group(_a , _a ).arrange(_a , buff=0.5 , aligned_edge=_a )
gpu.align_to(_a , _a )
gpu.set_x(gpu.get_x() - 1 )
self.add(_a )
SCREAMING_SNAKE_CASE__ : List[str] = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = VGroup(*_a ).arrange(_a , buff=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Text("""Model""" , font_size=24 )
SCREAMING_SNAKE_CASE__ : int = Group(_a , _a ).arrange(_a , buff=0.5 , aligned_edge=_a )
model.move_to([3, -1.0, 0] )
self.play(
Create(_a , run_time=1 ) , Create(_a , run_time=1 ) , Create(_a , run_time=1 ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = MarkupText(
f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''' , font_size=24 , )
SCREAMING_SNAKE_CASE__ : Optional[int] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE__ : int = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_a , run_time=2.5 ) , Write(_a ) , Write(_a ) )
self.add(_a )
SCREAMING_SNAKE_CASE__ : List[str] = []
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i, rect in enumerate(_a ):
SCREAMING_SNAKE_CASE__ : Tuple = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_a , opacity=0.7 )
cpu_target.move_to(_a )
cpu_target.generate_target()
SCREAMING_SNAKE_CASE__ : Optional[int] = 0.46 / 4
SCREAMING_SNAKE_CASE__ : List[Any] = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_a )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_a , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_a , buff=0.0 )
cpu_targs.append(_a )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_a ) )
second_animations.append(MoveToTarget(_a , run_time=1.5 ) )
self.play(*_a )
self.play(*_a )
self.wait()
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
def _a ( self , _a , _a , _a , **_a ) -> Union[str, Any]:
"""simple docstring"""
self.events.append("""on_init_end""" )
def _a ( self , _a , _a , _a , **_a ) -> str:
"""simple docstring"""
self.events.append("""on_train_begin""" )
def _a ( self , _a , _a , _a , **_a ) -> Optional[Any]:
"""simple docstring"""
self.events.append("""on_train_end""" )
def _a ( self , _a , _a , _a , **_a ) -> str:
"""simple docstring"""
self.events.append("""on_epoch_begin""" )
def _a ( self , _a , _a , _a , **_a ) -> List[Any]:
"""simple docstring"""
self.events.append("""on_epoch_end""" )
def _a ( self , _a , _a , _a , **_a ) -> Optional[int]:
"""simple docstring"""
self.events.append("""on_step_begin""" )
def _a ( self , _a , _a , _a , **_a ) -> Union[str, Any]:
"""simple docstring"""
self.events.append("""on_step_end""" )
def _a ( self , _a , _a , _a , **_a ) -> int:
"""simple docstring"""
self.events.append("""on_evaluate""" )
def _a ( self , _a , _a , _a , **_a ) -> List[Any]:
"""simple docstring"""
self.events.append("""on_predict""" )
def _a ( self , _a , _a , _a , **_a ) -> Union[str, Any]:
"""simple docstring"""
self.events.append("""on_save""" )
def _a ( self , _a , _a , _a , **_a ) -> Dict:
"""simple docstring"""
self.events.append("""on_log""" )
def _a ( self , _a , _a , _a , **_a ) -> List[Any]:
"""simple docstring"""
self.events.append("""on_prediction_step""" )
@require_torch
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = tempfile.mkdtemp()
def _a ( self ) -> int:
"""simple docstring"""
shutil.rmtree(self.output_dir )
def _a ( self , _a=0 , _a=0 , _a=64 , _a=64 , _a=None , _a=False , **_a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = RegressionDataset(length=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = RegressionDataset(length=_a )
SCREAMING_SNAKE_CASE__ : List[str] = RegressionModelConfig(a=_a , b=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = RegressionPreTrainedModel(_a )
SCREAMING_SNAKE_CASE__ : Any = TrainingArguments(self.output_dir , disable_tqdm=_a , report_to=[] , **_a )
return Trainer(
_a , _a , train_dataset=_a , eval_dataset=_a , callbacks=_a , )
def _a ( self , _a , _a ) -> int:
"""simple docstring"""
self.assertEqual(len(_a ) , len(_a ) )
# Order doesn't matter
SCREAMING_SNAKE_CASE__ : List[str] = sorted(_a , key=lambda _a : cb.__name__ if isinstance(_a , _a ) else cb.__class__.__name__ )
SCREAMING_SNAKE_CASE__ : List[Any] = sorted(_a , key=lambda _a : cb.__name__ if isinstance(_a , _a ) else cb.__class__.__name__ )
for cba, cba in zip(_a , _a ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
self.assertEqual(_a , _a )
elif isinstance(_a , _a ) and not isinstance(_a , _a ):
self.assertEqual(_a , cba.__class__ )
elif not isinstance(_a , _a ) and isinstance(_a , _a ):
self.assertEqual(cba.__class__ , _a )
else:
self.assertEqual(_a , _a )
def _a ( self , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""on_init_end""", """on_train_begin"""]
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : List[Any] = len(trainer.get_eval_dataloader() )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["""on_prediction_step"""] * len(trainer.get_eval_dataloader() ) + ["""on_log""", """on_evaluate"""]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("""on_epoch_begin""" )
for _ in range(_a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("""on_log""" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("""on_save""" )
expected_events.append("""on_epoch_end""" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_trainer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
# Callbacks passed at init are added to the default callbacks
SCREAMING_SNAKE_CASE__ : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_trainer(disable_tqdm=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
SCREAMING_SNAKE_CASE__ : Any = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(_a )
expected_callbacks.remove(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_trainer()
SCREAMING_SNAKE_CASE__ : List[Any] = trainer.pop_callback(_a )
self.assertEqual(cb.__class__ , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
trainer.add_callback(_a )
expected_callbacks.insert(0 , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
# We can also add, pop, or remove by instance
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_trainer()
SCREAMING_SNAKE_CASE__ : Optional[int] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(_a )
expected_callbacks.remove(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_trainer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = trainer.callback_handler.callbacks[0]
SCREAMING_SNAKE_CASE__ : int = trainer.pop_callback(_a )
self.assertEqual(_a , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
trainer.add_callback(_a )
expected_callbacks.insert(0 , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="""ignore""" , category=_a )
SCREAMING_SNAKE_CASE__ : Dict = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
SCREAMING_SNAKE_CASE__ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
# Independent log/save/eval
SCREAMING_SNAKE_CASE__ : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
SCREAMING_SNAKE_CASE__ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
SCREAMING_SNAKE_CASE__ : Any = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
SCREAMING_SNAKE_CASE__ : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="""steps""" )
trainer.train()
SCREAMING_SNAKE_CASE__ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="""epoch""" )
trainer.train()
SCREAMING_SNAKE_CASE__ : List[Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
# A bit of everything
SCREAMING_SNAKE_CASE__ : Any = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="""steps""" , )
trainer.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
# warning should be emitted for duplicated callbacks
with patch("""transformers.trainer_callback.logger.warning""" ) as warn_mock:
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(_a ) in warn_mock.call_args[0][0]
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a :int = logging.get_logger(__name__)
a :Dict = {
"post_extract_proj": "feature_projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
for attribute in key.split(""".""" ):
SCREAMING_SNAKE_CASE__ : int = getattr(__lowerCAmelCase , __lowerCAmelCase )
if weight_type is not None:
SCREAMING_SNAKE_CASE__ : int = getattr(__lowerCAmelCase , __lowerCAmelCase ).shape
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
SCREAMING_SNAKE_CASE__ : Dict = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE__ : Dict = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE__ : int = value
else:
SCREAMING_SNAKE_CASE__ : str = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : Dict = fairseq_model.state_dict()
SCREAMING_SNAKE_CASE__ : str = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
SCREAMING_SNAKE_CASE__ : List[str] = False
if "conv_layers" in name:
load_conv_layer(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = True
else:
for key, mapped_key in MAPPING.items():
SCREAMING_SNAKE_CASE__ : Dict = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE__ : List[Any] = name.split(__lowerCAmelCase )[0].split(""".""" )[-2]
SCREAMING_SNAKE_CASE__ : List[str] = mapped_key.replace("""*""" , __lowerCAmelCase )
if "weight_g" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """weight_g"""
elif "weight_v" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """weight_v"""
elif "weight" in name:
SCREAMING_SNAKE_CASE__ : int = """weight"""
elif "bias" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = """bias"""
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
set_recursively(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
continue
if not is_used:
unused_weights.append(__lowerCAmelCase )
logger.warning(F'''Unused weights: {unused_weights}''' )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str = full_name.split("""conv_layers.""" )[-1]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.split(""".""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = int(items[0] )
SCREAMING_SNAKE_CASE__ : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
SCREAMING_SNAKE_CASE__ : int = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
SCREAMING_SNAKE_CASE__ : Any = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = SEWConfig()
if is_finetuned:
SCREAMING_SNAKE_CASE__ : Tuple = model.wav_encoder.wav_model.cfg
else:
SCREAMING_SNAKE_CASE__ : Dict = model.cfg
SCREAMING_SNAKE_CASE__ : Any = fs_config.conv_bias
SCREAMING_SNAKE_CASE__ : Optional[Any] = eval(fs_config.conv_feature_layers )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [x[0] for x in conv_layers]
SCREAMING_SNAKE_CASE__ : List[Any] = [x[1] for x in conv_layers]
SCREAMING_SNAKE_CASE__ : Dict = [x[2] for x in conv_layers]
SCREAMING_SNAKE_CASE__ : List[str] = """gelu"""
SCREAMING_SNAKE_CASE__ : str = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
SCREAMING_SNAKE_CASE__ : Any = 0.0
SCREAMING_SNAKE_CASE__ : Tuple = fs_config.activation_fn.name
SCREAMING_SNAKE_CASE__ : Tuple = fs_config.encoder_embed_dim
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.02
SCREAMING_SNAKE_CASE__ : str = fs_config.encoder_ffn_embed_dim
SCREAMING_SNAKE_CASE__ : str = 1E-5
SCREAMING_SNAKE_CASE__ : str = fs_config.encoder_layerdrop
SCREAMING_SNAKE_CASE__ : Optional[int] = fs_config.encoder_attention_heads
SCREAMING_SNAKE_CASE__ : int = fs_config.conv_pos_groups
SCREAMING_SNAKE_CASE__ : Optional[int] = fs_config.conv_pos
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = fs_config.encoder_layers
SCREAMING_SNAKE_CASE__ : Any = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.cfg
SCREAMING_SNAKE_CASE__ : str = fs_config.final_dropout
SCREAMING_SNAKE_CASE__ : Union[str, Any] = fs_config.layerdrop
SCREAMING_SNAKE_CASE__ : List[str] = fs_config.activation_dropout
SCREAMING_SNAKE_CASE__ : Any = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
SCREAMING_SNAKE_CASE__ : str = fs_config.attention_dropout
SCREAMING_SNAKE_CASE__ : Optional[Any] = fs_config.dropout_input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = fs_config.dropout
SCREAMING_SNAKE_CASE__ : Dict = fs_config.mask_channel_length
SCREAMING_SNAKE_CASE__ : int = fs_config.mask_channel_prob
SCREAMING_SNAKE_CASE__ : Tuple = fs_config.mask_length
SCREAMING_SNAKE_CASE__ : Tuple = fs_config.mask_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = """Wav2Vec2FeatureExtractor"""
SCREAMING_SNAKE_CASE__ : int = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=True ) -> str:
if is_finetuned:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
SCREAMING_SNAKE_CASE__ : str = SEWConfig.from_pretrained(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : str = convert_config(model[0] , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = model[0].eval()
SCREAMING_SNAKE_CASE__ : Dict = True if config.feat_extract_norm == """layer""" else False
SCREAMING_SNAKE_CASE__ : Union[str, Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , )
if is_finetuned:
if dict_path:
SCREAMING_SNAKE_CASE__ : str = Dictionary.load(__lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
SCREAMING_SNAKE_CASE__ : Tuple = target_dict.pad_index
SCREAMING_SNAKE_CASE__ : List[str] = target_dict.bos_index
SCREAMING_SNAKE_CASE__ : Any = target_dict.pad_index
SCREAMING_SNAKE_CASE__ : Any = target_dict.bos_index
SCREAMING_SNAKE_CASE__ : int = target_dict.eos_index
SCREAMING_SNAKE_CASE__ : List[str] = len(target_dict.symbols )
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.join(__lowerCAmelCase , """vocab.json""" )
if not os.path.isdir(__lowerCAmelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCAmelCase ) )
return
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = WavaVecaCTCTokenizer(
__lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCAmelCase , )
SCREAMING_SNAKE_CASE__ : int = WavaVecaProcessor(feature_extractor=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SEWForCTC(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : Dict = SEWModel(__lowerCAmelCase )
feature_extractor.save_pretrained(__lowerCAmelCase )
recursively_load_weights(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
hf_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
a :List[Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
a :Dict = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
a :int = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
a :List[Any] = TaTokenizerFast
a :Dict = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Any = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[int] = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
a :Tuple = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
a :Dict = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """huggingface/label-files"""
SCREAMING_SNAKE_CASE__ : Tuple = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE__ : Tuple = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE__ : str = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """std_conv""" if """bit""" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
SCREAMING_SNAKE_CASE__ : Dict = BitConfig(
conv_layer=__lowerCAmelCase , num_labels=1000 , idalabel=__lowerCAmelCase , labelaid=__lowerCAmelCase , )
return config
def _lowercase ( __lowerCAmelCase ) -> Any:
if "stem.conv" in name:
SCREAMING_SNAKE_CASE__ : str = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
SCREAMING_SNAKE_CASE__ : str = name.replace("""blocks""" , """layers""" )
if "head.fc" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""head.fc""" , """classifier.1""" )
if name.startswith("""norm""" ):
SCREAMING_SNAKE_CASE__ : str = """bit.""" + name
if "bit" not in name and "classifier" not in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = """bit.encoder.""" + name
return name
def _lowercase ( ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] = get_config(__lowerCAmelCase )
# load original model from timm
SCREAMING_SNAKE_CASE__ : str = create_model(__lowerCAmelCase , pretrained=__lowerCAmelCase )
timm_model.eval()
# load state_dict of original model
SCREAMING_SNAKE_CASE__ : int = timm_model.state_dict()
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ : int = state_dict.pop(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = val.squeeze() if """head""" in key else val
# load HuggingFace model
SCREAMING_SNAKE_CASE__ : Any = BitForImageClassification(__lowerCAmelCase )
model.eval()
model.load_state_dict(__lowerCAmelCase )
# create image processor
SCREAMING_SNAKE_CASE__ : Dict = create_transform(**resolve_data_config({} , model=__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transform.transforms
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
SCREAMING_SNAKE_CASE__ : Tuple = BitImageProcessor(
do_resize=__lowerCAmelCase , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__lowerCAmelCase , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__lowerCAmelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = transform(__lowerCAmelCase ).unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any = processor(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(__lowerCAmelCase , __lowerCAmelCase )
# verify logits
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] = model(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits
print("""Logits:""" , logits[0, :3] )
print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] )
SCREAMING_SNAKE_CASE__ : Optional[int] = timm_model(__lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__lowerCAmelCase , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
if push_to_hub:
print(F'''Pushing model {model_name} and processor to the hub''' )
model.push_to_hub(F'''ybelkada/{model_name}''' )
processor.push_to_hub(F'''ybelkada/{model_name}''' )
if __name__ == "__main__":
a :str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model to the hub.",
)
a :List[Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int = int(__lowerCAmelCase )
assert noofclusters < len(__lowerCAmelCase )
# Find out the dimensionality
SCREAMING_SNAKE_CASE__ : int = len(vectors[0] )
# Will help select random centroids from among the available vectors
SCREAMING_SNAKE_CASE__ : List[Any] = list(range(len(__lowerCAmelCase ) ) )
shuffle(__lowerCAmelCase )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
SCREAMING_SNAKE_CASE__ : Any = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
SCREAMING_SNAKE_CASE__ : Optional[int] = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(__lowerCAmelCase )
]
##These nodes will assign the centroid Variables the appropriate
##values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.placeholder("""float64""" , [dim] )
SCREAMING_SNAKE_CASE__ : Dict = []
for centroid in centroids:
cent_assigns.append(tf.assign(__lowerCAmelCase , __lowerCAmelCase ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
SCREAMING_SNAKE_CASE__ : Optional[Any] = [tf.Variable(0 ) for i in range(len(__lowerCAmelCase ) )]
##These nodes will assign an assignment Variable the appropriate
##value
SCREAMING_SNAKE_CASE__ : Dict = tf.placeholder("""int32""" )
SCREAMING_SNAKE_CASE__ : List[str] = []
for assignment in assignments:
cluster_assigns.append(tf.assign(__lowerCAmelCase , __lowerCAmelCase ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
SCREAMING_SNAKE_CASE__ : List[Any] = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
SCREAMING_SNAKE_CASE__ : List[Any] = tf.reduce_mean(__lowerCAmelCase , 0 )
##Node for computing Euclidean distances
# Placeholders for input
SCREAMING_SNAKE_CASE__ : Dict = tf.placeholder("""float""" , [dim] )
SCREAMING_SNAKE_CASE__ : Tuple = tf.placeholder("""float""" , [dim] )
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCAmelCase , __lowerCAmelCase ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.placeholder("""float""" , [noofclusters] )
SCREAMING_SNAKE_CASE__ : Any = tf.argmin(__lowerCAmelCase , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
