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"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase = "cpu" , _lowercase = "openai/clip-vit-large-patch14" ) -> None:
'''simple docstring'''
snake_case_ : List[str] = device
snake_case_ : Tuple = CLIPTokenizerFast.from_pretrained(__a )
snake_case_ : Dict = [0.4814_5466, 0.457_8275, 0.4082_1073]
snake_case_ : Dict = [0.2686_2954, 0.2613_0258, 0.2757_7711]
snake_case_ : Tuple = torchvision.transforms.Normalize(self.image_mean , self.image_std )
snake_case_ : List[Any] = torchvision.transforms.Resize(2_2_4 )
snake_case_ : Dict = torchvision.transforms.CenterCrop(2_2_4 )
def UpperCAmelCase__ ( self , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = self.resize(__a )
snake_case_ : str = self.center_crop(__a )
snake_case_ : int = self.normalize(__a )
return images
def __call__( self , _lowercase=None , _lowercase=None , **_lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.tokenizer(text=__a , **__a )
snake_case_ : Optional[int] = self.preprocess_img(__a )
snake_case_ : str = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase=1_0 , _lowercase=0.01 , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=False , _lowercase=True , _lowercase="image" , _lowercase=True , _lowercase=False , _lowercase=False , _lowercase=False , ) -> None:
'''simple docstring'''
super().__init__()
snake_case_ : Any = None
snake_case_ : Optional[int] = device if device else get_device()
if vqgan:
snake_case_ : List[Any] = vqgan
else:
snake_case_ : Union[str, Any] = load_vqgan(self.device , conf_path=__a , ckpt_path=__a )
self.vqgan.eval()
if clip:
snake_case_ : str = clip
else:
snake_case_ : Tuple = CLIPModel.from_pretrained("""openai/clip-vit-base-patch32""" )
self.clip.to(self.device )
snake_case_ : Any = ProcessorGradientFlow(device=self.device )
snake_case_ : List[Any] = iterations
snake_case_ : str = lr
snake_case_ : Optional[int] = log
snake_case_ : List[Any] = make_grid
snake_case_ : Optional[int] = return_val
snake_case_ : Optional[int] = quantize
snake_case_ : List[Any] = self.vqgan.decoder.z_shape
def UpperCAmelCase__ ( self , _lowercase=None , _lowercase=None , _lowercase=5 , _lowercase=True ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = []
if output_path is None:
snake_case_ : Optional[int] = """./animation.gif"""
if input_path is None:
snake_case_ : List[str] = self.save_path
snake_case_ : str = sorted(glob(input_path + """/*""" ) )
if not len(__a ):
raise ValueError(
"""No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
""" function?)""" )
if len(__a ) == 1:
print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)""" )
snake_case_ : List[Any] = total_duration / len(__a )
snake_case_ : int = [frame_duration] * len(__a )
if extend_frames:
snake_case_ : Tuple = 1.5
snake_case_ : str = 3
for file_name in paths:
if file_name.endswith(""".png""" ):
images.append(imageio.imread(__a ) )
imageio.mimsave(__a , __a , duration=__a )
print(f'gif saved to {output_path}' )
def UpperCAmelCase__ ( self , _lowercase=None , _lowercase=None ) -> Optional[int]:
'''simple docstring'''
if not (path or img):
raise ValueError("""Input either path or tensor""" )
if img is not None:
raise NotImplementedError
snake_case_ : Tuple = preprocess(Image.open(__a ) , target_image_size=2_5_6 ).to(self.device )
snake_case_ : Dict = preprocess_vqgan(__a )
snake_case_ : List[str] = self.vqgan.encode(__a )
return z
def UpperCAmelCase__ ( self , _lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.latent.detach().requires_grad_()
snake_case_ : int = base_latent + transform_vector
if self.quantize:
snake_case_ : Union[str, Any] = self.vqgan.quantize(__a )
else:
snake_case_ : Optional[Any] = trans_latent
return self.vqgan.decode(__a )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.clip_preprocessor(text=__a , images=__a , return_tensors="""pt""" , padding=__a )
snake_case_ : Optional[int] = self.clip(**__a )
snake_case_ : Optional[int] = clip_outputs.logits_per_image
if weights is not None:
snake_case_ : List[str] = similarity_logits * weights
return similarity_logits.sum()
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = self._get_clip_similarity(pos_prompts["""prompts"""] , __a , weights=(1 / pos_prompts["""weights"""]) )
if neg_prompts:
snake_case_ : Optional[Any] = self._get_clip_similarity(neg_prompts["""prompts"""] , __a , weights=neg_prompts["""weights"""] )
else:
snake_case_ : int = torch.tensor([1] , device=self.device )
snake_case_ : Dict = -torch.log(__a ) + torch.log(__a )
return loss
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> int:
'''simple docstring'''
snake_case_ : Tuple = torch.randn_like(self.latent , requires_grad=__a , device=self.device )
snake_case_ : int = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
snake_case_ : Optional[Any] = self._add_vector(__a )
snake_case_ : Optional[Any] = loop_post_process(__a )
snake_case_ : Dict = self._get_CLIP_loss(__a , __a , __a )
print("""CLIP loss""" , __a )
if self.log:
wandb.log({"""CLIP Loss""": clip_loss} )
clip_loss.backward(retain_graph=__a )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> List[Any]:
'''simple docstring'''
wandb.init(reinit=__a , project="""face-editor""" )
wandb.config.update({"""Positive Prompts""": positive_prompts} )
wandb.config.update({"""Negative Prompts""": negative_prompts} )
wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations} )
if image_path:
snake_case_ : Dict = Image.open(__a )
snake_case_ : Optional[Any] = image.resize((2_5_6, 2_5_6) )
wandb.log("""Original Image""" , wandb.Image(__a ) )
def UpperCAmelCase__ ( self , _lowercase ) -> Dict:
'''simple docstring'''
if not prompts:
return []
snake_case_ : Dict = []
snake_case_ : str = []
if isinstance(__a , __a ):
snake_case_ : int = [prompt.strip() for prompt in prompts.split("""|""" )]
for prompt in prompts:
if isinstance(__a , (tuple, list) ):
snake_case_ : str = prompt[0]
snake_case_ : Optional[Any] = float(prompt[1] )
elif ":" in prompt:
snake_case_ : str = prompt.split(""":""" )
snake_case_ : int = float(__a )
else:
snake_case_ : Union[str, Any] = prompt
snake_case_ : Optional[int] = 1.0
processed_prompts.append(__a )
weights.append(__a )
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a , device=self.device ),
}
def UpperCAmelCase__ ( self , _lowercase , _lowercase=None , _lowercase=None , _lowercase=True , _lowercase=False , _lowercase=True , _lowercase=True , _lowercase=None , ) -> Union[str, Any]:
'''simple docstring'''
if image_path:
snake_case_ : str = self._get_latent(__a )
else:
snake_case_ : List[str] = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(__a , __a , __a )
assert pos_prompts, "You must provide at least one positive prompt."
snake_case_ : int = self.process_prompts(__a )
snake_case_ : int = self.process_prompts(__a )
if save_final and save_path is None:
snake_case_ : str = os.path.join("""./outputs/""" , """_""".join(pos_prompts["""prompts"""] ) )
if not os.path.exists(__a ):
os.makedirs(__a )
else:
snake_case_ : Dict = save_path + """_""" + get_timestamp()
os.makedirs(__a )
snake_case_ : Tuple = save_path
snake_case_ : str = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print("""Original Image""" )
show_pil(custom_to_pil(__a ) )
snake_case_ : Optional[int] = loop_post_process(__a )
for iter, transformed_img in enumerate(self._optimize_CLIP(__a , __a , __a ) ):
if show_intermediate:
show_pil(__a )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , f'iter_{iter:03d}.png' ) )
if self.log:
wandb.log({"""Image""": wandb.Image(__a )} )
if show_final:
show_pil(__a )
if save_final:
transformed_img.save(os.path.join(self.save_path , f'iter_{iter:03d}_final.png' ) )
| 719 |
"""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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : List[str] = batch_size
snake_case_ : int = seq_length
snake_case_ : List[Any] = is_training
snake_case_ : Optional[int] = use_attention_mask
snake_case_ : Optional[Any] = use_token_type_ids
snake_case_ : Union[str, Any] = use_labels
snake_case_ : str = vocab_size
snake_case_ : List[str] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : List[str] = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : str = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : str = num_choices
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[int] = None
if self.use_token_type_ids:
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = 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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_lowercase )
snake_case_ : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case_ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ : Dict = model(_lowercase )[0]
snake_case_ : Optional[int] = 5_0_0_0_0
snake_case_ : Union[str, Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _lowercase )
snake_case_ : Dict = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCAmelCase ( __UpperCamelCase : Dataset , __UpperCamelCase : Dict[str, str] ):
'''simple docstring'''
snake_case_ : Optional[Any] = args.log_outputs
snake_case_ : Dict = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
snake_case_ : Optional[int] = load_metric("""wer""" )
snake_case_ : Dict = load_metric("""cer""" )
# compute metrics
snake_case_ : Optional[int] = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
snake_case_ : Any = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
snake_case_ : Any = F'WER: {wer_result}\nCER: {cer_result}'
print(_UpperCamelCase )
with open(F'{dataset_id}_eval_results.txt' , """w""" ) as f:
f.write(_UpperCamelCase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
snake_case_ : Tuple = F'log_{dataset_id}_predictions.txt'
snake_case_ : Dict = F'log_{dataset_id}_targets.txt'
with open(_UpperCamelCase , """w""" ) as p, open(_UpperCamelCase , """w""" ) as t:
# mapping function to write output
def write_to_file(__UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] ):
p.write(F'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(_UpperCamelCase , with_indices=_UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Tuple = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
snake_case_ : List[str] = re.sub(_UpperCamelCase , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
snake_case_ : Union[str, Any] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
snake_case_ : List[str] = """ """.join(text.split(_UpperCamelCase ) )
return text
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : str = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_UpperCamelCase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
snake_case_ : Tuple = AutoFeatureExtractor.from_pretrained(args.model_id )
snake_case_ : str = feature_extractor.sampling_rate
# resample audio
snake_case_ : Tuple = dataset.cast_column("""audio""" , Audio(sampling_rate=_UpperCamelCase ) )
# load eval pipeline
if args.device is None:
snake_case_ : str = 0 if torch.cuda.is_available() else -1
snake_case_ : Optional[int] = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__UpperCamelCase : List[str] ):
snake_case_ : str = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
snake_case_ : int = prediction["""text"""]
snake_case_ : Union[str, Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
snake_case_ : Optional[Any] = dataset.map(_UpperCamelCase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
main(args)
| 720 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : int = 1_0
snake_case_ : Any = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
snake_case_ : Tuple = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(__UpperCamelCase ) ),
} , features=__UpperCamelCase , )
return dataset
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return filename
# FILE_CONTENT + files
__lowerCAmelCase : List[Any] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
snake_case_ : Optional[Any] = FILE_CONTENT
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
import bza
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
snake_case_ : Any = bytes(__UpperCamelCase , """utf-8""" )
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
import gzip
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
snake_case_ : List[Any] = bytes(__UpperCamelCase , """utf-8""" )
with gzip.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
snake_case_ : Optional[Any] = bytes(__UpperCamelCase , """utf-8""" )
with lza.frame.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(__UpperCamelCase , """w""" ) as archive:
archive.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple ):
'''simple docstring'''
import tarfile
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
import lzma
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
snake_case_ : str = bytes(__UpperCamelCase , """utf-8""" )
with lzma.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
import zipfile
snake_case_ : Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
snake_case_ : Tuple = bytes(__UpperCamelCase , """utf-8""" )
with zstd.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
snake_case_ : List[str] = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
__lowerCAmelCase : List[str] = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__lowerCAmelCase : Tuple = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCAmelCase : int = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
__lowerCAmelCase : int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
__lowerCAmelCase : Any = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = datasets.Dataset.from_dict(__UpperCamelCase )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(__UpperCamelCase ) ) as con:
snake_case_ : Tuple = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : Optional[Any] = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : str = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : int ):
'''simple docstring'''
import bza
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(__UpperCamelCase , """rb""" ) as f:
snake_case_ : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(__UpperCamelCase , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
snake_case_ : Any = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(__UpperCamelCase , """wb""" ) as f:
snake_case_ : Optional[int] = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase )
snake_case_ : Optional[int] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCamelCase ) )] for k in DATA[0]} , schema=__UpperCamelCase )
writer.write_table(__UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : Any = {"""data""": DATA}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : List[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
import gzip
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] ):
'''simple docstring'''
import gzip
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : List[str] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : str = ["""0""", """1""", """2""", """3"""]
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : int = ["""0""", """1""", """2""", """3"""]
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : List[Any] = ["""0""", """1""", """2""", """3"""]
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
snake_case_ : int = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
snake_case_ : int = 1
if upper_limit > 0:
snake_case_ : Optional[int] = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(__UpperCamelCase ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
print('''\n*** Enter -1 at any time to quit ***''')
print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
try:
while True:
__lowerCAmelCase : Union[str, Any] = int(input().strip())
if N < 0:
print('''\n********* Goodbye!! ************''')
break
else:
print(F'''The Catalan numbers from 0 through {N} are:''')
print(catalan_numbers(N))