SCREAMING_SNAKE_CASE__ : Any = tf.initialize_all_variables()
# Initialize all variables
sess.run(__lowerCAmelCase )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 100
for _ in range(__lowerCAmelCase ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : Dict = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
SCREAMING_SNAKE_CASE__ : int = [
sess.run(__lowerCAmelCase , feed_dict={va: vect, va: sess.run(__lowerCAmelCase )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
SCREAMING_SNAKE_CASE__ : Optional[Any] = sess.run(
__lowerCAmelCase , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(__lowerCAmelCase ):
# Collect all the vectors assigned to this cluster
SCREAMING_SNAKE_CASE__ : Any = [
vectors[i]
for i in range(len(__lowerCAmelCase ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
SCREAMING_SNAKE_CASE__ : Optional[int] = sess.run(
__lowerCAmelCase , feed_dict={mean_input: array(__lowerCAmelCase )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
SCREAMING_SNAKE_CASE__ : Dict = sess.run(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = sess.run(__lowerCAmelCase )
return centroids, assignments
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class __a :
'''simple docstring'''
def __init__( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {}
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {}
def _a ( self , _a , _a , _a ) -> None:
"""simple docstring"""
if nodea not in self.connections:
self.add_node(_a )
if nodea not in self.connections:
self.add_node(_a )
SCREAMING_SNAKE_CASE__ : int = probability
def _a ( self ) -> list[str]:
"""simple docstring"""
return list(self.connections )
def _a ( self , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = 0
SCREAMING_SNAKE_CASE__ : List[Any] = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, int]:
SCREAMING_SNAKE_CASE__ : Any = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = Counter(graph.get_nodes() )
SCREAMING_SNAKE_CASE__ : Optional[Any] = start
for _ in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = graph.transition(__lowerCAmelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = UnCLIPImageVariationPipeline
_SCREAMING_SNAKE_CASE :int = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
_SCREAMING_SNAKE_CASE :Tuple = IMAGE_VARIATION_BATCH_PARAMS
_SCREAMING_SNAKE_CASE :Optional[Any] = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
_SCREAMING_SNAKE_CASE :Tuple = False
@property
def _a ( self ) -> int:
"""simple docstring"""
return 32
@property
def _a ( self ) -> List[Any]:
"""simple docstring"""
return 32
@property
def _a ( self ) -> List[str]:
"""simple docstring"""
return self.time_input_dim
@property
def _a ( self ) -> Dict:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self ) -> Any:
"""simple docstring"""
return 100
@property
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def _a ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(_a )
@property
def _a ( self ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(_a )
@property
def _a ( self ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""clip_embeddings_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""cross_attention_dim""": self.cross_attention_dim,
}
SCREAMING_SNAKE_CASE__ : List[Any] = UnCLIPTextProjModel(**_a )
return model
@property
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""sample_size""": 32,
# RGB in channels
"""in_channels""": 3,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 6,
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": """identity""",
}
SCREAMING_SNAKE_CASE__ : List[str] = UNetaDConditionModel(**_a )
return model
@property
def _a ( self ) -> List[str]:
"""simple docstring"""
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(1 )
SCREAMING_SNAKE_CASE__ : List[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.dummy_decoder
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_text_proj
SCREAMING_SNAKE_CASE__ : Any = self.dummy_text_encoder
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dummy_tokenizer
SCREAMING_SNAKE_CASE__ : Any = self.dummy_super_res_first
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_super_res_last
SCREAMING_SNAKE_CASE__ : int = UnCLIPScheduler(
variance_type="""learned_range""" , prediction_type="""epsilon""" , num_train_timesteps=1_000 , )
SCREAMING_SNAKE_CASE__ : Dict = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""epsilon""" , num_train_timesteps=1_000 , )
SCREAMING_SNAKE_CASE__ : List[Any] = CLIPImageProcessor(crop_size=32 , size=32 )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def _a ( self , _a , _a=0 , _a=True ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device=_a ).manual_seed(_a )
if pil_image:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_image * 0.5 + 0.5
SCREAMING_SNAKE_CASE__ : int = input_image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : int = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
SCREAMING_SNAKE_CASE__ : str = DiffusionPipeline.numpy_to_pil(_a )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = """cpu"""
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[int] = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : str = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe(**_a )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe(
**_a , return_dict=_a , )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : str = np.array(
[
0.9_997,
0.0_002,
0.9_997,
0.9_997,
0.9_969,
0.0_023,
0.9_997,
0.9_969,
0.9_970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = """cpu"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : str = pipe(**_a )
SCREAMING_SNAKE_CASE__ : List[str] = output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : Any = pipe(
**_a , return_dict=_a , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Any = np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = """cpu"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : int = [
pipeline_inputs["""image"""],
pipeline_inputs["""image"""],
]
SCREAMING_SNAKE_CASE__ : int = pipe(**_a )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [
tuple_pipeline_inputs["""image"""],
tuple_pipeline_inputs["""image"""],
]
SCREAMING_SNAKE_CASE__ : int = pipe(
**_a , return_dict=_a , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array(
[
0.9_997,
0.9_989,
0.0_008,
0.0_021,
0.9_960,
0.0_018,
0.0_014,
0.0_002,
0.9_933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = torch.device("""cpu""" )
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = 1
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.Generator(device=_a ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = pipe.decoder.dtype
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : List[Any] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
SCREAMING_SNAKE_CASE__ : Any = pipe.prepare_latents(
_a , dtype=_a , device=_a , generator=_a , latents=_a , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE__ : Tuple = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
SCREAMING_SNAKE_CASE__ : List[Any] = pipe.prepare_latents(
_a , dtype=_a , device=_a , generator=_a , latents=_a , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_inputs(_a , pil_image=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = pipe(
**_a , decoder_latents=_a , super_res_latents=_a ).images
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_inputs(_a , pil_image=_a )
# Don't pass image, instead pass embedding
SCREAMING_SNAKE_CASE__ : Dict = pipeline_inputs.pop("""image""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe.image_encoder(_a ).image_embeds
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe(
**_a , decoder_latents=_a , super_res_latents=_a , image_embeddings=_a , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = torch_device == """cpu"""
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
SCREAMING_SNAKE_CASE__ : Tuple = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=_a , expected_max_diff=_a )
@skip_mps
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = torch_device == """cpu"""
SCREAMING_SNAKE_CASE__ : List[str] = True
SCREAMING_SNAKE_CASE__ : List[str] = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
self._test_inference_batch_single_identical(
test_max_difference=_a , relax_max_difference=_a , additional_params_copy_to_batched_inputs=_a , )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
SCREAMING_SNAKE_CASE__ : List[Any] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=_a , additional_params_copy_to_batched_inputs=_a , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=_a )
@skip_mps
def _a ( self ) -> int:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def _a ( self ) -> Optional[int]:
"""simple docstring"""
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""" )
SCREAMING_SNAKE_CASE__ : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/unclip/karlo_v1_alpha_cat_variation_fp16.npy""" )
SCREAMING_SNAKE_CASE__ : List[Any] = UnCLIPImageVariationPipeline.from_pretrained(
"""kakaobrain/karlo-v1-alpha-image-variations""" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : int = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = pipeline(
_a , generator=_a , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Any = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(_a , _a , 15 )
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'{price_plus_tax(100, 0.25) = }')
print(f'{price_plus_tax(125.50, 0.05) = }')
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a :str = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
"ChineseCLIPOnnxConfig",
"ChineseCLIPTextConfig",
"ChineseCLIPVisionConfig",
],
"processing_chinese_clip": ["ChineseCLIPProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[str] = ["ChineseCLIPFeatureExtractor"]
a :List[str] = ["ChineseCLIPImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = [
"CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ChineseCLIPModel",
"ChineseCLIPPreTrainedModel",
"ChineseCLIPTextModel",
"ChineseCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
a :Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
a :Optional[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""layers""", """blocks"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
a :Any = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] = list(s_dict.keys() )
for key in keys:
SCREAMING_SNAKE_CASE__ : int = key
for k, v in WHISPER_MAPPING.items():
if k in key:
SCREAMING_SNAKE_CASE__ : Dict = new_key.replace(__lowerCAmelCase , __lowerCAmelCase )
print(F'''{key} -> {new_key}''' )
SCREAMING_SNAKE_CASE__ : Dict = s_dict.pop(__lowerCAmelCase )
return s_dict
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = emb.weight.shape
SCREAMING_SNAKE_CASE__ : str = nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = emb.weight.data
return lin_layer
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bytes:
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = url.split("""/""" )[-2]
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ) and not os.path.isfile(__lowerCAmelCase ):
raise RuntimeError(F'''{download_target} exists and is not a regular file''' )
if os.path.isfile(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = open(__lowerCAmelCase , """rb""" ).read()
if hashlib.shaaaa(__lowerCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file''' )
with urllib.request.urlopen(__lowerCAmelCase ) as source, open(__lowerCAmelCase , """wb""" ) as output:
with tqdm(
total=int(source.info().get("""Content-Length""" ) ) , ncols=80 , unit="""iB""" , unit_scale=__lowerCAmelCase , unit_divisor=1024 ) as loop:
while True:
SCREAMING_SNAKE_CASE__ : str = source.read(8192 )
if not buffer:
break
output.write(__lowerCAmelCase )
loop.update(len(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : Any = open(__lowerCAmelCase , """rb""" ).read()
if hashlib.shaaaa(__lowerCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
"""Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.""" )
return model_bytes
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
if ".pt" not in checkpoint_path:
SCREAMING_SNAKE_CASE__ : Optional[Any] = _download(_MODELS[checkpoint_path] )
else:
SCREAMING_SNAKE_CASE__ : Dict = torch.load(__lowerCAmelCase , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : Any = original_checkpoint["""dims"""]
SCREAMING_SNAKE_CASE__ : Optional[int] = original_checkpoint["""model_state_dict"""]
SCREAMING_SNAKE_CASE__ : Tuple = state_dict["""decoder.token_embedding.weight"""]
remove_ignore_keys_(__lowerCAmelCase )
rename_keys(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : List[str] = state_dict["""decoder.layers.0.fc1.weight"""].shape[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = WhisperConfig(
vocab_size=dimensions["""n_vocab"""] , encoder_ffn_dim=__lowerCAmelCase , decoder_ffn_dim=__lowerCAmelCase , num_mel_bins=dimensions["""n_mels"""] , d_model=dimensions["""n_audio_state"""] , max_target_positions=dimensions["""n_text_ctx"""] , encoder_layers=dimensions["""n_audio_layer"""] , encoder_attention_heads=dimensions["""n_audio_head"""] , decoder_layers=dimensions["""n_text_layer"""] , decoder_attention_heads=dimensions["""n_text_state"""] , max_source_positions=dimensions["""n_audio_ctx"""] , )
SCREAMING_SNAKE_CASE__ : List[Any] = WhisperForConditionalGeneration(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0 and not set(__lowerCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"""Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"""
F''' but all the following weights are missing {missing}''' )
if tie_embeds:
SCREAMING_SNAKE_CASE__ : Dict = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
SCREAMING_SNAKE_CASE__ : int = proj_out_weights
model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
a :Tuple = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
a :int = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a :int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
a :str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a :int = logging.get_logger(__name__)
a :Union[str, Any] = {
"facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
"facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
"facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
"facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
"facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
"facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
"facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
"facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
"facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = """xmod"""
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1E-1_2 , _a=1 , _a=0 , _a=2 , _a="absolute" , _a=True , _a=None , _a=False , _a=2 , _a=False , _a=True , _a=True , _a=("en_XX",) , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : str = intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE__ : str = layer_norm_eps
SCREAMING_SNAKE_CASE__ : str = position_embedding_type
SCREAMING_SNAKE_CASE__ : List[Any] = use_cache
SCREAMING_SNAKE_CASE__ : int = classifier_dropout
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pre_norm
SCREAMING_SNAKE_CASE__ : Union[str, Any] = adapter_reduction_factor
SCREAMING_SNAKE_CASE__ : str = adapter_layer_norm
SCREAMING_SNAKE_CASE__ : Any = adapter_reuse_layer_norm
SCREAMING_SNAKE_CASE__ : Any = ln_before_adapter
SCREAMING_SNAKE_CASE__ : List[Any] = list(_a )
SCREAMING_SNAKE_CASE__ : Any = default_language
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ : int = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE__ : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> list[float]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = coefficient_matrix.shape
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
raise ValueError(__lowerCAmelCase )
if colsa != 1:
SCREAMING_SNAKE_CASE__ : int = F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
raise ValueError(__lowerCAmelCase )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE__ : Any = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
)
raise ValueError(__lowerCAmelCase )
if len(__lowerCAmelCase ) != rowsa:
SCREAMING_SNAKE_CASE__ : Optional[int] = (
"""Number of initial values must be equal to number of rows in coefficient """
F'''matrix but received {len(__lowerCAmelCase )} and {rowsa}'''
)
raise ValueError(__lowerCAmelCase )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
SCREAMING_SNAKE_CASE__ : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = table.shape
strictly_diagonally_dominant(__lowerCAmelCase )
# Iterates the whole matrix for given number of times
for _ in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
for row in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
for col in range(__lowerCAmelCase ):
if col == row:
SCREAMING_SNAKE_CASE__ : Dict = table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE__ : List[Any] = (temp + val) / denom
new_val.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = new_val
return [float(__lowerCAmelCase ) for i in new_val]
def _lowercase ( __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = table.shape
SCREAMING_SNAKE_CASE__ : List[Any] = True
for i in range(0 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _lowercase ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" , 1024 )
print("""Key files generation successful.""" )
def _lowercase ( __lowerCAmelCase ) -> tuple[tuple[int, int], tuple[int, int]]:
print("""Generating prime p...""" )
SCREAMING_SNAKE_CASE__ : Any = rabinMiller.generate_large_prime(__lowerCAmelCase )
print("""Generating prime q...""" )
SCREAMING_SNAKE_CASE__ : List[Any] = rabinMiller.generate_large_prime(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = p * q
print("""Generating e that is relatively prime to (p - 1) * (q - 1)...""" )
while True:
SCREAMING_SNAKE_CASE__ : List[Any] = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(__lowerCAmelCase , (p - 1) * (q - 1) ) == 1:
break
print("""Calculating d that is mod inverse of e...""" )
SCREAMING_SNAKE_CASE__ : int = cryptoMath.find_mod_inverse(__lowerCAmelCase , (p - 1) * (q - 1) )
SCREAMING_SNAKE_CASE__ : str = (n, e)
SCREAMING_SNAKE_CASE__ : Tuple = (n, d)
return (public_key, private_key)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> None:
if os.path.exists(F'''{name}_pubkey.txt''' ) or os.path.exists(F'''{name}_privkey.txt''' ):
print("""\nWARNING:""" )
print(
F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
"""Use a different name or delete these files and re-run this program.""" )
sys.exit()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = generate_key(__lowerCAmelCase )
print(F'''\nWriting public key to file {name}_pubkey.txt...''' )
with open(F'''{name}_pubkey.txt''' , """w""" ) as out_file:
out_file.write(F'''{key_size},{public_key[0]},{public_key[1]}''' )
print(F'''Writing private key to file {name}_privkey.txt...''' )
with open(F'''{name}_privkey.txt''' , """w""" ) as out_file:
out_file.write(F'''{key_size},{private_key[0]},{private_key[1]}''' )
if __name__ == "__main__":
main()
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[Any] = 0
for i in range(1 , 1001 ):
total += i**i
return str(__lowerCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a :Optional[int] = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) -> None:
"""simple docstring"""
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , _a , )
super().__init__(*_a , **_a )
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = CodeGenTokenizer
_SCREAMING_SNAKE_CASE :Union[str, Any] = CodeGenTokenizerFast
_SCREAMING_SNAKE_CASE :Tuple = True
_SCREAMING_SNAKE_CASE :Optional[int] = {"""add_prefix_space""": True}
_SCREAMING_SNAKE_CASE :int = False
def _a ( self ) -> Dict:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
"""<|endoftext|>""",
]
SCREAMING_SNAKE_CASE__ : Any = dict(zip(_a , range(len(_a ) ) ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
SCREAMING_SNAKE_CASE__ : Any = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_a ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_a ) )
def _a ( self , **_a ) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Union[str, Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
return input_text, output_text
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ : Tuple = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.tokenize(_a , add_prefix_space=_a )
self.assertListEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : Tuple = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : Optional[int] = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer(add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : str = """lower newer"""
# Testing tokenization
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.tokenize(_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : int = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
# Testing conversion to ids without special tokens
SCREAMING_SNAKE_CASE__ : int = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
# Testing conversion to ids with special tokens
SCREAMING_SNAKE_CASE__ : Tuple = self.get_rust_tokenizer(add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
# Testing the unknown token
SCREAMING_SNAKE_CASE__ : List[str] = tokens + [rust_tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : Tuple = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a ) , _a )
def _a ( self , *_a , **_a ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self , _a=15 ) -> Union[str, Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : Any = self.rust_tokenizer_class.from_pretrained(_a , **_a )
# Simple input
SCREAMING_SNAKE_CASE__ : int = """This is a simple input"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = ("""This is a simple input""", """This is a pair""")
SCREAMING_SNAKE_CASE__ : int = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
# Pair input
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="""<pad>""" )
# Simple input
SCREAMING_SNAKE_CASE__ : Dict = """This is a simple input"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""This is a simple input looooooooong""", """This is a simple input"""]
SCREAMING_SNAKE_CASE__ : List[str] = ("""This is a simple input""", """This is a pair""")
SCREAMING_SNAKE_CASE__ : List[str] = [
("""This is a simple input loooooong""", """This is a simple input"""),
("""This is a simple pair loooooong""", """This is a simple pair"""),
]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.pad_token_id
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(_a , padding="""max_length""" , max_length=30 , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(_a , padding=_a , truncate=_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer(*_a , padding="""max_length""" , max_length=60 , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , padding=_a , truncate=_a , return_tensors="""np""" )
# s
# test single string max_length padding
self.assertEqual(out_s["""input_ids"""].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s["""input_ids"""] )
self.assertTrue(0 in out_s["""attention_mask"""] )
# s2
# test automatic padding
self.assertEqual(out_sa["""input_ids"""].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] )
self.assertFalse(0 in out_sa["""attention_mask"""][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] )
self.assertTrue(0 in out_sa["""attention_mask"""][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["""input_ids"""].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p["""input_ids"""] )
self.assertTrue(0 in out_p["""attention_mask"""] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["""input_ids"""].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] )
self.assertFalse(0 in out_pa["""attention_mask"""][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] )
self.assertTrue(0 in out_pa["""attention_mask"""][1] )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = """$$$"""
SCREAMING_SNAKE_CASE__ : Dict = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=_a , add_bos_token=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """This is a simple input"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""This is a simple input 1""", """This is a simple input 2"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.bos_token_id
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(_a )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a )
self.assertEqual(out_s.input_ids[0] , _a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.decode(out_s.input_ids )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono""" )
SCREAMING_SNAKE_CASE__ : Tuple = """\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"""
SCREAMING_SNAKE_CASE__ : Tuple = """\nif len_a > len_b: result = a\nelse: result = b"""
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode(_a )
SCREAMING_SNAKE_CASE__ : Dict = ["""^#""", re.escape("""<|endoftext|>""" ), """^'''""", """^\"\"\"""", """\n\n\n"""]
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(_a , truncate_before_pattern=_a )
self.assertEqual(_a , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
pass
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while y: # --> when y=0 then loop will terminate and return x as final GCD.
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = y, x % y
return abs(__lowerCAmelCase )
def _lowercase ( ) -> List[str]:
try:
SCREAMING_SNAKE_CASE__ : Optional[Any] = input("""Enter two integers separated by comma (,): """ ).split(""",""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(nums[0] )
SCREAMING_SNAKE_CASE__ : Dict = int(nums[1] )
print(
F'''greatest_common_divisor({num_a}, {num_a}) = '''
F'''{greatest_common_divisor(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowerCAmelCase , __lowerCAmelCase )}''' )
except (IndexError, UnboundLocalError, ValueError):
print("""Wrong input""" )
if __name__ == "__main__":
main()
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str = s.rsplit(__lowerCAmelCase , __lowerCAmelCase )
return new.join(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Dict:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def _lowercase ( __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Dict = {}
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""group_1""", """group_2""", """group_3""", """group_4"""]
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
SCREAMING_SNAKE_CASE__ : Optional[int] = key.replace(F'''{group_key}.''' , F'''{group_key}.group.''' )
if "res_path" in key:
SCREAMING_SNAKE_CASE__ : Tuple = key.replace("""res_path.""" , """res_path.path.""" )
if key.endswith(""".w""" ):
SCREAMING_SNAKE_CASE__ : Any = rreplace(__lowerCAmelCase , """.w""" , """.weight""" , 1 )
if key.endswith(""".b""" ):
SCREAMING_SNAKE_CASE__ : List[Any] = rreplace(__lowerCAmelCase , """.b""" , """.bias""" , 1 )
SCREAMING_SNAKE_CASE__ : Any = value.float()
return upgrade
@torch.no_grad()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=True ) -> List[str]:
from dall_e import Encoder
SCREAMING_SNAKE_CASE__ : int = Encoder()
if os.path.exists(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.load(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : int = torch.hub.load_state_dict_from_url(__lowerCAmelCase )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[str] = ckpt.state_dict()
encoder.load_state_dict(__lowerCAmelCase )
if config_path is not None:
SCREAMING_SNAKE_CASE__ : str = FlavaImageCodebookConfig.from_pretrained(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = FlavaImageCodebookConfig()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlavaImageCodebook(__lowerCAmelCase ).eval()
SCREAMING_SNAKE_CASE__ : Any = encoder.state_dict()
SCREAMING_SNAKE_CASE__ : int = upgrade_state_dict(__lowerCAmelCase )
hf_model.load_state_dict(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = hf_model.state_dict()
SCREAMING_SNAKE_CASE__ : int = count_parameters(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = count_parameters(__lowerCAmelCase )
assert torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(__lowerCAmelCase )
else:
return hf_state_dict
if __name__ == "__main__":
a :Any = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
a :List[Any] = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
a :Any = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
a :List[str] = subprocess.check_output(f'git diff --name-only {fork_point_sha}'.split()).decode("utf-8").split()
a :Optional[Any] = "|".join(sys.argv[1:])
a :List[Any] = re.compile(rf'^({joined_dirs}).*?\.py$')
a :int = [x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : int = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE__ : int = SqlDatasetReader(
"""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_sql_dataset(__lowerCAmelCase , __lowerCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Tuple = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE__ : List[str] = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_sql_dataset(__lowerCAmelCase , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Any:
with contextlib.closing(sqlitea.connect(__lowerCAmelCase ) ) as con:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = con.cursor()
cur.execute("""SELECT * FROM dataset""" )
for row in cur:
yield row
@require_sqlalchemy
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Tuple = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """tmp.sql""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase ).read()
SqlDatasetWriter(__lowerCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=1 ).write()
SCREAMING_SNAKE_CASE__ : Tuple = iter_sql_file(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = iter_sql_file(__lowerCAmelCase )
for rowa, rowa in zip(__lowerCAmelCase , __lowerCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Any = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , """tmp.sql""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase ).read()
SqlDatasetWriter(__lowerCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=2 ).write()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = iter_sql_file(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = iter_sql_file(__lowerCAmelCase )
for rowa, rowa in zip(__lowerCAmelCase , __lowerCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , """tmp.sql""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase ).read()
with pytest.raises(__lowerCAmelCase ):
SqlDatasetWriter(__lowerCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=0 ).write()
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
print(F'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(__lowerCAmelCase ):
print(F'''{i}\t\t{d}''' )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
for j in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[float]:
SCREAMING_SNAKE_CASE__ : int = [float("""inf""" )] * vertex_count
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
for _ in range(vertex_count - 1 ):
for j in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
SCREAMING_SNAKE_CASE__ : int = distance[u] + w
SCREAMING_SNAKE_CASE__ : List[str] = check_negative_cycle(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
a :Dict = int(input("Enter number of vertices: ").strip())
a :Union[str, Any] = int(input("Enter number of edges: ").strip())
a :list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
a ,a ,a :Union[str, Any] = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
a :List[str] = {"src": src, "dst": dest, "weight": weight}
a :str = int(input("\nEnter shortest path source:").strip())
a :Optional[Any] = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from math import factorial
a :dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def _lowercase ( __lowerCAmelCase ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__lowerCAmelCase ) )
def _lowercase ( __lowerCAmelCase = 60 , __lowerCAmelCase = 100_0000 ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
SCREAMING_SNAKE_CASE__ : str = 0
# the cached sizes of the previous chains
SCREAMING_SNAKE_CASE__ : dict[int, int] = {}
for start_chain_element in range(1 , __lowerCAmelCase ):
# The temporary set will contain the elements of the chain
SCREAMING_SNAKE_CASE__ : Optional[Any] = set()
SCREAMING_SNAKE_CASE__ : List[str] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
SCREAMING_SNAKE_CASE__ : str = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__lowerCAmelCase )
chain_set_length += 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = digit_factorial_sum(__lowerCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
SCREAMING_SNAKE_CASE__ : str = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'{solution()}')
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
a :Tuple = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.13.2",
"requests-mock": "requests-mock==1.10.0",
"importlib_metadata": "importlib_metadata",
"invisible-watermark": "invisible-watermark",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2",
"jaxlib": "jaxlib>=0.1.65",
"Jinja2": "Jinja2",
"k-diffusion": "k-diffusion>=0.0.12",
"torchsde": "torchsde",
"note_seq": "note_seq",
"librosa": "librosa",
"numpy": "numpy",
"omegaconf": "omegaconf",
"parameterized": "parameterized",
"protobuf": "protobuf>=3.20.3,<4",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"ruff": "ruff>=0.0.241",
"safetensors": "safetensors",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"scipy": "scipy",
"onnx": "onnx",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.25.1",
"urllib3": "urllib3<=2.0.0",
}
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
a :Union[str, Any] = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
print("Torch version:", None)
try:
import deepspeed
print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
print("DeepSpeed version:", None)
try:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
print("TensorFlow version:", None)
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """open-llama"""
def __init__( self , _a=100_000 , _a=4_096 , _a=11_008 , _a=32 , _a=32 , _a="silu" , _a=2_048 , _a=0.02 , _a=1E-6 , _a=True , _a=0 , _a=1 , _a=2 , _a=False , _a=True , _a=0.1 , _a=0.1 , _a=True , _a=True , _a=None , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = hidden_act
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = rms_norm_eps
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : Union[str, Any] = kwargs.pop(
"""use_memorry_efficient_attention""" , _a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = attention_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = use_stable_embedding
SCREAMING_SNAKE_CASE__ : Union[str, Any] = shared_input_output_embedding
SCREAMING_SNAKE_CASE__ : Dict = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , tie_word_embeddings=_a , **_a , )
def _a ( self ) -> str:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _a ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f'''got {self.rope_scaling}''' )
SCREAMING_SNAKE_CASE__ : List[Any] = self.rope_scaling.get("""type""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rope_scaling.get("""factor""" , _a )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
if rope_scaling_factor is None or not isinstance(_a , _a ) or rope_scaling_factor <= 1.0:
raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
a :dict[tuple[int, int, int], int] = {}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
SCREAMING_SNAKE_CASE__ : Dict = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
SCREAMING_SNAKE_CASE__ : Optional[Any] = _calculate(days - 1 , __lowerCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
SCREAMING_SNAKE_CASE__ : int = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
SCREAMING_SNAKE_CASE__ : Any = _calculate(days - 1 , __lowerCAmelCase , 0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = state_late + state_absent + state_ontime
SCREAMING_SNAKE_CASE__ : Dict = prizestrings
return prizestrings
def _lowercase ( __lowerCAmelCase = 30 ) -> int:
return _calculate(__lowerCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dict(zip(_a , range(len(_a ) ) ) )
SCREAMING_SNAKE_CASE__ : int = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
SCREAMING_SNAKE_CASE__ : int = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_a ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : str = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Dict:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> str:
"""simple docstring"""
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : List[Any] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[int] = CLIPProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : int = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = CLIPProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : List[str] = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = CLIPProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Any = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = CLIPProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Tuple = """lower newer"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = CLIPProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : str = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : int = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[Any] = CLIPProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : str = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : int = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = CLIPProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Tuple = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : int = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
a :int = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
a :Tuple = pytest.mark.integration
a :List[Any] = {"comet"}
a :Optional[Any] = importlib.util.find_spec("fairseq") is not None
a :Dict = {"code_eval"}
a :Union[str, Any] = os.name == "nt"
a :Union[str, Any] = {"bertscore", "frugalscore", "perplexity"}
a :Dict = importlib.util.find_spec("transformers") is not None
def _lowercase ( __lowerCAmelCase ) -> List[str]:
@wraps(__lowerCAmelCase )
def wrapper(self , __lowerCAmelCase ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("""\"test requires Fairseq\"""" )
else:
test_case(self , __lowerCAmelCase )
return wrapper
def _lowercase ( __lowerCAmelCase ) -> List[str]:
@wraps(__lowerCAmelCase )
def wrapper(self , __lowerCAmelCase ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("""\"test requires transformers\"""" )
else:
test_case(self , __lowerCAmelCase )
return wrapper
def _lowercase ( __lowerCAmelCase ) -> Dict:
@wraps(__lowerCAmelCase )
def wrapper(self , __lowerCAmelCase ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("""\"test not supported on Windows\"""" )
else:
test_case(self , __lowerCAmelCase )
return wrapper
def _lowercase ( ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names())
@for_all_test_methods(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_)
@local
class __a (parameterized.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = {}
_SCREAMING_SNAKE_CASE :str = None
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""" )
def _a ( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = """[...]"""