print('''Try another upper limit for the sequence: ''', end='''''')
except (NameError, ValueError):
print('''\n********* Invalid input, goodbye! ************\n''')
import doctest
doctest.testmod()
| 721 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ : List[str] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : Union[str, Any] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : int = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
if num < 0:
return False
snake_case_ : List[str] = num
snake_case_ : List[str] = 0
while num > 0:
snake_case_ : List[str] = rev_num * 1_0 + (num % 1_0)
num //= 1_0
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
_lowerCamelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_lowercase , **_lowercase ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> int:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> Any:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
_lowerCamelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_lowercase , **_lowercase ) -> str:
'''simple docstring'''
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> Dict:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
_lowerCamelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_lowercase , **_lowercase ) -> List[str]:
'''simple docstring'''
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> Any:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
_lowerCamelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_lowercase , **_lowercase ) -> List[str]:
'''simple docstring'''
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
_lowerCamelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_lowercase , **_lowercase ) -> int:
'''simple docstring'''
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> str:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> int:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
_lowerCamelCase = ['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_lowercase , **_lowercase ) -> Tuple:
'''simple docstring'''
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> int:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def UpperCAmelCase__ ( cls , *_lowercase , **_lowercase ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
| 701 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''nat'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=4 , _lowercase=3 , _lowercase=6_4 , _lowercase=[3, 4, 6, 5] , _lowercase=[2, 4, 8, 1_6] , _lowercase=7 , _lowercase=3.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=0.0 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : Any = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = embed_dim
snake_case_ : Tuple = depths
snake_case_ : int = len(_lowercase )
snake_case_ : Optional[int] = num_heads
snake_case_ : List[str] = kernel_size
snake_case_ : str = mlp_ratio
snake_case_ : str = qkv_bias
snake_case_ : str = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Tuple = drop_path_rate
snake_case_ : Dict = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Tuple = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Union[str, Any] = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Union[str, Any] = layer_scale_init_value
snake_case_ : Optional[Any] = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 21 | 0 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Any = ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')
snake_case_ : Dict = (
('''layer.''', '''layer_'''),
('''word_embeddings.weight''', '''word_embeddings'''),
('''position_embeddings.weight''', '''position_embeddings'''),
('''token_type_embeddings.weight''', '''token_type_embeddings'''),
('''.''', '''/'''),
('''LayerNorm/weight''', '''LayerNorm/gamma'''),
('''LayerNorm/bias''', '''LayerNorm/beta'''),
('''weight''', '''kernel'''),
)
if not os.path.isdir(lowerCAmelCase_ ):
os.makedirs(lowerCAmelCase_ )
snake_case_ : Tuple = model.state_dict()
def to_tf_var_name(__UpperCamelCase : List[Any] ):
for patt, repl in iter(lowerCAmelCase_ ):
snake_case_ : List[Any] = name.replace(lowerCAmelCase_ , lowerCAmelCase_ )
return F'bert/{name}'
def create_tf_var(__UpperCamelCase : Optional[Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] ):
snake_case_ : Optional[int] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Any = tf.get_variable(dtype=lowerCAmelCase_ , shape=tensor.shape , name=lowerCAmelCase_ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(lowerCAmelCase_ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Dict = to_tf_var_name(lowerCAmelCase_ )
snake_case_ : Tuple = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : Any = torch_tensor.T
snake_case_ : Dict = create_tf_var(tensor=lowerCAmelCase_ , name=lowerCAmelCase_ , session=lowerCAmelCase_ )
tf.keras.backend.set_value(lowerCAmelCase_ , lowerCAmelCase_ )
snake_case_ : Optional[Any] = session.run(lowerCAmelCase_ )
print(F'Successfully created {tf_name}: {np.allclose(lowerCAmelCase_ , lowerCAmelCase_ )}' )
snake_case_ : Optional[Any] = tf.train.Saver(tf.trainable_variables() )
saver.save(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , model_name.replace("""-""" , """_""" ) + """.ckpt""" ) )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int]=None ):
'''simple docstring'''
snake_case_ : Tuple = argparse.ArgumentParser()
parser.add_argument("""--model_name""" , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help="""model name e.g. bert-base-uncased""" )
parser.add_argument(
"""--cache_dir""" , type=lowerCAmelCase_ , default=lowerCAmelCase_ , required=lowerCAmelCase_ , help="""Directory containing pytorch model""" )
parser.add_argument("""--pytorch_model_path""" , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help="""/path/to/<pytorch-model-name>.bin""" )
parser.add_argument("""--tf_cache_dir""" , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help="""Directory in which to save tensorflow model""" )
snake_case_ : Any = parser.parse_args(lowerCAmelCase_ )
snake_case_ : Tuple = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 702 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__lowerCAmelCase : Optional[Any] = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[Any] = VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(_lowercase )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = 1_2
snake_case_ : Tuple = 1_2
snake_case_ : Tuple = {
"""attention_bias""": True,
"""cross_attention_dim""": 3_2,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 3_2,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
snake_case_ : Optional[Any] = TransformeraDModel(**_lowercase )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = """cpu"""
snake_case_ : List[str] = self.dummy_vqvae
snake_case_ : Any = self.dummy_text_encoder
snake_case_ : Tuple = self.dummy_tokenizer
snake_case_ : int = self.dummy_transformer
snake_case_ : int = VQDiffusionScheduler(self.num_embed )
snake_case_ : Dict = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase )
snake_case_ : Optional[Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : int = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : List[Any] = """teddy bear playing in the pool"""
snake_case_ : Dict = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : List[Any] = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Optional[int] = output.images
snake_case_ : List[Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Dict = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : List[Any] = image[0, -3:, -3:, -1]
snake_case_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : Dict = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
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 UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : int = """cpu"""
snake_case_ : List[Any] = self.dummy_vqvae
snake_case_ : Optional[int] = self.dummy_text_encoder
snake_case_ : List[Any] = self.dummy_tokenizer
snake_case_ : Union[str, Any] = self.dummy_transformer
snake_case_ : str = VQDiffusionScheduler(self.num_embed )
snake_case_ : List[Any] = LearnedClassifierFreeSamplingEmbeddings(
learnable=_lowercase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ : Union[str, Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : Any = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : Tuple = """teddy bear playing in the pool"""
snake_case_ : str = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Tuple = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Dict = output.images
snake_case_ : Union[str, Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Any = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case_ : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : int = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
snake_case_ : str = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
snake_case_ : Optional[Any] = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ : Any = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Optional[int] = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=_lowercase , output_type="""np""" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 21 | 0 |