SCREAMING_SNAKE_CASE__ : Dict = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , _a ) ).module_path )
SCREAMING_SNAKE_CASE__ : List[str] = datasets.load.import_main_class(metric_module.__name__ , dataset=_a )
# check parameters
SCREAMING_SNAKE_CASE__ : List[Any] = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(_a , metric_module.__name__ ):
with self.use_local_metrics():
try:
SCREAMING_SNAKE_CASE__ : List[str] = doctest.testmod(_a , verbose=_a , raise_on_error=_a )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def _a ( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = """[...]"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , _a ) ).module_path )
# run doctest
with self.use_local_metrics():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = doctest.testmod(_a , verbose=_a , raise_on_error=_a )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def _a ( self , _a , _a ) -> Dict:
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](_a ):
yield
else:
yield
@contextmanager
def _a ( self ) -> List[str]:
"""simple docstring"""
def load_local_metric(_a , *_a , **_a ):
return load_metric(os.path.join("""metrics""" , _a ) , *_a , **_a )
with patch("""datasets.load_metric""" ) as mock_load_metric:
SCREAMING_SNAKE_CASE__ : Any = load_local_metric
yield
@classmethod
def _a ( cls , _a ) -> Dict:
"""simple docstring"""
def wrapper(_a ):
SCREAMING_SNAKE_CASE__ : Tuple = contextmanager(_a )
SCREAMING_SNAKE_CASE__ : List[str] = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("""sv""" , """""" , """""" ) # handle pytest cli flags
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
assert len(input_dict["""input_ids"""] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
SCREAMING_SNAKE_CASE__ : Optional[Any] = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def _lowercase ( __lowerCAmelCase ) -> Tuple:
import torch
def bert_cos_score_idf(__lowerCAmelCase , __lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(__lowerCAmelCase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("""bert_score.scorer.get_model""" ), patch(
"""bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
SCREAMING_SNAKE_CASE__ : Optional[int] = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def _lowercase ( __lowerCAmelCase ) -> List[str]:
def load_from_checkpoint(__lowerCAmelCase ):
class __a :
'''simple docstring'''
def _a ( self , _a , *_a , **_a ) -> Dict:
"""simple docstring"""
assert len(_a ) == 2
SCREAMING_SNAKE_CASE__ : Any = [0.19, 0.92]
return scores, sum(_a ) / len(_a )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("""comet.download_model""" ) as mock_download_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = None
with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_from_checkpoint
yield
def _lowercase ( ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Dict = load_metric(os.path.join("""metrics""" , """seqeval""" ) )
SCREAMING_SNAKE_CASE__ : Tuple = """ERROR"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(__lowerCAmelCase , match=re.escape(__lowerCAmelCase ) ):
metric.compute(predictions=[] , references=[] , scheme=__lowerCAmelCase )
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
a :Union[str, Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
a :Union[str, Any] = {value: key for key, value in encode_dict.items()}
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def _lowercase ( __lowerCAmelCase ) -> str:
if set(__lowerCAmelCase ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
SCREAMING_SNAKE_CASE__ : Tuple = """"""
for word in coded.split():
while len(__lowerCAmelCase ) != 0:
decoded += decode_dict[word[:5]]
SCREAMING_SNAKE_CASE__ : str = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a :Optional[int] = ""
a :Dict = ""
a :Optional[int] = ""
a :Tuple = 1 # (0 is vertical, 1 is horizontal)
def _lowercase ( ) -> None:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = get_dataset(__lowerCAmelCase , __lowerCAmelCase )
print("""Processing...""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = update_image_and_anno(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
for index, image in enumerate(__lowerCAmelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE__ : Tuple = random_chars(32 )
SCREAMING_SNAKE_CASE__ : int = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE__ : int = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , __lowerCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(__lowerCAmelCase )} with {file_name}''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE__ : List[Any] = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(__lowerCAmelCase )
with open(F'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> tuple[list, list]:
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : int = []
for label_file in glob.glob(os.path.join(__lowerCAmelCase , """*.txt""" ) ):
SCREAMING_SNAKE_CASE__ : Any = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(__lowerCAmelCase ) as in_file:
SCREAMING_SNAKE_CASE__ : Tuple = in_file.readlines()
SCREAMING_SNAKE_CASE__ : str = os.path.join(__lowerCAmelCase , F'''{label_name}.jpg''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE__ : Any = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(__lowerCAmelCase )
labels.append(__lowerCAmelCase )
return img_paths, labels
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 ) -> tuple[list, list, list]:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
SCREAMING_SNAKE_CASE__ : Tuple = []
for idx in range(len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : int = img_list[idx]
path_list.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = anno_list[idx]
SCREAMING_SNAKE_CASE__ : Optional[int] = cva.imread(__lowerCAmelCase )
if flip_type == 1:
SCREAMING_SNAKE_CASE__ : Optional[int] = cva.flip(__lowerCAmelCase , __lowerCAmelCase )
for bbox in img_annos:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cva.flip(__lowerCAmelCase , __lowerCAmelCase )
for bbox in img_annos:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(__lowerCAmelCase )
new_imgs_list.append(__lowerCAmelCase )
return new_imgs_list, new_annos_lists, path_list
def _lowercase ( __lowerCAmelCase = 32 ) -> str:
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE__ : List[str] = ascii_lowercase + digits
return "".join(random.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) )
if __name__ == "__main__":
main()
print("DONE ✅")
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Generator[tuple[str, ...], None, None]:
SCREAMING_SNAKE_CASE__ : str = iter(__lowerCAmelCase )
while True:
SCREAMING_SNAKE_CASE__ : Optional[int] = tuple(itertools.islice(__lowerCAmelCase , __lowerCAmelCase ) )
if not chunk:
return
yield chunk
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Dict = """""".join([c.upper() for c in dirty if c in string.ascii_letters] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """"""
if len(__lowerCAmelCase ) < 2:
return dirty
for i in range(len(__lowerCAmelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(__lowerCAmelCase ) & 1:
clean += "X"
return clean
def _lowercase ( __lowerCAmelCase ) -> list[str]:
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """ABCDEFGHIKLMNOPQRSTUVWXYZ"""
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
SCREAMING_SNAKE_CASE__ : Tuple = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(__lowerCAmelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(__lowerCAmelCase )
return table
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Tuple = generate_table(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = prepare_input(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = """"""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(__lowerCAmelCase , 2 ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = divmod(table.index(__lowerCAmelCase ) , 5 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = divmod(table.index(__lowerCAmelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = generate_table(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = """"""
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(__lowerCAmelCase , 2 ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = divmod(table.index(__lowerCAmelCase ) , 5 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = divmod(table.index(__lowerCAmelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a :Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
a :int = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
a :List[str] = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __a (datasets.Metric):
'''simple docstring'''
def _a ( self ) -> MetricInfo:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , )
def _a ( self , _a , _a , _a = 1 , _a = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_a , hypotheses=_a , min_len=_a , max_len=_a )
}
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
a :str = "Create a default config file for Accelerate with only a few flags set."
def _lowercase ( __lowerCAmelCase="no" , __lowerCAmelCase = default_json_config_file , __lowerCAmelCase = False ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int = Path(__lowerCAmelCase )
path.parent.mkdir(parents=__lowerCAmelCase , exist_ok=__lowerCAmelCase )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
SCREAMING_SNAKE_CASE__ : Dict = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""compute_environment""": """LOCAL_MACHINE""",
"""mixed_precision""": mixed_precision,
}
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE__ : str = torch.cuda.device_count()
SCREAMING_SNAKE_CASE__ : str = num_gpus
SCREAMING_SNAKE_CASE__ : str = False
if num_gpus > 1:
SCREAMING_SNAKE_CASE__ : str = """MULTI_GPU"""
else:
SCREAMING_SNAKE_CASE__ : int = """NO"""
elif is_xpu_available() and use_xpu:
SCREAMING_SNAKE_CASE__ : Dict = torch.xpu.device_count()
SCREAMING_SNAKE_CASE__ : Optional[int] = num_xpus
SCREAMING_SNAKE_CASE__ : Optional[int] = False
if num_xpus > 1:
SCREAMING_SNAKE_CASE__ : List[Any] = """MULTI_XPU"""
else:
SCREAMING_SNAKE_CASE__ : Dict = """NO"""
elif is_npu_available():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.npu.device_count()
SCREAMING_SNAKE_CASE__ : Any = num_npus
SCREAMING_SNAKE_CASE__ : Optional[int] = False
if num_npus > 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """MULTI_NPU"""
else:
SCREAMING_SNAKE_CASE__ : int = """NO"""
else:
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = """NO"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ClusterConfig(**__lowerCAmelCase )
config.to_json_file(__lowerCAmelCase )
return path
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Any = parser.add_parser("""default""" , parents=__lowerCAmelCase , help=__lowerCAmelCase , formatter_class=__lowerCAmelCase )
parser.add_argument(
"""--config_file""" , default=__lowerCAmelCase , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , dest="""save_location""" , )
parser.add_argument(
"""--mixed_precision""" , choices=["""no""", """fp16""", """bf16"""] , type=__lowerCAmelCase , help="""Whether or not to use mixed precision training. """
"""Choose between FP16 and BF16 (bfloat16) training. """
"""BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" , default="""no""" , )
parser.set_defaults(func=__lowerCAmelCase )
return parser
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : str = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a :Tuple = logging.get_logger(__name__)
a :Union[str, Any] = {
"openai/imagegpt-small": "",
"openai/imagegpt-medium": "",
"openai/imagegpt-large": "",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = """imagegpt"""
_SCREAMING_SNAKE_CASE :Any = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _a=512 + 1 , _a=32 * 32 , _a=512 , _a=24 , _a=8 , _a=None , _a="quick_gelu" , _a=0.1 , _a=0.1 , _a=0.1 , _a=1E-5 , _a=0.02 , _a=True , _a=True , _a=False , _a=False , _a=False , **_a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = n_positions
SCREAMING_SNAKE_CASE__ : List[Any] = n_embd
SCREAMING_SNAKE_CASE__ : Union[str, Any] = n_layer
SCREAMING_SNAKE_CASE__ : List[str] = n_head
SCREAMING_SNAKE_CASE__ : Any = n_inner
SCREAMING_SNAKE_CASE__ : List[str] = activation_function
SCREAMING_SNAKE_CASE__ : str = resid_pdrop
SCREAMING_SNAKE_CASE__ : List[str] = embd_pdrop
SCREAMING_SNAKE_CASE__ : Optional[int] = attn_pdrop
SCREAMING_SNAKE_CASE__ : List[Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : Any = scale_attn_weights
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_cache
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE__ : Dict = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tie_word_embeddings
super().__init__(tie_word_embeddings=_a , **_a )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
] )
def _a ( self , _a , _a = 1 , _a = -1 , _a = False , _a = None , _a = 3 , _a = 32 , _a = 32 , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self._generate_dummy_images(_a , _a , _a , _a )
SCREAMING_SNAKE_CASE__ : Tuple = dict(preprocessor(images=_a , return_tensors=_a ) )
return inputs
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import os
import sys
import unittest
a :str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
a :int = os.path.join(git_repo_path, "src", "diffusers")
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = find_backend(""" if not is_torch_available():""" )
self.assertEqual(_a , """torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
SCREAMING_SNAKE_CASE__ : Union[str, Any] = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(_a , """torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
SCREAMING_SNAKE_CASE__ : Dict = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(_a , """torch_and_transformers_and_onnx""" )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""" , _a )
self.assertIn("""torch_and_transformers""" , _a )
self.assertIn("""flax_and_transformers""" , _a )
self.assertIn("""torch_and_transformers_and_onnx""" , _a )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""" , objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""" , objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""" , objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""" , objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""" , objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""" , objects["""torch_and_transformers_and_onnx"""] )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = create_dummy_object("""CONSTANT""" , """'torch'""" )
self.assertEqual(_a , """\nCONSTANT = None\n""" )
SCREAMING_SNAKE_CASE__ : str = create_dummy_object("""function""" , """'torch'""" )
self.assertEqual(
_a , """\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
SCREAMING_SNAKE_CASE__ : Any = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
SCREAMING_SNAKE_CASE__ : Dict = create_dummy_object("""FakeClass""" , """'torch'""" )
self.assertEqual(_a , _a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
SCREAMING_SNAKE_CASE__ : Any = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""] , _a )
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a :Dict = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": True,
"scale": 0.1,
"eta": 0.0,
"t_grad_cutoff": 2,
"device": "cpu",
}
if __name__ == "__main__":
a :Dict = "hopper-medium-v2"
a :Any = gym.make(env_name)
a :Optional[Any] = ValueGuidedRLPipeline.from_pretrained(
"bglick13/hopper-medium-v2-value-function-hor32",
env=env,
)
env.seed(0)
a :Optional[int] = env.reset()
a :Dict = 0
a :str = 0
a :Any = 1_000
a :Tuple = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
a :int = pipeline(obs, planning_horizon=32)
# execute action in environment
a ,a ,a ,a :Union[str, Any] = env.step(denorm_actions)
a :str = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
f' {total_score}'
)
# save observations for rendering
rollout.append(next_observation.copy())
a :Optional[int] = next_observation
except KeyboardInterrupt:
pass
print(f'Total reward: {total_reward}')
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
# load base model
SCREAMING_SNAKE_CASE__ : Union[str, Any] = StableDiffusionPipeline.from_pretrained(__lowerCAmelCase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
SCREAMING_SNAKE_CASE__ : Union[str, Any] = load_file(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
SCREAMING_SNAKE_CASE__ : List[Any] = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
SCREAMING_SNAKE_CASE__ : Dict = pipeline.text_encoder
else:
SCREAMING_SNAKE_CASE__ : List[Any] = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
SCREAMING_SNAKE_CASE__ : Any = pipeline.unet
# find the target layer
SCREAMING_SNAKE_CASE__ : Optional[Any] = layer_infos.pop(0 )
while len(__lowerCAmelCase ) > -1:
try:
SCREAMING_SNAKE_CASE__ : str = curr_layer.__getattr__(__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
SCREAMING_SNAKE_CASE__ : List[Any] = layer_infos.pop(0 )
elif len(__lowerCAmelCase ) == 0:
break
except Exception:
if len(__lowerCAmelCase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
SCREAMING_SNAKE_CASE__ : int = layer_infos.pop(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(__lowerCAmelCase )
else:
pair_keys.append(__lowerCAmelCase )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
SCREAMING_SNAKE_CASE__ : List[str] = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase , __lowerCAmelCase ).unsqueeze(2 ).unsqueeze(3 )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = state_dict[pair_keys[0]].to(torch.floataa )
SCREAMING_SNAKE_CASE__ : Any = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase , __lowerCAmelCase )
# update visited list
for item in pair_keys:
visited.append(__lowerCAmelCase )
return pipeline
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
a :int = parser.parse_args()
a :List[Any] = args.base_model_path
a :Dict = args.checkpoint_path
a :List[str] = args.dump_path
a :List[Any] = args.lora_prefix_unet
a :Optional[Any] = args.lora_prefix_text_encoder
a :Optional[Any] = args.alpha
a :Optional[int] = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
a :Tuple = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import sys
a :Dict = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def _lowercase ( __lowerCAmelCase = N ) -> int:
SCREAMING_SNAKE_CASE__ : int = -sys.maxsize - 1
for i in range(len(__lowerCAmelCase ) - 12 ):
SCREAMING_SNAKE_CASE__ : str = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
SCREAMING_SNAKE_CASE__ : Optional[int] = product
return largest_product
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str
_SCREAMING_SNAKE_CASE :int
def _lowercase ( __lowerCAmelCase ) -> list[str]:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__lowerCAmelCase ) )]
def _lowercase ( __lowerCAmelCase ) -> BWTTransformDict:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
SCREAMING_SNAKE_CASE__ : str = all_rotations(__lowerCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
SCREAMING_SNAKE_CASE__ : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__lowerCAmelCase ),
}
return response
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
SCREAMING_SNAKE_CASE__ : str = int(__lowerCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__lowerCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
SCREAMING_SNAKE_CASE__ : List[str] = [""""""] * len(__lowerCAmelCase )
for _ in range(len(__lowerCAmelCase ) ):
for i in range(len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : Any = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
a :Union[str, Any] = "Provide a string that I will generate its BWT transform: "
a :List[str] = input(entry_msg).strip()
a :List[str] = bwt_transform(s)
print(
f'Burrows Wheeler transform for string \'{s}\' results '
f'in \'{result["bwt_string"]}\''
)
a :Dict = reverse_bwt(result["bwt_string"], result["idx_original_string"])
print(
f'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
f'we get original string \'{original_string}\''
)
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
a :str = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
a :Dict = "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"
a :Union[str, Any] = "\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric(\"mse\")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {'mse': 0.6123724356957945}\n\n If you're using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mse': array([0.41666667, 1. ])}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __a (datasets.Metric):
'''simple docstring'''
def _a ( self ) -> Dict:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _a ( self ) -> Tuple:
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _a ( self , _a , _a , _a=None , _a="uniform_average" , _a=True ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = mean_squared_error(
_a , _a , sample_weight=_a , multioutput=_a , squared=_a )
return {"mse": mse}
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a :List[str] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = ReformerTokenizer
_SCREAMING_SNAKE_CASE :Union[str, Any] = ReformerTokenizerFast
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Union[str, Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
def _a ( self ) -> List[str]:
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ : List[Any] = ReformerTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = """<s>"""
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_a ) , 1_000 )
def _a ( self ) -> Any:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : int = """I was born in 92000, and this is falsé."""
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.tokenize(_a )
SCREAMING_SNAKE_CASE__ : Any = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(_a )
SCREAMING_SNAKE_CASE__ : Any = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def _a ( self , _a=15 ) -> Union[str, Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : Dict = self.rust_tokenizer_class.from_pretrained(_a , **_a )
# Simple input
SCREAMING_SNAKE_CASE__ : List[Any] = """This is a simple input"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
SCREAMING_SNAKE_CASE__ : int = ("""This is a simple input""", """This is a pair""")
SCREAMING_SNAKE_CASE__ : Dict = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
# Pair input
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
pass
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ReformerTokenizer(_a , keep_accents=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_a , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" )
@slow
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = """Hello World!"""
SCREAMING_SNAKE_CASE__ : List[str] = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
SCREAMING_SNAKE_CASE__ : int = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def _a ( self ) -> Tuple:
"""simple docstring"""
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
SCREAMING_SNAKE_CASE__ : Dict = list(self.big_tokenizer.get_vocab().keys() )[:10]
SCREAMING_SNAKE_CASE__ : List[Any] = """ """.join(_a )
SCREAMING_SNAKE_CASE__ : str = self.big_tokenizer.encode_plus(_a , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ : Tuple = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
SCREAMING_SNAKE_CASE__ : Any = encoded_sequence["""input_ids"""].shape
SCREAMING_SNAKE_CASE__ : Any = ReformerModel(_a )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
SCREAMING_SNAKE_CASE__ : Tuple = [
"""This is a very simple sentence.""",
"""The quick brown fox jumps over the lazy dog.""",
]
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="""google/reformer-crime-and-punishment""" , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""" , padding=_a , sequences=_a , )
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
import math
def _lowercase ( __lowerCAmelCase ) -> list[int]:
SCREAMING_SNAKE_CASE__ : Tuple = []
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(math.sqrt(__lowerCAmelCase ) ) # Size of every segment
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [True] * (end + 1)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
while start <= end:
if temp[start] is True:
in_prime.append(__lowerCAmelCase )
for i in range(start * start , end + 1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
start += 1
prime += in_prime
SCREAMING_SNAKE_CASE__ : Tuple = end + 1
SCREAMING_SNAKE_CASE__ : Any = min(2 * end , __lowerCAmelCase )
while low <= n:
SCREAMING_SNAKE_CASE__ : Optional[int] = [True] * (high - low + 1)
for each in in_prime:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = math.floor(low / each ) * each
if t < low:
t += each
for j in range(__lowerCAmelCase , high + 1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : str = False
for j in range(len(__lowerCAmelCase ) ):
if temp[j] is True:
prime.append(j + low )
SCREAMING_SNAKE_CASE__ : Optional[int] = high + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = min(high + end , __lowerCAmelCase )
return prime
print(sieve(10**6))
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
a :Union[str, Any] = datasets.utils.logging.get_logger(__name__)
class __a (folder_based_builder.FolderBasedBuilderConfig):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :bool = None
_SCREAMING_SNAKE_CASE :bool = None
class __a (folder_based_builder.FolderBasedBuilder):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :int = datasets.Audio()
_SCREAMING_SNAKE_CASE :int = """audio"""
_SCREAMING_SNAKE_CASE :Union[str, Any] = AudioFolderConfig
_SCREAMING_SNAKE_CASE :List[str] # definition at the bottom of the script
_SCREAMING_SNAKE_CASE :List[str] = AudioClassification(audio_column="""audio""" , label_column="""label""")
a :Union[str, Any] = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
a :Tuple = AUDIO_EXTENSIONS
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """A painting of a squirrel eating a burger"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.device_count()
SCREAMING_SNAKE_CASE__ : Any = num_samples * [prompt]
SCREAMING_SNAKE_CASE__ : Dict = sd_pipe.prepare_inputs(_a )
SCREAMING_SNAKE_CASE__ : Dict = replicate(_a )
SCREAMING_SNAKE_CASE__ : Tuple = shard(_a )
SCREAMING_SNAKE_CASE__ : int = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = jax.random.split(_a , jax.device_count() )
SCREAMING_SNAKE_CASE__ : int = sd_pipe(_a , _a , _a , num_inference_steps=25 , jit=_a )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
SCREAMING_SNAKE_CASE__ : int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
SCREAMING_SNAKE_CASE__ : int = images[0, 253:256, 253:256, -1]
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = """stabilityai/stable-diffusion-2"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(_a , subfolder="""scheduler""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = FlaxStableDiffusionPipeline.from_pretrained(
_a , scheduler=_a , revision="""bf16""" , dtype=jnp.bfloataa , )
SCREAMING_SNAKE_CASE__ : int = scheduler_params
SCREAMING_SNAKE_CASE__ : Tuple = """A painting of a squirrel eating a burger"""
SCREAMING_SNAKE_CASE__ : str = jax.device_count()
SCREAMING_SNAKE_CASE__ : int = num_samples * [prompt]
SCREAMING_SNAKE_CASE__ : Optional[int] = sd_pipe.prepare_inputs(_a )
SCREAMING_SNAKE_CASE__ : str = replicate(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = shard(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = jax.random.split(_a , jax.device_count() )
SCREAMING_SNAKE_CASE__ : Tuple = sd_pipe(_a , _a , _a , num_inference_steps=25 , jit=_a )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
SCREAMING_SNAKE_CASE__ : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
SCREAMING_SNAKE_CASE__ : int = images[0, 253:256, 253:256, -1]
SCREAMING_SNAKE_CASE__ : int = jnp.asarray(jax.device_get(image_slice.flatten() ) )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(f'''output_slice: {output_slice}''' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class __a :
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=128 , _a=32 , _a=16 , _a=2 , _a=0.02 , _a=3 , _a=4 , _a=None , ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : List[Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : str = use_input_mask
SCREAMING_SNAKE_CASE__ : Any = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Tuple = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_choices
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scope
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Optional[int] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : Any = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self ) -> Dict:
"""simple docstring"""
return NezhaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
def _a ( self ) -> str:
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Tuple = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = NezhaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : int = model(_a , attention_mask=_a , token_type_ids=_a )
SCREAMING_SNAKE_CASE__ : List[str] = model(_a , token_type_ids=_a )
SCREAMING_SNAKE_CASE__ : List[str] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = True
SCREAMING_SNAKE_CASE__ : List[str] = NezhaModel(_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(
_a , attention_mask=_a , token_type_ids=_a , encoder_hidden_states=_a , encoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : List[str] = model(
_a , attention_mask=_a , token_type_ids=_a , encoder_hidden_states=_a , )
SCREAMING_SNAKE_CASE__ : str = model(_a , attention_mask=_a , token_type_ids=_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = NezhaForMaskedLM(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Any = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = NezhaForNextSentencePrediction(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = NezhaForPreTraining(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , next_sentence_label=_a , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = NezhaForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(
_a , attention_mask=_a , token_type_ids=_a , start_positions=_a , end_positions=_a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.num_labels
SCREAMING_SNAKE_CASE__ : int = NezhaForSequenceClassification(_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.num_labels
SCREAMING_SNAKE_CASE__ : Dict = NezhaForTokenClassification(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _a ( self , _a , _a , _a , _a , _a , _a , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_choices
SCREAMING_SNAKE_CASE__ : str = NezhaForMultipleChoice(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : str = config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE :List[Any] = (
{
"""feature-extraction""": NezhaModel,
"""fill-mask""": NezhaForMaskedLM,
"""question-answering""": NezhaForQuestionAnswering,
"""text-classification""": NezhaForSequenceClassification,
"""token-classification""": NezhaForTokenClassification,
"""zero-shot""": NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Optional[Any] = True
def _a ( self , _a , _a , _a=False ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class in get_values(_a ):
SCREAMING_SNAKE_CASE__ : Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_a )
SCREAMING_SNAKE_CASE__ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = NezhaModelTester(self )
SCREAMING_SNAKE_CASE__ : int = ConfigTester(self , config_class=_a , hidden_size=37 )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
SCREAMING_SNAKE_CASE__ : Any = None
self.model_tester.create_and_check_model_as_decoder(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_a )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_a )
@slow
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = NezhaModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@slow
@require_torch_gpu
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_class(config=_a )
SCREAMING_SNAKE_CASE__ : Any = self._prepare_for_class(_a , _a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.jit.trace(
_a , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_a , os.path.join(_a , """bert.pt""" ) )
SCREAMING_SNAKE_CASE__ : Any = torch.jit.load(os.path.join(_a , """bert.pt""" ) , map_location=_a )
loaded(inputs_dict["""input_ids"""].to(_a ) , inputs_dict["""attention_mask"""].to(_a ) )
@require_torch
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Dict = model(_a , attention_mask=_a )[0]
SCREAMING_SNAKE_CASE__ : str = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : int = torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _a , atol=1E-4 ) )
@slow
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Dict = model(_a , attention_mask=_a )[0]
SCREAMING_SNAKE_CASE__ : List[str] = torch.Size((1, 6, 21_128) )
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _a , atol=1E-4 ) )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a :int = 16
a :List[str] = 32
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = 16 ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = AutoTokenizer.from_pretrained("""bert-base-cased""" )
SCREAMING_SNAKE_CASE__ : List[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(__lowerCAmelCase ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE__ : int = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE__ : Dict = datasets.map(
__lowerCAmelCase , batched=__lowerCAmelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE__ : str = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__lowerCAmelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE__ : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE__ : int = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE__ : Optional[int] = 8
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = None
return tokenizer.pad(
__lowerCAmelCase , padding="""longest""" , max_length=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a :Union[str, Any] = mocked_dataloaders # noqa: F811
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , __lowerCAmelCase ) == "1":
SCREAMING_SNAKE_CASE__ : str = 2
# New Code #
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(args.gradient_accumulation_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE__ : List[str] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__lowerCAmelCase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"""Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE__ : int = config["""lr"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(config["""num_epochs"""] )
SCREAMING_SNAKE_CASE__ : int = int(config["""seed"""] )
SCREAMING_SNAKE_CASE__ : List[str] = int(config["""batch_size"""] )
SCREAMING_SNAKE_CASE__ : Optional[int] = evaluate.load("""glue""" , """mrpc""" )
set_seed(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = get_dataloaders(__lowerCAmelCase , __lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE__ : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=__lowerCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE__ : List[str] = AdamW(params=model.parameters() , lr=__lowerCAmelCase )
# Instantiate scheduler
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(__lowerCAmelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = accelerator.prepare(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Now we train the model
for epoch in range(__lowerCAmelCase ):
model.train()
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = model(**__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = output.loss
accelerator.backward(__lowerCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any = model(**__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=__lowerCAmelCase , references=__lowerCAmelCase , )
SCREAMING_SNAKE_CASE__ : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , __lowerCAmelCase )
def _lowercase ( ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=__lowerCAmelCase , default=__lowerCAmelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=__lowerCAmelCase , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : List[str] = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> Any:
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
SCREAMING_SNAKE_CASE__ : int = nn.Parameter(__lowerCAmelCase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
SCREAMING_SNAKE_CASE__ : Tuple = nn.Parameter(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
# set torch weights for 1-to-1 comparison
SCREAMING_SNAKE_CASE__ : Optional[int] = np.asarray(weights[0] )
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(weights[1] )
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__lowerCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCAmelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowerCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCAmelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowerCAmelCase ).view(-1 , __lowerCAmelCase ).contiguous().transpose(0 , 1 ) , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
# set torch weights for 1-to-1 comparison
SCREAMING_SNAKE_CASE__ : List[Any] = np.asarray(weights[0] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.asarray(weights[1] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.asarray(weights[2] )
SCREAMING_SNAKE_CASE__ : List[Any] = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__lowerCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCAmelCase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__lowerCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCAmelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowerCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCAmelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowerCAmelCase ).view(-1 , __lowerCAmelCase ).contiguous().transpose(0 , 1 ) , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
# layernorm 1
SCREAMING_SNAKE_CASE__ : List[str] = weights[0][0][0]
SCREAMING_SNAKE_CASE__ : str = np.asarray(layer_norm_a[0] )
SCREAMING_SNAKE_CASE__ : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__lowerCAmelCase ) , torch.tensor(__lowerCAmelCase ) , )
# lsh weights + output
SCREAMING_SNAKE_CASE__ : Union[str, Any] = weights[0][1]
if len(__lowerCAmelCase ) < 4:
set_layer_weights_in_torch_lsh(__lowerCAmelCase , torch_block.attention , __lowerCAmelCase )
else:
set_layer_weights_in_torch_local(__lowerCAmelCase , torch_block.attention , __lowerCAmelCase )
# intermediate weighs
SCREAMING_SNAKE_CASE__ : int = weights[2][0][1][2]
# Chunked Feed Forward
if len(__lowerCAmelCase ) == 4:
SCREAMING_SNAKE_CASE__ : int = intermediate_weights[2]
# layernorm 2
SCREAMING_SNAKE_CASE__ : List[Any] = np.asarray(intermediate_weights[0][0] )
SCREAMING_SNAKE_CASE__ : List[Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__lowerCAmelCase ) , torch.tensor(__lowerCAmelCase ) , )
# intermediate dense
SCREAMING_SNAKE_CASE__ : Tuple = np.asarray(intermediate_weights[1][0] )
SCREAMING_SNAKE_CASE__ : int = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__lowerCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCAmelCase ) , )
# intermediate out
SCREAMING_SNAKE_CASE__ : str = np.asarray(intermediate_weights[4][0] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__lowerCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCAmelCase ) , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# reformer model
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch_model.reformer
# word embeds
SCREAMING_SNAKE_CASE__ : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__lowerCAmelCase ) , )
if isinstance(weights[3] , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[Any] = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
SCREAMING_SNAKE_CASE__ : int = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
SCREAMING_SNAKE_CASE__ : Tuple = nn.Parameter(torch.tensor(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : str = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__lowerCAmelCase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# output layer norm
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.asarray(weights[7][0] )
SCREAMING_SNAKE_CASE__ : Tuple = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__lowerCAmelCase ) , torch.tensor(__lowerCAmelCase ) , )
# output embeddings
SCREAMING_SNAKE_CASE__ : Any = np.asarray(weights[9][0] )
SCREAMING_SNAKE_CASE__ : Dict = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__lowerCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCAmelCase ) , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# Initialise PyTorch model
SCREAMING_SNAKE_CASE__ : Optional[int] = ReformerConfig.from_json_file(__lowerCAmelCase )
print(F'''Building PyTorch model from configuration: {config}''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = ReformerModelWithLMHead(__lowerCAmelCase )
with open(__lowerCAmelCase , """rb""" ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] = pickle.load(__lowerCAmelCase )["""weights"""]
set_model_weights_in_torch(__lowerCAmelCase , __lowerCAmelCase , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __lowerCAmelCase )
if __name__ == "__main__":
a :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :Optional[int] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import argparse
import os
import re
a :Union[str, Any] = "src/diffusers"