"""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 MobileViTImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=7 , _lowercase=3 , _lowercase=1_8 , _lowercase=3_0 , _lowercase=4_0_0 , _lowercase=True , _lowercase=None , _lowercase=True , _lowercase=None , _lowercase=True , ) -> int:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"""shortest_edge""": 2_0}
snake_case_ : List[Any] = crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
snake_case_ : Union[str, Any] = parent
snake_case_ : Any = batch_size
snake_case_ : Tuple = num_channels
snake_case_ : Optional[int] = image_size
snake_case_ : int = min_resolution
snake_case_ : Union[str, Any] = max_resolution
snake_case_ : int = do_resize
snake_case_ : List[str] = size
snake_case_ : List[Any] = do_center_crop
snake_case_ : str = crop_size
snake_case_ : Dict = do_flip_channel_order
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class _lowerCAmelCase ( _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MobileViTImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = MobileViTImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , """do_resize""" ) )
self.assertTrue(hasattr(lowercase__ , """size""" ) )
self.assertTrue(hasattr(lowercase__ , """do_center_crop""" ) )
self.assertTrue(hasattr(lowercase__ , """center_crop""" ) )
self.assertTrue(hasattr(lowercase__ , """do_flip_channel_order""" ) )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 2_0} )
self.assertEqual(image_processor.crop_size , {"""height""": 1_8, """width""": 1_8} )
snake_case_ : int = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 4_2} )
self.assertEqual(image_processor.crop_size , {"""height""": 8_4, """width""": 8_4} )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
snake_case_ : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
snake_case_ : Union[str, Any] = image_processing(lowercase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ , numpify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , np.ndarray )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
snake_case_ : Dict = image_processing(lowercase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ , torchify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
snake_case_ : Any = image_processing(lowercase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 703 |
"""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 __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Any=0 ):
'''simple docstring'''
if name is None:
snake_case_ : Dict = None
else:
snake_case_ : Dict = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(5_0 - spaces ) + """s}"""
snake_case_ : Any = fmt.format(__UpperCamelCase )
# Print and recurse (if needed).
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if msg is not None:
print(__UpperCamelCase )
for k in val.keys():
recursive_print(__UpperCamelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCamelCase , torch.Tensor ):
print(__UpperCamelCase , """:""" , val.size() )
else:
print(__UpperCamelCase , """:""" , __UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Any = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case_ : List[str] = (num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case_ : Tuple = param.view(*__UpperCamelCase )
snake_case_ : Tuple = param.transpose(0 , 2 )
snake_case_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case_ : Optional[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case_ : str = param.view(*__UpperCamelCase )
snake_case_ : Dict = param.transpose(0 , 1 ).contiguous()
snake_case_ : int = param.view(*__UpperCamelCase )
return param
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Dict = {}
# old versions did not store training args
snake_case_ : List[str] = input_state_dict.get("""args""" , __UpperCamelCase )
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))
snake_case_ : Tuple = ds_args.padded_vocab_size
snake_case_ : Optional[int] = ds_args.max_position_embeddings
snake_case_ : Union[str, Any] = ds_args.hidden_size
snake_case_ : Union[str, Any] = ds_args.num_layers
snake_case_ : str = ds_args.num_attention_heads
snake_case_ : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case_ : Union[str, Any] = config.n_head
# The hidden_size per head.
snake_case_ : Optional[Any] = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case_ : Optional[Any] = input_state_dict["""checkpoint_version"""]
else:
snake_case_ : int = 0.0
# The model.
snake_case_ : List[str] = input_state_dict["""model"""]
# The language model.
snake_case_ : str = model["""language_model"""]
# The embeddings.
snake_case_ : Tuple = lm["""embedding"""]
# The word embeddings.
snake_case_ : List[str] = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
snake_case_ : Optional[int] = word_embeddings[: config.vocab_size, :]
snake_case_ : Optional[int] = word_embeddings
# The position embeddings.
snake_case_ : List[Any] = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
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.
snake_case_ : Union[str, Any] = pos_embeddings
# The transformer.
snake_case_ : Optional[Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
snake_case_ : Optional[Any] = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
snake_case_ : List[str] = {
"""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.
snake_case_ : int = layer_re.match(__UpperCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case_ : Tuple = int(m.group(1 ) )
# The name of the operation.
snake_case_ : Any = m.group(2 )
# Is it a weight or a bias?
snake_case_ : Union[str, Any] = m.group(3 )
# The name of the layer.
snake_case_ : str = F'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
snake_case_ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
snake_case_ : Optional[int] = 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.
snake_case_ : Optional[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCamelCase , __UpperCamelCase )
snake_case_ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case_ : str = torch.tensor(-1E4 , dtype=torch.floataa )
snake_case_ : List[Any] = masked_bias
snake_case_ : Optional[int] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case_ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case_ : Tuple = 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":
snake_case_ : Optional[Any] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Store. No change of shape.
snake_case_ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case_ : Any = megatron_to_transformers[op_name]
snake_case_ : str = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case_ : List[str] = megatron_to_transformers[op_name]
snake_case_ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case_ : Dict = transformer["""final_layernorm.weight"""]
snake_case_ : Dict = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case_ : Optional[int] = word_embeddings
# It should be done!
return output_state_dict
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__UpperCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__UpperCamelCase , help="""An optional config json file describing the pre-trained model.""" , )
snake_case_ : str = parser.parse_args()
# Extract the basename.
snake_case_ : Optional[Any] = 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:
snake_case_ : Optional[int] = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
snake_case_ : List[Any] = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
snake_case_ : Any = input_state_dict.get("""args""" , __UpperCamelCase )
# 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:
snake_case_ : Any = """gelu_fast"""
elif ds_args.openai_gelu:
snake_case_ : Tuple = """gelu_new"""
else:
snake_case_ : List[str] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
snake_case_ : Dict = """gelu_new"""
# Spell out all parameters in case the defaults change.
snake_case_ : List[str] = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=__UpperCamelCase , 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=__UpperCamelCase , summary_activation=__UpperCamelCase , summary_proj_to_labels=__UpperCamelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCamelCase , use_cache=__UpperCamelCase , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
snake_case_ : List[Any] = GPTaConfig.from_json_file(args.config_file )
snake_case_ : int = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
snake_case_ : Tuple = convert_megatron_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCamelCase , __UpperCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case_ : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case_ : Optional[Any] = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
snake_case_ : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
else:
snake_case_ : List[str] = """gpt2"""
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(__UpperCamelCase )
snake_case_ : List[str] = type(__UpperCamelCase ).__name__
snake_case_ : Optional[int] = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__UpperCamelCase )
# Save tokenizer based on args
print(F'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(__UpperCamelCase )