# Pattern that looks at the indentation in a line.
a :int = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
a :Tuple = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a :Union[str, Any] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
a :Any = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a :Any = re.compile(r"\[([^\]]+)\]")
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = _re_indent.search(__lowerCAmelCase )
return "" if search is None else search.groups()[0]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase="" , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> str:
SCREAMING_SNAKE_CASE__ : int = 0
SCREAMING_SNAKE_CASE__ : Optional[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(__lowerCAmelCase ):
index += 1
SCREAMING_SNAKE_CASE__ : Tuple = ["""\n""".join(lines[:index] )]
else:
SCREAMING_SNAKE_CASE__ : Dict = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [lines[index]]
index += 1
while index < len(__lowerCAmelCase ) and (end_prompt is None or not lines[index].startswith(__lowerCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__lowerCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(__lowerCAmelCase ) )
if index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [lines[index + 1]]
index += 1
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
else:
blocks.append("""\n""".join(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : Any = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__lowerCAmelCase ) > 0:
blocks.append("""\n""".join(__lowerCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__lowerCAmelCase ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def _lowercase ( __lowerCAmelCase ) -> List[str]:
def _inner(__lowerCAmelCase ):
return key(__lowerCAmelCase ).lower().replace("""_""" , """""" )
return _inner
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None ) -> List[str]:
# If no key is provided, we use a noop.
def noop(__lowerCAmelCase ):
return x
if key is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = noop
# Constants are all uppercase, they go first.
SCREAMING_SNAKE_CASE__ : Optional[int] = [obj for obj in objects if key(__lowerCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
SCREAMING_SNAKE_CASE__ : Any = [obj for obj in objects if key(__lowerCAmelCase )[0].isupper() and not key(__lowerCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
SCREAMING_SNAKE_CASE__ : List[str] = [obj for obj in objects if not key(__lowerCAmelCase )[0].isupper()]
SCREAMING_SNAKE_CASE__ : Dict = ignore_underscore(__lowerCAmelCase )
return sorted(__lowerCAmelCase , key=__lowerCAmelCase ) + sorted(__lowerCAmelCase , key=__lowerCAmelCase ) + sorted(__lowerCAmelCase , key=__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
# This inner function sort imports between [ ].
def _replace(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = match.groups()[0]
if "," not in imports:
return F'''[{imports}]'''
SCREAMING_SNAKE_CASE__ : Optional[int] = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
SCREAMING_SNAKE_CASE__ : Any = keys[:-1]
return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase )] ) + "]"
SCREAMING_SNAKE_CASE__ : Optional[Any] = import_statement.split("""\n""" )
if len(__lowerCAmelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
SCREAMING_SNAKE_CASE__ : List[str] = 2 if lines[1].strip() == """[""" else 1
SCREAMING_SNAKE_CASE__ : int = [(i, _re_strip_line.search(__lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
SCREAMING_SNAKE_CASE__ : Any = sort_objects(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] )
SCREAMING_SNAKE_CASE__ : Tuple = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__lowerCAmelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
SCREAMING_SNAKE_CASE__ : int = _re_bracket_content.sub(_replace , lines[1] )
else:
SCREAMING_SNAKE_CASE__ : List[str] = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
SCREAMING_SNAKE_CASE__ : int = keys[:-1]
SCREAMING_SNAKE_CASE__ : Any = get_indent(lines[1] ) + """, """.join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase )] )
return "\n".join(__lowerCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
SCREAMING_SNAKE_CASE__ : List[Any] = _re_bracket_content.sub(_replace , __lowerCAmelCase )
return import_statement
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=True ) -> int:
with open(__lowerCAmelCase , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
SCREAMING_SNAKE_CASE__ : Optional[Any] = split_code_in_indented_blocks(
__lowerCAmelCase , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__lowerCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
SCREAMING_SNAKE_CASE__ : Dict = main_blocks[block_idx]
SCREAMING_SNAKE_CASE__ : Tuple = block.split("""\n""" )
# Get to the start of the imports.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
while line_idx < len(__lowerCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
SCREAMING_SNAKE_CASE__ : List[str] = len(__lowerCAmelCase )
else:
line_idx += 1
if line_idx >= len(__lowerCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
SCREAMING_SNAKE_CASE__ : str = """\n""".join(block_lines[line_idx:-1] )
SCREAMING_SNAKE_CASE__ : int = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
SCREAMING_SNAKE_CASE__ : Optional[Any] = split_code_in_indented_blocks(__lowerCAmelCase , indent_level=__lowerCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
SCREAMING_SNAKE_CASE__ : Any = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
SCREAMING_SNAKE_CASE__ : Optional[Any] = [(pattern.search(__lowerCAmelCase ).groups()[0] if pattern.search(__lowerCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
SCREAMING_SNAKE_CASE__ : Any = [(i, key) for i, key in enumerate(__lowerCAmelCase ) if key is not None]
SCREAMING_SNAKE_CASE__ : Dict = [x[0] for x in sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Dict = []
for i in range(len(__lowerCAmelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
SCREAMING_SNAKE_CASE__ : Dict = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__lowerCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
SCREAMING_SNAKE_CASE__ : Optional[int] = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__lowerCAmelCase ):
if check_only:
return True
else:
print(F'''Overwriting {file}.''' )
with open(__lowerCAmelCase , """w""" ) as f:
f.write("""\n""".join(__lowerCAmelCase ) )
def _lowercase ( __lowerCAmelCase=True ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[Any] = []
for root, _, files in os.walk(__lowerCAmelCase ):
if "__init__.py" in files:
SCREAMING_SNAKE_CASE__ : List[Any] = sort_imports(os.path.join(__lowerCAmelCase , """__init__.py""" ) , check_only=__lowerCAmelCase )
if result:
SCREAMING_SNAKE_CASE__ : str = [os.path.join(__lowerCAmelCase , """__init__.py""" )]
if len(__lowerCAmelCase ) > 0:
raise ValueError(F'''Would overwrite {len(__lowerCAmelCase )} files, run `make style`.''' )
if __name__ == "__main__":
a :Any = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
a :Dict = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
a :Optional[int] = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> float:
if not nums:
raise ValueError("""List is empty""" )
return sum(__lowerCAmelCase ) / len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if not numbers:
return 0
if not isinstance(__lowerCAmelCase , (list, tuple) ) or not all(
isinstance(__lowerCAmelCase , __lowerCAmelCase ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
SCREAMING_SNAKE_CASE__ : Any = numbers[0]
for i in range(1 , len(__lowerCAmelCase ) ):
# update the maximum and minimum subarray products
SCREAMING_SNAKE_CASE__ : List[Any] = numbers[i]
if number < 0:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = min_till_now, max_till_now
SCREAMING_SNAKE_CASE__ : Optional[int] = max(__lowerCAmelCase , max_till_now * number )
SCREAMING_SNAKE_CASE__ : str = min(__lowerCAmelCase , min_till_now * number )
# update the maximum product found till now
SCREAMING_SNAKE_CASE__ : List[Any] = max(__lowerCAmelCase , __lowerCAmelCase )
return max_prod
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
import cmath
import math
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> complex:
SCREAMING_SNAKE_CASE__ : Optional[int] = math.radians(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = math.radians(__lowerCAmelCase )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE__ : int = cmath.rect(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cmath.rect(__lowerCAmelCase , __lowerCAmelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a :Optional[Any] = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def _lowercase ( __lowerCAmelCase ) -> str:
config.addinivalue_line(
"""markers""" , """is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested""" )
config.addinivalue_line(
"""markers""" , """is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested""" )
config.addinivalue_line("""markers""" , """is_pipeline_test: mark test to run only when pipelines are tested""" )
config.addinivalue_line("""markers""" , """is_staging_test: mark test to run only in the staging environment""" )
config.addinivalue_line("""markers""" , """accelerate_tests: mark test that require accelerate""" )
config.addinivalue_line("""markers""" , """tool_tests: mark the tool tests that are run on their specific schedule""" )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Tuple:
from transformers.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE__ : Any = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(__lowerCAmelCase , id=__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
SCREAMING_SNAKE_CASE__ : Any = 0
# Doctest custom flag to ignore output.
a :Tuple = doctest.register_optionflag("IGNORE_RESULT")
a :Optional[int] = doctest.OutputChecker
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a , _a , _a ) -> Optional[Any]:
"""simple docstring"""
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , _a , _a , _a )
a :List[Any] = CustomOutputChecker
a :List[Any] = HfDoctestModule
a :Tuple = HfDocTestParser
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = sum(__lowerCAmelCase ) / len(__lowerCAmelCase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
a :Optional[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :int = """tapas"""
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=1_024 , _a=[3, 256, 256, 2, 256, 256, 10] , _a=0.02 , _a=1E-1_2 , _a=0 , _a=10.0 , _a=0 , _a=1.0 , _a=None , _a=1.0 , _a=False , _a=None , _a=1.0 , _a=1.0 , _a=False , _a=False , _a="ratio" , _a=None , _a=None , _a=64 , _a=32 , _a=False , _a=True , _a=False , _a=False , _a=True , _a=False , _a=None , _a=None , **_a , ) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=_a , **_a )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = hidden_size
SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : str = type_vocab_sizes
SCREAMING_SNAKE_CASE__ : List[str] = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
SCREAMING_SNAKE_CASE__ : str = positive_label_weight
SCREAMING_SNAKE_CASE__ : List[Any] = num_aggregation_labels
SCREAMING_SNAKE_CASE__ : Dict = aggregation_loss_weight
SCREAMING_SNAKE_CASE__ : List[str] = use_answer_as_supervision
SCREAMING_SNAKE_CASE__ : Dict = answer_loss_importance
SCREAMING_SNAKE_CASE__ : List[Any] = use_normalized_answer_loss
SCREAMING_SNAKE_CASE__ : List[Any] = huber_loss_delta
SCREAMING_SNAKE_CASE__ : Optional[Any] = temperature
SCREAMING_SNAKE_CASE__ : Tuple = aggregation_temperature
SCREAMING_SNAKE_CASE__ : Dict = use_gumbel_for_cells
SCREAMING_SNAKE_CASE__ : List[Any] = use_gumbel_for_aggregation
SCREAMING_SNAKE_CASE__ : Optional[int] = average_approximation_function
SCREAMING_SNAKE_CASE__ : Dict = cell_selection_preference
SCREAMING_SNAKE_CASE__ : Optional[int] = answer_loss_cutoff
SCREAMING_SNAKE_CASE__ : str = max_num_rows
SCREAMING_SNAKE_CASE__ : List[Any] = max_num_columns
SCREAMING_SNAKE_CASE__ : Optional[int] = average_logits_per_cell
SCREAMING_SNAKE_CASE__ : Dict = select_one_column
SCREAMING_SNAKE_CASE__ : Optional[int] = allow_empty_column_selection
SCREAMING_SNAKE_CASE__ : Union[str, Any] = init_cell_selection_weights_to_zero
SCREAMING_SNAKE_CASE__ : Any = reset_position_index_per_cell
SCREAMING_SNAKE_CASE__ : int = disable_per_token_loss
# Aggregation hyperparameters
SCREAMING_SNAKE_CASE__ : Union[str, Any] = aggregation_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels , _a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(_a ): v for k, v in aggregation_labels.items()}
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = ["""image_processor""", """tokenizer"""]
_SCREAMING_SNAKE_CASE :List[str] = """AutoImageProcessor"""
_SCREAMING_SNAKE_CASE :Dict = """AutoTokenizer"""
def __init__( self , _a , _a ) -> str:
"""simple docstring"""
super().__init__(_a , _a )
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processor
def __call__( self , _a=None , _a=None , _a=None , **_a ) -> Tuple:
"""simple docstring"""
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer(_a , return_tensors=_a , **_a )
if images is not None:
SCREAMING_SNAKE_CASE__ : Tuple = self.image_processor(_a , return_tensors=_a , **_a )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_a ) , tensor_type=_a )
def _a ( self , *_a , **_a ) -> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*_a , **_a )
def _a ( self , *_a , **_a ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*_a , **_a )
@property
def _a ( self ) -> Any:
"""simple docstring"""
return ["input_ids", "attention_mask", "pixel_values"]
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=3 , _a=224 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = size if size is not None else {"""height""": 18, """width""": 18}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : Optional[int] = min_resolution
SCREAMING_SNAKE_CASE__ : Any = max_resolution
SCREAMING_SNAKE_CASE__ : Tuple = do_resize
SCREAMING_SNAKE_CASE__ : int = size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_normalize
SCREAMING_SNAKE_CASE__ : Any = image_mean
SCREAMING_SNAKE_CASE__ : str = image_std
def _a ( self ) -> Optional[int]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = ViTImageProcessor if is_vision_available() else None
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = EfficientFormerImageProcessorTester(self )
@property
def _a ( self ) -> List[str]:
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , """image_mean""" ) )
self.assertTrue(hasattr(_a , """image_std""" ) )
self.assertTrue(hasattr(_a , """do_normalize""" ) )
self.assertTrue(hasattr(_a , """do_resize""" ) )
self.assertTrue(hasattr(_a , """size""" ) )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_proc_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : int = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ : int = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_proc_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
a :int = open # noqa: we just need to have a builtin inside this module to test it properly
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a :Dict = 0
a :Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
a :Optional[int] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
a :int = tuple[int, int]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = pos_x
SCREAMING_SNAKE_CASE__ : int = pos_y
SCREAMING_SNAKE_CASE__ : Any = (pos_y, pos_x)
SCREAMING_SNAKE_CASE__ : List[Any] = goal_x
SCREAMING_SNAKE_CASE__ : int = goal_y
SCREAMING_SNAKE_CASE__ : str = g_cost
SCREAMING_SNAKE_CASE__ : Optional[int] = parent
SCREAMING_SNAKE_CASE__ : Dict = self.calculate_heuristic()
SCREAMING_SNAKE_CASE__ : Optional[int] = self.g_cost + self.h_cost
def _a ( self ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.pos_x - self.goal_x
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(_a ) + abs(_a )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self , _a ) -> bool:
"""simple docstring"""
return self.f_cost < other.f_cost
class __a :
'''simple docstring'''
def __init__( self , _a , _a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , _a )
SCREAMING_SNAKE_CASE__ : str = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99_999 , _a )
SCREAMING_SNAKE_CASE__ : List[str] = [self.start]
SCREAMING_SNAKE_CASE__ : list[Node] = []
SCREAMING_SNAKE_CASE__ : Any = False
def _a ( self ) -> list[TPosition]:
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(_a )
self.closed_nodes.append(_a )
SCREAMING_SNAKE_CASE__ : Dict = self.get_successors(_a )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(_a )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE__ : List[Any] = self.open_nodes.pop(self.open_nodes.index(_a ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(_a )
else:
self.open_nodes.append(_a )
return [self.start.pos]
def _a ( self , _a ) -> list[Node]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = []
for action in delta:
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent.pos_x + action[1]
SCREAMING_SNAKE_CASE__ : str = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
_a , _a , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , _a , ) )
return successors
def _a ( self , _a ) -> list[TPosition]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = node
SCREAMING_SNAKE_CASE__ : Tuple = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
SCREAMING_SNAKE_CASE__ : List[Any] = current_node.parent
path.reverse()
return path
class __a :
'''simple docstring'''
def __init__( self , _a , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = AStar(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = AStar(_a , _a )
SCREAMING_SNAKE_CASE__ : List[str] = False
def _a ( self ) -> list[TPosition]:
"""simple docstring"""
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
SCREAMING_SNAKE_CASE__ : Any = self.fwd_astar.open_nodes.pop(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
_a , _a )
self.fwd_astar.closed_nodes.append(_a )
self.bwd_astar.closed_nodes.append(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = current_bwd_node
SCREAMING_SNAKE_CASE__ : Tuple = current_fwd_node
SCREAMING_SNAKE_CASE__ : Tuple = {
self.fwd_astar: self.fwd_astar.get_successors(_a ),
self.bwd_astar: self.bwd_astar.get_successors(_a ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(_a )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE__ : List[str] = astar.open_nodes.pop(
astar.open_nodes.index(_a ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(_a )
else:
astar.open_nodes.append(_a )
return [self.fwd_astar.start.pos]
def _a ( self , _a , _a ) -> list[TPosition]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.fwd_astar.retrace_path(_a )
SCREAMING_SNAKE_CASE__ : str = self.bwd_astar.retrace_path(_a )
bwd_path.pop()
bwd_path.reverse()
SCREAMING_SNAKE_CASE__ : Optional[Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
a :Optional[Any] = (0, 0)
a :List[Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
a :str = time.time()
a :Any = AStar(init, goal)
a :Optional[Any] = a_star.search()
a :Union[str, Any] = time.time() - start_time
print(f'AStar execution time = {end_time:f} seconds')
a :Tuple = time.time()
a :Any = BidirectionalAStar(init, goal)
a :int = time.time() - bd_start_time
print(f'BidirectionalAStar execution time = {bd_end_time:f} seconds')
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _lowercase ( __lowerCAmelCase ) -> Any:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Tuple = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
SCREAMING_SNAKE_CASE__ : str = key.replace("""heads.cmd.mim_head.cls.predictions""" , """mmm_image_head""" )
SCREAMING_SNAKE_CASE__ : str = key.replace("""heads.cmd.mlm_head.cls.predictions""" , """mmm_text_head""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = key.replace("""heads.cmd.itm_head.cls""" , """itm_head""" )
SCREAMING_SNAKE_CASE__ : List[str] = key.replace("""heads.cmd.itm_head.pooler""" , """itm_head.pooler""" )
SCREAMING_SNAKE_CASE__ : int = key.replace("""heads.cmd.clip_head.logit_scale""" , """flava.logit_scale""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = key.replace("""heads.fairseq_mlm.cls.predictions""" , """mlm_head""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = key.replace("""heads.imagenet.mim_head.cls.predictions""" , """mim_head""" )
SCREAMING_SNAKE_CASE__ : Any = key.replace("""mm_text_projection""" , """flava.text_to_mm_projection""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = key.replace("""mm_image_projection""" , """flava.image_to_mm_projection""" )
SCREAMING_SNAKE_CASE__ : List[Any] = key.replace("""image_encoder.module""" , """flava.image_model""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = key.replace("""text_encoder.module""" , """flava.text_model""" )
SCREAMING_SNAKE_CASE__ : List[Any] = key.replace("""mm_encoder.module.encoder.cls_token""" , """flava.multimodal_model.cls_token""" )
SCREAMING_SNAKE_CASE__ : Tuple = key.replace("""mm_encoder.module""" , """flava.multimodal_model""" )
SCREAMING_SNAKE_CASE__ : Dict = key.replace("""text_projection""" , """flava.text_projection""" )
SCREAMING_SNAKE_CASE__ : List[Any] = key.replace("""image_projection""" , """flava.image_projection""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = value.float()
for key, value in codebook_state_dict.items():
SCREAMING_SNAKE_CASE__ : Dict = value
return upgrade
@torch.no_grad()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> List[Any]:
if config_path is not None:
SCREAMING_SNAKE_CASE__ : str = FlavaConfig.from_pretrained(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : str = FlavaConfig()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlavaForPreTraining(__lowerCAmelCase ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = convert_dalle_checkpoint(__lowerCAmelCase , __lowerCAmelCase , save_checkpoint=__lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[str] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : Tuple = upgrade_state_dict(__lowerCAmelCase , __lowerCAmelCase )
hf_model.load_state_dict(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = hf_model.state_dict()
SCREAMING_SNAKE_CASE__ : str = count_parameters(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = count_parameters(__lowerCAmelCase ) + count_parameters(__lowerCAmelCase )
assert torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 )
hf_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
a :str = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
a :Optional[Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = None ) -> list[list[str]]:
SCREAMING_SNAKE_CASE__ : List[str] = word_bank or []
# create a table
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : list[list[list[str]]] = []
for _ in range(__lowerCAmelCase ):
table.append([] )
# seed value
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__lowerCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__lowerCAmelCase )] == word:
SCREAMING_SNAKE_CASE__ : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__lowerCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__lowerCAmelCase )]:
combination.reverse()
return table[len(__lowerCAmelCase )]
if __name__ == "__main__":
print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
print(
all_construct(
"hexagonosaurus",
["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
)
)
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_)
class __a (UpperCamelCase_):
'''simple docstring'''
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
_SCREAMING_SNAKE_CASE :str = field(default="""question-answering-extractive""" , metadata={"""include_in_asdict_even_if_is_default""": True})
_SCREAMING_SNAKE_CASE :ClassVar[Features] = Features({"""question""": Value("""string"""), """context""": Value("""string""")})
_SCREAMING_SNAKE_CASE :ClassVar[Features] = Features(
{
"""answers""": Sequence(
{
"""text""": Value("""string"""),
"""answer_start""": Value("""int32"""),
})
})
_SCREAMING_SNAKE_CASE :str = "question"
_SCREAMING_SNAKE_CASE :str = "context"
_SCREAMING_SNAKE_CASE :str = "answers"
@property
def _a ( self ) -> Dict[str, str]:
"""simple docstring"""
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str = k_size // 2
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
SCREAMING_SNAKE_CASE__ : Any = 1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase ) + square(__lowerCAmelCase )) / (2 * square(__lowerCAmelCase )) )
return g
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = image.shape[0], image.shape[1]
# dst image height and width
SCREAMING_SNAKE_CASE__ : str = height - k_size + 1
SCREAMING_SNAKE_CASE__ : Optional[int] = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
SCREAMING_SNAKE_CASE__ : Any = zeros((dst_height * dst_width, k_size * k_size) )
SCREAMING_SNAKE_CASE__ : int = 0
for i, j in product(range(__lowerCAmelCase ) , range(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : List[Any] = ravel(image[i : i + k_size, j : j + k_size] )
SCREAMING_SNAKE_CASE__ : Dict = window
row += 1
# turn the kernel into shape(k*k, 1)
SCREAMING_SNAKE_CASE__ : str = gen_gaussian_kernel(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ravel(__lowerCAmelCase )
# reshape and get the dst image
SCREAMING_SNAKE_CASE__ : Any = dot(__lowerCAmelCase , __lowerCAmelCase ).reshape(__lowerCAmelCase , __lowerCAmelCase ).astype(__lowerCAmelCase )
return dst
if __name__ == "__main__":
# read original image
a :Tuple = imread(r"../image_data/lena.jpg")
# turn image in gray scale value
a :str = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
a :Tuple = gaussian_filter(gray, 3, sigma=1)
a :Dict = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussianaxa)
imshow("gaussian filter with 5x5 mask", gaussianaxa)
waitKey()
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = """pixel_values"""
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :str = TimmBackboneConfig
def __init__( self , _a , **_a ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , """timm""" )
super().__init__(_a )
SCREAMING_SNAKE_CASE__ : List[str] = config
if config.backbone is None:
raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""" )
if config.backbone not in timm.list_models():
raise ValueError(f'''backbone {config.backbone} is not supported by timm.''' )
if hasattr(_a , """out_features""" ) and config.out_features is not None:
raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""" )
SCREAMING_SNAKE_CASE__ : Any = getattr(_a , """use_pretrained_backbone""" , _a )
if pretrained is None:
raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""" )
# We just take the final layer by default. This matches the default for the transformers models.
SCREAMING_SNAKE_CASE__ : List[str] = config.out_indices if getattr(_a , """out_indices""" , _a ) is not None else (-1,)
SCREAMING_SNAKE_CASE__ : Tuple = timm.create_model(
config.backbone , pretrained=_a , features_only=config.features_only , in_chans=config.num_channels , out_indices=_a , **_a , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
SCREAMING_SNAKE_CASE__ : Dict = self._backbone.return_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {layer["""module"""]: str(_a ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(_a )
@classmethod
def _a ( cls , _a , *_a , **_a ) -> Any:
"""simple docstring"""
requires_backends(cls , ["""vision""", """timm"""] )
from ...models.timm_backbone import TimmBackboneConfig
SCREAMING_SNAKE_CASE__ : Dict = kwargs.pop("""config""" , TimmBackboneConfig() )
SCREAMING_SNAKE_CASE__ : Dict = kwargs.pop("""use_timm_backbone""" , _a )
if not use_timm:
raise ValueError("""use_timm_backbone must be True for timm backbones""" )
SCREAMING_SNAKE_CASE__ : List[Any] = kwargs.pop("""num_channels""" , config.num_channels )
SCREAMING_SNAKE_CASE__ : Dict = kwargs.pop("""features_only""" , config.features_only )
SCREAMING_SNAKE_CASE__ : List[Any] = kwargs.pop("""use_pretrained_backbone""" , config.use_pretrained_backbone )
SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs.pop("""out_indices""" , config.out_indices )
SCREAMING_SNAKE_CASE__ : List[str] = TimmBackboneConfig(
backbone=_a , num_channels=_a , features_only=_a , use_pretrained_backbone=_a , out_indices=_a , )
return super()._from_config(_a , **_a )
def _a ( self , _a ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self , _a , _a=None , _a=None , _a=None , **_a ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE__ : List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE__ : List[Any] = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("""Cannot output attentions for timm backbones at the moment""" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
SCREAMING_SNAKE_CASE__ : List[Any] = self._all_layers
SCREAMING_SNAKE_CASE__ : Dict = self._backbone(_a , **_a )
SCREAMING_SNAKE_CASE__ : Tuple = self._return_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] = tuple(hidden_states[i] for i in self.out_indices )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._backbone(_a , **_a )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = tuple(_a )
SCREAMING_SNAKE_CASE__ : Dict = tuple(_a ) if hidden_states is not None else None
if not return_dict:
SCREAMING_SNAKE_CASE__ : Any = (feature_maps,)
if output_hidden_states:
SCREAMING_SNAKE_CASE__ : Any = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=_a , hidden_states=_a , attentions=_a )
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Dict = logging.get_logger(__name__)
a :str = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """autoformer"""
_SCREAMING_SNAKE_CASE :Tuple = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__( self , _a = None , _a = None , _a = "student_t" , _a = "nll" , _a = 1 , _a = [1, 2, 3, 4, 5, 6, 7] , _a = True , _a = 0 , _a = 0 , _a = 0 , _a = 0 , _a = None , _a = None , _a = 64 , _a = 2 , _a = 2 , _a = 2 , _a = 2 , _a = 32 , _a = 32 , _a = "gelu" , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 100 , _a = 0.02 , _a = True , _a=True , _a = 10 , _a = 25 , _a = 3 , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = prediction_length
SCREAMING_SNAKE_CASE__ : List[Any] = context_length if context_length is not None else prediction_length
SCREAMING_SNAKE_CASE__ : List[str] = distribution_output
SCREAMING_SNAKE_CASE__ : str = loss
SCREAMING_SNAKE_CASE__ : str = input_size
SCREAMING_SNAKE_CASE__ : str = num_time_features
SCREAMING_SNAKE_CASE__ : Optional[Any] = lags_sequence
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scaling
SCREAMING_SNAKE_CASE__ : Optional[int] = num_dynamic_real_features
SCREAMING_SNAKE_CASE__ : int = num_static_real_features
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = cardinality
else:
SCREAMING_SNAKE_CASE__ : Dict = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embedding_dimension
else:
SCREAMING_SNAKE_CASE__ : Tuple = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
SCREAMING_SNAKE_CASE__ : List[str] = num_parallel_samples
# Transformer architecture configuration
SCREAMING_SNAKE_CASE__ : Optional[int] = input_size * len(self.lags_sequence ) + self._number_of_features
SCREAMING_SNAKE_CASE__ : List[Any] = d_model
SCREAMING_SNAKE_CASE__ : Optional[Any] = encoder_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : List[Any] = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = decoder_layers
SCREAMING_SNAKE_CASE__ : str = dropout
SCREAMING_SNAKE_CASE__ : List[str] = attention_dropout
SCREAMING_SNAKE_CASE__ : Optional[int] = activation_dropout
SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_layerdrop
SCREAMING_SNAKE_CASE__ : List[str] = decoder_layerdrop
SCREAMING_SNAKE_CASE__ : Optional[Any] = activation_function
SCREAMING_SNAKE_CASE__ : Dict = init_std
SCREAMING_SNAKE_CASE__ : List[str] = use_cache
# Autoformer
SCREAMING_SNAKE_CASE__ : Dict = label_length
SCREAMING_SNAKE_CASE__ : Tuple = moving_average
SCREAMING_SNAKE_CASE__ : Any = autocorrelation_factor
super().__init__(is_encoder_decoder=_a , **_a )
@property
def _a ( self ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = RobertaTokenizer
_SCREAMING_SNAKE_CASE :List[str] = RobertaTokenizerFast
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = {"""cls_token""": """<s>"""}
def _a ( self ) -> Tuple:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE__ : str = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
SCREAMING_SNAKE_CASE__ : Tuple = dict(zip(_a , range(len(_a ) ) ) )
SCREAMING_SNAKE_CASE__ : Dict = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_a ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_a ) )
def _a ( self , **_a ) -> Union[str, Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Union[str, Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Dict = """lower newer"""
return input_text, output_text
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize(_a ) # , add_prefix_space=True)
self.assertListEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : int = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : int = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("""Hello world!""" , add_special_tokens=_a ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("""Hello world! cécé herlolip 418""" , add_special_tokens=_a ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer_class.from_pretrained("""roberta-base""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.encode("""multi-sequence build""" , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.encode(
"""sequence builders""" , add_special_tokens=_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(
"""sequence builders""" , """multi-sequence build""" , add_special_tokens=_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(_a )
SCREAMING_SNAKE_CASE__ : int = tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = """Encode this sequence."""