# Store the state_dict to file.
snake_case_ : List[Any] = os.path.join(__UpperCamelCase , """pytorch_model.bin""" )
print(F'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(__UpperCamelCase , __UpperCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 21 | 0 |
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : List[Any]=0 ):
'''simple docstring'''
return sorted(a_ , key=lambda __UpperCamelCase : x[column] )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any]=float("""inf""" ) ):
'''simple docstring'''
for i in range(points_counts - 1 ):
for j in range(i + 1 , a_ ):
snake_case_ : str = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
snake_case_ : List[str] = current_dis
return min_dis
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict=float("""inf""" ) ):
'''simple docstring'''
for i in range(min(6 , points_counts - 1 ) , a_ ):
for j in range(max(0 , i - 6 ) , a_ ):
snake_case_ : List[Any] = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
snake_case_ : Tuple = current_dis
return min_dis
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if points_counts <= 3:
return dis_between_closest_pair(a_ , a_ )
# recursion
snake_case_ : Any = points_counts // 2
snake_case_ : List[str] = closest_pair_of_points_sqr(
a_ , points_sorted_on_y[:mid] , a_ )
snake_case_ : Any = closest_pair_of_points_sqr(
a_ , points_sorted_on_y[mid:] , points_counts - mid )
snake_case_ : Tuple = min(a_ , a_ )
snake_case_ : Tuple = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(a_ )
snake_case_ : List[Any] = dis_between_closest_in_strip(
a_ , len(a_ ) , a_ )
return min(a_ , a_ )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = column_based_sort(a_ , column=0 )
snake_case_ : List[Any] = column_based_sort(a_ , column=1 )
return (
closest_pair_of_points_sqr(
a_ , a_ , a_ )
) ** 0.5
if __name__ == "__main__":
__lowerCAmelCase : List[Any] = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print('''Distance:''', closest_pair_of_points(points, len(points)))
| 704 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : List[Any] = tf.cast(math.pi , x.dtype )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Tuple = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -1_0 , 1_0 )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=-1 ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
__lowerCAmelCase : int = tf.keras.activations.gelu
__lowerCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
__lowerCAmelCase : List[Any] = _gelu
__lowerCAmelCase : Any = _gelu_new
__lowerCAmelCase : Dict = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 21 | 0 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] , __UpperCamelCase : int ):
'''simple docstring'''
assert isinstance(__UpperCamelCase , __UpperCamelCase )
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
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : str = tmp_path / """cache"""
snake_case_ : 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():
snake_case_ : int = ParquetDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase ).read()
_check_parquet_dataset(__UpperCamelCase , __UpperCamelCase )
@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 __lowerCAmelCase ( __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Any = tmp_path / """cache"""
snake_case_ : Optional[int] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
snake_case_ : Optional[Any] = features.copy() if features else default_expected_features
snake_case_ : Any = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ : Optional[Any] = ParquetDatasetReader(__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_parquet_dataset(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : int = tmp_path / """cache"""
snake_case_ : Any = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
snake_case_ : List[Any] = ParquetDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase , split=__UpperCamelCase ).read()
_check_parquet_dataset(__UpperCamelCase , __UpperCamelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int ):
'''simple docstring'''
if issubclass(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : List[Any] = parquet_path
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : Optional[int] = [parquet_path]
snake_case_ : Union[str, Any] = tmp_path / """cache"""
snake_case_ : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
snake_case_ : Optional[Any] = ParquetDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_parquet_dataset(__UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Dict=("train",) ):
'''simple docstring'''
assert isinstance(__UpperCamelCase , __UpperCamelCase )
for split in splits:
snake_case_ : Any = dataset_dict[split]
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
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = tmp_path / """cache"""
snake_case_ : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ : List[str] = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase ).read()
_check_parquet_datasetdict(__UpperCamelCase , __UpperCamelCase )
@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 __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Any , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = tmp_path / """cache"""
snake_case_ : Tuple = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
snake_case_ : Dict = features.copy() if features else default_expected_features
snake_case_ : Tuple = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ : Optional[Any] = ParquetDatasetReader({"""train""": parquet_path} , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_parquet_datasetdict(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] ):
'''simple docstring'''
if split:
snake_case_ : str = {split: parquet_path}
else:
snake_case_ : List[Any] = """train"""
snake_case_ : Any = {"""train""": parquet_path, """test""": parquet_path}
snake_case_ : int = tmp_path / """cache"""
snake_case_ : List[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
snake_case_ : int = ParquetDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_parquet_datasetdict(__UpperCamelCase , __UpperCamelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = ParquetDatasetWriter(__UpperCamelCase , tmp_path / """foo.parquet""" )
assert writer.write() > 0
snake_case_ : List[Any] = pq.ParquetFile(tmp_path / """foo.parquet""" )
snake_case_ : Union[str, Any] = pf.read()
assert dataset.data.table == output_table
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : List[str] = str(shared_datadir / """test_image_rgb.jpg""" )
snake_case_ : Any = {"""image""": [image_path]}
snake_case_ : str = Features({"""image""": Image()} )
snake_case_ : int = Dataset.from_dict(__UpperCamelCase , features=__UpperCamelCase )
snake_case_ : List[str] = ParquetDatasetWriter(__UpperCamelCase , tmp_path / """foo.parquet""" )
assert writer.write() > 0
snake_case_ : str = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
snake_case_ : Any = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=__UpperCamelCase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
assert get_writer_batch_size(__UpperCamelCase ) == expected
| 705 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : int = [0] * len(__UpperCamelCase )
snake_case_ : List[str] = []
snake_case_ : Any = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCamelCase )
while queue:
snake_case_ : Optional[int] = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case_ : Union[str, Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__UpperCamelCase )
print(max(__UpperCamelCase ) )
# Adjacency list of Graph
__lowerCAmelCase : str = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 21 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowerCAmelCase : Any = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase : Tuple = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase : Tuple = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = len([g for position, g in enumerate(lowercase_ ) if g == main_target[position]] )
return (item, float(lowercase_ ))
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Tuple = random.randint(0 , len(lowercase_ ) - 1 )
snake_case_ : Dict = parent_a[:random_slice] + parent_a[random_slice:]
snake_case_ : Tuple = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = list(lowercase_ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
snake_case_ : int = random.choice(lowercase_ )
return "".join(lowercase_ )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : List[str] , ):
'''simple docstring'''
snake_case_ : Optional[Any] = []
# Generate more children proportionally to the fitness score.
snake_case_ : List[str] = int(parent_a[1] * 1_0_0 ) + 1
snake_case_ : Union[str, Any] = 1_0 if child_n >= 1_0 else child_n
for _ in range(lowercase_ ):
snake_case_ : Dict = population_score[random.randint(0 , lowercase_ )][0]
snake_case_ : Dict = crossover(parent_a[0] , lowercase_ )
# Append new string to the population list.
pop.append(mutate(lowercase_ , lowercase_ ) )
pop.append(mutate(lowercase_ , lowercase_ ) )
return pop
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] = True ):
'''simple docstring'''
if N_POPULATION < N_SELECTED:
snake_case_ : List[str] = F'{N_POPULATION} must be bigger than {N_SELECTED}'
raise ValueError(lowercase_ )
# Verify that the target contains no genes besides the ones inside genes variable.
snake_case_ : int = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
snake_case_ : int = F'{not_in_genes_list} is not in genes list, evolution cannot converge'
raise ValueError(lowercase_ )
# Generate random starting population.
snake_case_ : Union[str, Any] = []
for _ in range(lowercase_ ):
population.append("""""".join([random.choice(lowercase_ ) for i in range(len(lowercase_ ) )] ) )
# Just some logs to know what the algorithms is doing.
snake_case_ : str = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(lowercase_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
snake_case_ : int = [evaluate(lowercase_ , lowercase_ ) for item in population]
# Check if there is a matching evolution.
snake_case_ : Optional[Any] = sorted(lowercase_ , key=lambda __UpperCamelCase : x[1] , reverse=lowercase_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 1_0 == 0:
print(
F'\nGeneration: {generation}'
F'\nTotal Population:{total_population}'
F'\nBest score: {population_score[0][1]}'
F'\nBest string: {population_score[0][0]}' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
snake_case_ : str = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(lowercase_ )
# Normalize population score to be between 0 and 1.
snake_case_ : str = [
(item, score / len(lowercase_ )) for item, score in population_score
]
# This is selection
for i in range(lowercase_ ):
population.extend(select(population_score[int(lowercase_ )] , lowercase_ , lowercase_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(lowercase_ ) > N_POPULATION:
break
if __name__ == "__main__":
__lowerCAmelCase : List[str] = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
__lowerCAmelCase : Dict = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : int = basic(target_str, genes_list)
print(
F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 706 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(__UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return binomial_coefficient(2 * node_count , __UpperCamelCase ) // (node_count + 1)
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
snake_case_ : Optional[int] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return catalan_number(__UpperCamelCase ) * factorial(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 21 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[Any] = args.log_outputs
snake_case_ : str = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
snake_case_ : Dict = load_metric("""wer""" )
snake_case_ : List[Any] = load_metric("""cer""" )
# compute metrics
snake_case_ : Dict = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
snake_case_ : Optional[int] = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
snake_case_ : List[str] = F'WER: {wer_result}\nCER: {cer_result}'
print(__UpperCamelCase )
with open(F'{dataset_id}_eval_results.txt' , """w""" ) as f:
f.write(__UpperCamelCase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
snake_case_ : Dict = F'log_{dataset_id}_predictions.txt'
snake_case_ : str = F'log_{dataset_id}_targets.txt'
with open(__UpperCamelCase , """w""" ) as p, open(__UpperCamelCase , """w""" ) as t:
# mapping function to write output
def write_to_file(__UpperCamelCase : List[Any] , __UpperCamelCase : str ):
p.write(F'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(__UpperCamelCase , with_indices=__UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
snake_case_ : str = re.sub(__UpperCamelCase , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
snake_case_ : Optional[Any] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
snake_case_ : int = """ """.join(text.split(__UpperCamelCase ) )
return text
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Any = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__UpperCamelCase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
snake_case_ : List[str] = AutoFeatureExtractor.from_pretrained(args.model_id )
snake_case_ : List[str] = feature_extractor.sampling_rate
# resample audio
snake_case_ : Optional[int] = dataset.cast_column("""audio""" , Audio(sampling_rate=__UpperCamelCase ) )
# load eval pipeline
if args.device is None:
snake_case_ : List[Any] = 0 if torch.cuda.is_available() else -1
snake_case_ : int = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__UpperCamelCase : List[Any] ):
snake_case_ : int = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
snake_case_ : int = prediction["""text"""]
snake_case_ : Dict = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
snake_case_ : List[str] = dataset.map(__UpperCamelCase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : str = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__lowerCAmelCase : Tuple = parser.parse_args()
main(args)
| 707 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''swin'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=2_2_4 , _lowercase=4 , _lowercase=3 , _lowercase=9_6 , _lowercase=[2, 2, 6, 2] , _lowercase=[3, 6, 1_2, 2_4] , _lowercase=7 , _lowercase=4.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=3_2 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : str = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = embed_dim
snake_case_ : Optional[int] = depths
snake_case_ : Optional[int] = len(_lowercase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : Optional[Any] = window_size
snake_case_ : Optional[Any] = mlp_ratio
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = drop_path_rate
snake_case_ : List[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Any = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Tuple = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Tuple = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 21 | 0 |
"""simple docstring"""
# Copyright 2023 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Dict = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 708 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
snake_case_ : List[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
snake_case_ : Dict = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
snake_case_ : List[str] = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
snake_case_ : Optional[Any] = shift_tokens_right(_lowercase , model.config.pad_token_id , model.config.decoder_start_token_id )
snake_case_ : Tuple = model(_lowercase , decoder_input_ids=_lowercase ).logits
snake_case_ : Tuple = optax.softmax_cross_entropy(_lowercase , onehot(_lowercase , logits.shape[-1] ) ).mean()
snake_case_ : List[str] = -(labels.shape[-1] * loss.item())
snake_case_ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : List[Any] = 5_0 ):
'''simple docstring'''
snake_case_ : str = [1] * (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 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 709 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 21 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__lowerCAmelCase : int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def __lowerCAmelCase ( __UpperCamelCase : str = "mumbai" ):
'''simple docstring'''
snake_case_ : Tuple = BeautifulSoup(requests.get(url + location ).content , """html.parser""" )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("""div""" , attrs={"""data-tn-component""": """organicJob"""} ):
snake_case_ : int = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""} ).text.strip()
snake_case_ : Union[str, Any] = job.find("""span""" , {"""class""": """company"""} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs('''Bangalore'''), 1):
print(F'''Job {i:>2} is {job[0]} at {job[1]}''')
| 710 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCAmelCase : List[str] = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
__lowerCAmelCase : Optional[Any] = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
__lowerCAmelCase : str = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Any ):
'''simple docstring'''
return float((preds == labels).mean() )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : str="binary" ):
'''simple docstring'''
snake_case_ : Optional[Any] = simple_accuracy(__UpperCamelCase , __UpperCamelCase )