SCREAMING_SNAKE_CASE__ : str = tokenizer.byte_encoder[""" """.encode("""utf-8""" )[0]]
# Testing encoder arguments
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(_a , _a )
tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(_a , _a )
# Testing spaces after special tokens
SCREAMING_SNAKE_CASE__ : Any = """<mask>"""
tokenizer.add_special_tokens(
{"""mask_token""": AddedToken(_a , lstrip=_a , rstrip=_a )} ) # mask token has a left space
SCREAMING_SNAKE_CASE__ : str = tokenizer.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = """Encode <mask> sequence"""
SCREAMING_SNAKE_CASE__ : str = """Encode <mask>sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.encode(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = encoded.index(_a )
SCREAMING_SNAKE_CASE__ : str = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.encode(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = encoded.index(_a )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self ) -> Dict:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : str = self.tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : str = """A, <mask> AllenNLP sentence."""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer_r.encode_plus(_a , add_special_tokens=_a , return_token_type_ids=_a )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer_p.encode_plus(_a , add_special_tokens=_a , return_token_type_ids=_a )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) , sum(tokens_p["""token_type_ids"""] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) , sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) , )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
SCREAMING_SNAKE_CASE__ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
_a , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
_a , ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
def _a ( self ) -> Tuple:
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
SCREAMING_SNAKE_CASE__ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : int = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
SCREAMING_SNAKE_CASE__ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] , _a )
self.assertEqual(post_processor_state["""add_prefix_space"""] , _a )
self.assertEqual(post_processor_state["""trim_offsets"""] , _a )
def _a ( self ) -> int:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : str = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
SCREAMING_SNAKE_CASE__ : List[Any] = f'''{text_of_1_token} {text_of_1_token}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ) + 1, len(_a ) + 1 + len(_a )) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : int = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ) + 1, len(_a ) + 1 + len(_a )) , )
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ), len(_a ) + 1 + len(_a )) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(_a ), len(_a ) + 1 + len(_a )) , )
SCREAMING_SNAKE_CASE__ : List[str] = f''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
SCREAMING_SNAKE_CASE__ : Any = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : str = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_a ) + 1, 1 + len(_a ) + 1 + len(_a )) , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_a ), 1 + len(_a ) + 1 + len(_a )) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
_a , use_fast=_a , add_prefix_space=_a , trim_offsets=_a )
SCREAMING_SNAKE_CASE__ : str = tokenizer_r(_a , return_offsets_mapping=_a , add_special_tokens=_a )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(_a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(_a ), 1 + len(_a ) + 1 + len(_a )) , )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
a :Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase_)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , **_a ) -> List[str]:
"""simple docstring"""
super().__init__(**_a )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , """vision""" )
self.check_model_type(_a )
def __call__( self , _a , _a = None , **_a , ) -> Any:
"""simple docstring"""
if "text_queries" in kwargs:
SCREAMING_SNAKE_CASE__ : Any = kwargs.pop("""text_queries""" )
if isinstance(_a , (str, Image.Image) ):
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""image""": image, """candidate_labels""": candidate_labels}
else:
SCREAMING_SNAKE_CASE__ : int = image
SCREAMING_SNAKE_CASE__ : List[str] = super().__call__(_a , **_a )
return results
def _a ( self , **_a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = {}
if "threshold" in kwargs:
SCREAMING_SNAKE_CASE__ : Optional[int] = kwargs["""threshold"""]
if "top_k" in kwargs:
SCREAMING_SNAKE_CASE__ : Dict = kwargs["""top_k"""]
return {}, {}, postprocess_params
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = load_image(inputs["""image"""] )
SCREAMING_SNAKE_CASE__ : List[str] = inputs["""candidate_labels"""]
if isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = candidate_labels.split(""",""" )
SCREAMING_SNAKE_CASE__ : str = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(_a ):
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer(_a , return_tensors=self.framework )
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(_a , return_tensors=self.framework )
yield {
"is_last": i == len(_a ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def _a ( self , _a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = model_inputs.pop("""target_size""" )
SCREAMING_SNAKE_CASE__ : str = model_inputs.pop("""candidate_label""" )
SCREAMING_SNAKE_CASE__ : Dict = model_inputs.pop("""is_last""" )
SCREAMING_SNAKE_CASE__ : Tuple = self.model(**_a )
SCREAMING_SNAKE_CASE__ : int = {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
return model_outputs
def _a ( self , _a , _a=0.1 , _a=None ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
for model_output in model_outputs:
SCREAMING_SNAKE_CASE__ : List[str] = model_output["""candidate_label"""]
SCREAMING_SNAKE_CASE__ : List[Any] = BaseModelOutput(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processor.post_process_object_detection(
outputs=_a , threshold=_a , target_sizes=model_output["""target_size"""] )[0]
for index in outputs["scores"].nonzero():
SCREAMING_SNAKE_CASE__ : List[str] = outputs["""scores"""][index].item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._get_bounding_box(outputs["""boxes"""][index][0] )
SCREAMING_SNAKE_CASE__ : Dict = {"""score""": score, """label""": label, """box""": box}
results.append(_a )
SCREAMING_SNAKE_CASE__ : List[str] = sorted(_a , key=lambda _a : x["score"] , reverse=_a )
if top_k:
SCREAMING_SNAKE_CASE__ : List[str] = results[:top_k]
return results
def _a ( self , _a ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = box.int().tolist()
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Dict = multiprocessing.Manager()
SCREAMING_SNAKE_CASE__ : str = manager.list()
SCREAMING_SNAKE_CASE__ : Any = multiprocessing.Process(target=__lowerCAmelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
SCREAMING_SNAKE_CASE__ : int = shutil.rmtree
SCREAMING_SNAKE_CASE__ : Any = os.rmdir
SCREAMING_SNAKE_CASE__ : Dict = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
with swallow_io():
with time_limit(__lowerCAmelCase ):
exec(__lowerCAmelCase , __lowerCAmelCase )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(F'''failed: {e}''' )
# Needed for cleaning up.
SCREAMING_SNAKE_CASE__ : List[str] = rmtree
SCREAMING_SNAKE_CASE__ : Optional[Any] = rmdir
SCREAMING_SNAKE_CASE__ : List[str] = chdir
@contextlib.contextmanager
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
def signal_handler(__lowerCAmelCase , __lowerCAmelCase ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL , __lowerCAmelCase )
signal.signal(signal.SIGALRM , __lowerCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def _lowercase ( ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Dict = WriteOnlyStringIO()
with contextlib.redirect_stdout(__lowerCAmelCase ):
with contextlib.redirect_stderr(__lowerCAmelCase ):
with redirect_stdin(__lowerCAmelCase ):
yield
@contextlib.contextmanager
def _lowercase ( ) -> int:
with tempfile.TemporaryDirectory() as dirname:
with chdir(__lowerCAmelCase ):
yield dirname
class __a (UpperCamelCase_):
'''simple docstring'''
pass
class __a (io.StringIO):
'''simple docstring'''
def _a ( self , *_a , **_a ) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _a ( self , *_a , **_a ) -> Optional[int]:
"""simple docstring"""
raise OSError
def _a ( self , *_a , **_a ) -> List[str]:
"""simple docstring"""
raise OSError
def _a ( self , *_a , **_a ) -> int:
"""simple docstring"""
return False
class __a (contextlib._RedirectStream): # type: ignore
'''simple docstring'''
_SCREAMING_SNAKE_CASE :int = """stdin"""
@contextlib.contextmanager
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
if root == ".":
yield
return
SCREAMING_SNAKE_CASE__ : int = os.getcwd()
os.chdir(__lowerCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase=None ) -> Any:
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : Dict = None
import os
SCREAMING_SNAKE_CASE__ : Dict = """1"""
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
import shutil
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : int = None
import subprocess
SCREAMING_SNAKE_CASE__ : int = None # type: ignore
SCREAMING_SNAKE_CASE__ : int = None
import sys
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = FlaxAutoencoderKL
@property
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = 4
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = (32, 32)
SCREAMING_SNAKE_CASE__ : Any = jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ : Any = jax.random.uniform(_a , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
SCREAMING_SNAKE_CASE__ : Tuple = self.dummy_input
return init_dict, inputs_dict
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = ShapEImgaImgPipeline
_SCREAMING_SNAKE_CASE :Tuple = ["""image"""]
_SCREAMING_SNAKE_CASE :Any = ["""image"""]
_SCREAMING_SNAKE_CASE :int = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
_SCREAMING_SNAKE_CASE :Tuple = False
@property
def _a ( self ) -> Dict:
"""simple docstring"""
return 32
@property
def _a ( self ) -> Tuple:
"""simple docstring"""
return 32
@property
def _a ( self ) -> Any:
"""simple docstring"""
return self.time_input_dim * 4
@property
def _a ( self ) -> Any:
"""simple docstring"""
return 8
@property
def _a ( self ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
SCREAMING_SNAKE_CASE__ : Optional[int] = CLIPVisionModel(_a )
return model
@property
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = CLIPImageProcessor(
crop_size=224 , do_center_crop=_a , do_normalize=_a , do_resize=_a , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def _a ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = PriorTransformer(**_a )
return model
@property
def _a ( self ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""param_shapes""": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"""d_latent""": self.time_input_dim,
"""d_hidden""": self.renderer_dim,
"""n_output""": 12,
"""background""": (
0.1,
0.1,
0.1,
),
}
SCREAMING_SNAKE_CASE__ : List[str] = ShapERenderer(**_a )
return model
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.dummy_prior
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_image_encoder
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_image_processor
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_renderer
SCREAMING_SNAKE_CASE__ : Optional[int] = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1_024 , prediction_type="""sample""" , use_karras_sigmas=_a , clip_sample=_a , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def _a ( self , _a , _a=0 ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = """cpu"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : int = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : List[str] = pipe(**self.get_dummy_inputs(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = output.images[0]
SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> Optional[int]:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch_device == """cpu"""
SCREAMING_SNAKE_CASE__ : Dict = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_a , relax_max_difference=_a , )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.pipeline_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = 2
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_inputs(_a )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE__ : Dict = batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe(**_a , num_images_per_prompt=_a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
SCREAMING_SNAKE_CASE__ : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
SCREAMING_SNAKE_CASE__ : Any = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Any = torch.Generator(device=_a ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = pipe(
_a , generator=_a , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_a , _a )
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE__ : Any = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
SCREAMING_SNAKE_CASE__ : str = dict(zip(_a , range(len(_a ) ) ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
SCREAMING_SNAKE_CASE__ : List[str] = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_a ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_a ) )
SCREAMING_SNAKE_CASE__ : int = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : Any = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Optional[Any] = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[Any] = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = processor(images=_a , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : int = tokenizer(_a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = """lower newer"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : int = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(images=_a , visual_prompt=_a )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """conditional_pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : int = CLIPSegProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __a :
'''simple docstring'''
def __init__( self , _a , _a=3 , _a=32 , _a=3 , _a=10 , _a=[10, 20, 30, 40] , _a=[1, 1, 2, 1] , _a=True , _a=True , _a="relu" , _a=3 , _a=None , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : List[Any] = image_size
SCREAMING_SNAKE_CASE__ : Tuple = num_channels
SCREAMING_SNAKE_CASE__ : Dict = embeddings_size
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Any = depths
SCREAMING_SNAKE_CASE__ : Union[str, Any] = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = scope
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : str = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> List[Any]:
"""simple docstring"""
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _a ( self , _a , _a , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = TFResNetModel(config=_a )
SCREAMING_SNAKE_CASE__ : int = model(_a )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.num_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = TFResNetForImageClassification(_a )
SCREAMING_SNAKE_CASE__ : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE :Optional[Any] = (
{"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Union[str, Any] = False
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :Dict = False
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = TFResNetModelTester(self )
SCREAMING_SNAKE_CASE__ : Dict = ConfigTester(self , config_class=_a , has_text_modality=_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_class(_a )
SCREAMING_SNAKE_CASE__ : Dict = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Any = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
def check_hidden_states_output(_a , _a , _a ):
SCREAMING_SNAKE_CASE__ : Dict = model_class(_a )
SCREAMING_SNAKE_CASE__ : Dict = model(**self._prepare_for_class(_a , _a ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.num_stages
self.assertEqual(len(_a ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
SCREAMING_SNAKE_CASE__ : Dict = layer_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
check_hidden_states_output(_a , _a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def _a ( self ) -> List[str]:
"""simple docstring"""
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFResNetModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def _lowercase ( ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
@cached_property
def _a ( self ) -> List[Any]:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_img()
SCREAMING_SNAKE_CASE__ : str = image_processor(images=_a , return_tensors="""tf""" )
# forward pass
SCREAMING_SNAKE_CASE__ : str = model(**_a )
# verify the logits
SCREAMING_SNAKE_CASE__ : List[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.constant([-11.1_069, -9.7_877, -8.3_777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _a , atol=1E-4 ) )
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
a :Union[str, Any] = logging.getLogger(__name__)
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] = git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE__ : int = {
"""repo_id""": str(SCREAMING_SNAKE_CASE_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(SCREAMING_SNAKE_CASE_ , """git_log.json""" ) , """w""" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , indent=4 )
def _lowercase ( __lowerCAmelCase ) -> Dict:
if params.n_gpu <= 0:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Any = -1
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : Tuple = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
SCREAMING_SNAKE_CASE__ : List[str] = int(os.environ["""WORLD_SIZE"""] )
SCREAMING_SNAKE_CASE__ : List[str] = int(os.environ["""N_GPU_NODE"""] )
SCREAMING_SNAKE_CASE__ : str = int(os.environ["""RANK"""] )
# number of nodes / node ID
SCREAMING_SNAKE_CASE__ : Dict = params.world_size // params.n_gpu_per_node
SCREAMING_SNAKE_CASE__ : Any = params.global_rank // params.n_gpu_per_node
SCREAMING_SNAKE_CASE__ : Optional[int] = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
SCREAMING_SNAKE_CASE__ : str = 1
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Tuple = 1
SCREAMING_SNAKE_CASE__ : List[str] = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
SCREAMING_SNAKE_CASE__ : Tuple = params.node_id == 0 and params.local_rank == 0
SCREAMING_SNAKE_CASE__ : List[Any] = params.n_nodes > 1
# summary
SCREAMING_SNAKE_CASE__ : Union[str, Any] = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 700 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 0 |
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