snake_case_ : Dict = float(fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average=__UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[Any] = {}
for id_pred, label in zip(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : Optional[int] = F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
snake_case_ : Union[str, Any] = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
snake_case_ : str = [(pred, label)]
snake_case_ , snake_case_ : List[str] = [], []
for question, preds_labels in question_map.items():
snake_case_ , snake_case_ : Optional[Any] = zip(*__UpperCamelCase )
snake_case_ : int = fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average="""macro""" )
fas.append(__UpperCamelCase )
snake_case_ : Dict = int(sum(pred == label for pred, label in preds_labels ) == len(__UpperCamelCase ) )
ems.append(__UpperCamelCase )
snake_case_ : Optional[int] = float(sum(__UpperCamelCase ) / len(__UpperCamelCase ) )
snake_case_ : Any = sum(__UpperCamelCase ) / len(__UpperCamelCase )
snake_case_ : int = float(fa_score(y_true=__UpperCamelCase , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_lowercase , _lowercase )}
elif self.config_name == "cb":
return acc_and_fa(_lowercase , _lowercase , fa_avg="""macro""" )
elif self.config_name == "record":
snake_case_ : Optional[Any] = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
snake_case_ : Dict = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(_lowercase , _lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_lowercase , _lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 21 | 0 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase : Dict = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase : Optional[Any] = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> List[str]:
'''simple docstring'''
snake_case_ : int = WATERMARK_BITS
snake_case_ : str = WatermarkEncoder()
self.encoder.set_watermark("""bits""" , self.watermark )
def UpperCAmelCase__ ( self , _lowercase ) -> List[str]:
'''simple docstring'''
if images.shape[-1] < 2_5_6:
return images
snake_case_ : Any = (2_5_5 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
snake_case_ : Union[str, Any] = [self.encoder.encode(_lowerCamelCase , """dwtDct""" ) for image in images]
snake_case_ : Tuple = torch.from_numpy(np.array(_lowerCamelCase ) ).permute(0 , 3 , 1 , 2 )
snake_case_ : List[str] = torch.clamp(2 * (images / 2_5_5 - 0.5) , min=-1.0 , max=1.0 )
return images
| 711 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Dict = batch_size
snake_case_ : Any = seq_length
snake_case_ : Tuple = is_training
snake_case_ : Dict = use_attention_mask
snake_case_ : int = use_token_type_ids
snake_case_ : List[Any] = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : str = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : int = max_position_embeddings
snake_case_ : Dict = type_vocab_size
snake_case_ : Dict = type_sequence_label_size
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Tuple = num_choices
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : List[str] = None
if self.use_token_type_ids:
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Tuple = RobertaPreLayerNormConfig(
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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : int = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Any = config_and_inputs
snake_case_ : Union[str, Any] = True
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : List[str] = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : str = model(_lowercase )[0]
snake_case_ : int = [1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ) , _lowercase )
# compare the actual values for a slice.
snake_case_ : Tuple = np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
@slow
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Dict = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : Any = model(_lowercase )[0]
# compare the actual values for a slice.
snake_case_ : Optional[Any] = np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
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(math.sqrt(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : str = 2
while True:
if is_prime(__snake_case ):
yield num
num += 1
def __lowerCAmelCase ( __UpperCamelCase : int = 2_0_0_0_0_0_0 ):
'''simple docstring'''
return sum(takewhile(lambda __UpperCamelCase : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 712 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCAmelCase : Optional[Any] = parser.parse_args()
__lowerCAmelCase : Dict = '''cpu'''
__lowerCAmelCase : Optional[Any] = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__lowerCAmelCase : Tuple = '''path-to-your-trained-model'''
__lowerCAmelCase : List[Any] = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__lowerCAmelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase : List[Any] = pipe.to(device)
# to channels last
__lowerCAmelCase : Optional[Any] = pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase : List[str] = pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase : Optional[Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__lowerCAmelCase : Dict = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__lowerCAmelCase : Tuple = torch.randn(2, 4, 64, 64)
__lowerCAmelCase : Any = torch.rand(1) * 999
__lowerCAmelCase : List[str] = torch.randn(2, 77, 768)
__lowerCAmelCase : Optional[int] = (sample, timestep, encoder_hidden_status)
try:
__lowerCAmelCase : List[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__lowerCAmelCase : Optional[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : Any = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : int = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__lowerCAmelCase : Union[str, Any] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__lowerCAmelCase : List[str] = 666
__lowerCAmelCase : Optional[int] = torch.Generator(device).manual_seed(seed)
__lowerCAmelCase : List[Any] = {'''generator''': generator}
if args.steps is not None:
__lowerCAmelCase : Any = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__lowerCAmelCase : str = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Dict = {
'''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
'''processing_mgp_str''': ['''MgpstrProcessor'''],
'''tokenization_mgp_str''': ['''MgpstrTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Dict = [
'''MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MgpstrModel''',
'''MgpstrPreTrainedModel''',
'''MgpstrForSceneTextRecognition''',
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 713 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = RoFormerTokenizer
_lowerCamelCase = RoFormerTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
def UpperCAmelCase__ ( self , **_lowercase ) -> str:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = """永和服装饰品有限公司,今天天气非常好"""
snake_case_ : int = """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ , snake_case_ : Optional[Any] = self.get_chinese_input_output_texts()
snake_case_ : List[str] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : str = tokens + [tokenizer.unk_token]
snake_case_ : Tuple = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ , snake_case_ : List[Any] = self.get_chinese_input_output_texts()
snake_case_ : Union[str, Any] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : Optional[int] = tokens + [tokenizer.unk_token]
snake_case_ : Union[str, Any] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : list[int] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Dict = int(UpperCamelCase__ )
# Initialize Result
snake_case_ : str = []
# Traverse through all denomination
for denomination in reversed(UpperCamelCase__ ):
# Find denominations
while int(UpperCamelCase__ ) >= int(UpperCamelCase__ ):
total_value -= int(UpperCamelCase__ )
answer.append(UpperCamelCase__ ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
__lowerCAmelCase : int = []
__lowerCAmelCase : List[str] = '''0'''
if (
input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
== "y"
):
__lowerCAmelCase : Optional[int] = int(input('''Enter the number of denominations you want to add: ''').strip())
for i in range(0, n):
denominations.append(int(input(F'''Denomination {i}: ''').strip()))
__lowerCAmelCase : int = input('''Enter the change you want to make in Indian Currency: ''').strip()
else:
# All denominations of Indian Currency if user does not enter
__lowerCAmelCase : Dict = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
__lowerCAmelCase : List[Any] = input('''Enter the change you want to make: ''').strip()
if int(value) == 0 or int(value) < 0:
print('''The total value cannot be zero or negative.''')
else:
print(F'''Following is minimal change for {value}: ''')
__lowerCAmelCase : Optional[Any] = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=''' ''')
| 714 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
snake_case_ : List[Any] = [
2_0_4_7,
1_3_7_3_6_5_3,
2_5_3_2_6_0_0_1,
3_2_1_5_0_3_1_7_5_1,
2_1_5_2_3_0_2_8_9_8_7_4_7,
3_4_7_4_7_4_9_6_6_0_3_8_3,
3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
1,
3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
1,
1,
3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
]
snake_case_ : Dict = [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
for idx, _p in enumerate(__UpperCamelCase , 1 ):
if n < _p:
# then we have our last prime to check
snake_case_ : Optional[int] = primes[:idx]
break
snake_case_ , snake_case_ : Tuple = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
snake_case_ : List[str] = False
for r in range(__UpperCamelCase ):
snake_case_ : int = pow(__UpperCamelCase , d * 2**r , __UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
snake_case_ : Optional[Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def __lowerCAmelCase ( ):
'''simple docstring'''
assert not miller_rabin(5_6_1 )
assert miller_rabin(5_6_3 )
# 2047
assert not miller_rabin(8_3_8_2_0_1 )
assert miller_rabin(8_3_8_2_0_7 )
# 1_373_653
assert not miller_rabin(1_7_3_1_6_0_0_1 )
assert miller_rabin(1_7_3_1_6_0_1_7 )
# 25_326_001
assert not miller_rabin(3_0_7_8_3_8_6_6_4_1 )
assert miller_rabin(3_0_7_8_3_8_6_6_5_3 )
# 3_215_031_751
assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1 )
assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9 )
# 2_152_302_898_747
assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7 )
assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7 )
# 3_474_749_660_383
assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1 )
assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7 )
# 341_550_071_728_321
assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1 )
assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7 )
assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3 )
assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 21 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__lowerCAmelCase : Any = 3
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
print("""Generating primitive root of p""" )
while True:
snake_case_ : Dict = random.randrange(3 , _UpperCAmelCase )
if pow(_UpperCAmelCase , 2 , _UpperCAmelCase ) == 1:
continue
if pow(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) == 1:
continue
return g
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
print("""Generating prime p...""" )
snake_case_ : Optional[Any] = rabin_miller.generate_large_prime(_UpperCAmelCase ) # select large prime number.
snake_case_ : int = primitive_root(_UpperCAmelCase ) # one primitive root on modulo p.
snake_case_ : List[Any] = random.randrange(3 , _UpperCAmelCase ) # private_key -> have to be greater than 2 for safety.
snake_case_ : Optional[Any] = cryptomath.find_mod_inverse(pow(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase )
snake_case_ : List[str] = (key_size, e_a, e_a, p)
snake_case_ : Dict = (key_size, d)
return public_key, private_key
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Tuple ):
'''simple docstring'''
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()
snake_case_ , snake_case_ : Dict = generate_key(_UpperCAmelCase )
print(F'\nWriting public key to file {name}_pubkey.txt...' )
with open(F'{name}_pubkey.txt' , """w""" ) as fo:
fo.write(F'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}' )
print(F'Writing private key to file {name}_privkey.txt...' )
with open(F'{name}_privkey.txt' , """w""" ) as fo:
fo.write(F'{private_key[0]},{private_key[1]}' )
def __lowerCAmelCase ( ):
'''simple docstring'''
print("""Making key files...""" )
make_key_files("""elgamal""" , 2_0_4_8 )
print("""Key files generation successful""" )
if __name__ == "__main__":
main()
| 715 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def is_in_circle(__UpperCamelCase : float , __UpperCamelCase : float ) -> bool:
snake_case_ : Dict = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
snake_case_ : Tuple = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
snake_case_ : Union[str, Any] = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Callable[[float], float] , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 , ):
'''simple docstring'''
return mean(
function_to_integrate(uniform(__UpperCamelCase , __UpperCamelCase ) ) for _ in range(__UpperCamelCase ) ) * (max_value - min_value)
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ):
'''simple docstring'''
def identity_function(__UpperCamelCase : float ) -> float:
return x
snake_case_ : int = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : str = (max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("""******************""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def function_to_integrate(__UpperCamelCase : float ) -> float:
return sqrt(4.0 - x * x )
snake_case_ : List[Any] = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Any = RemBertConfig.from_json_file(__UpperCamelCase )
print("""Building PyTorch model from configuration: {}""".format(str(__UpperCamelCase ) ) )
snake_case_ : str = RemBertModel(__UpperCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Save pytorch-model
print("""Save PyTorch model to {}""".format(__UpperCamelCase ) )
torch.save(model.state_dict() , __UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--rembert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained RemBERT 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.'''
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 716 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = nn.functional.normalize(__UpperCamelCase )
snake_case_ : Tuple = nn.functional.normalize(__UpperCamelCase )
return torch.mm(__UpperCamelCase , normalized_text_embeds.t() )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = CLIPConfig
_lowerCamelCase = ['''CLIPEncoderLayer''']
def __init__( self , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(_lowercase )
snake_case_ : Tuple = CLIPVisionModel(config.vision_config )
snake_case_ : int = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=_lowercase )
snake_case_ : Optional[Any] = nn.Parameter(torch.ones(1_7 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Dict = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Any = nn.Parameter(torch.ones(1_7 ) , requires_grad=_lowercase )
snake_case_ : List[str] = nn.Parameter(torch.ones(3 ) , requires_grad=_lowercase )
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : int = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : str = self.visual_projection(_lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Dict = cosine_distance(_lowercase , self.special_care_embeds ).cpu().float().numpy()
snake_case_ : List[str] = cosine_distance(_lowercase , self.concept_embeds ).cpu().float().numpy()
snake_case_ : Any = []
snake_case_ : Any = image_embeds.shape[0]
for i in range(_lowercase ):
snake_case_ : List[Any] = {"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : int = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
snake_case_ : List[str] = special_cos_dist[i][concept_idx]
snake_case_ : Union[str, Any] = self.special_care_embeds_weights[concept_idx].item()
snake_case_ : Tuple = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]} )
snake_case_ : Dict = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
snake_case_ : int = cos_dist[i][concept_idx]
snake_case_ : List[Any] = self.concept_embeds_weights[concept_idx].item()
snake_case_ : List[str] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(_lowercase )
result.append(_lowercase )
snake_case_ : Union[str, Any] = [len(res["""bad_concepts"""] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : List[str] = self.visual_projection(_lowercase )
snake_case_ : str = cosine_distance(_lowercase , self.special_care_embeds )
snake_case_ : Optional[int] = cosine_distance(_lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : Tuple = 0.0
snake_case_ : List[Any] = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
snake_case_ : str = torch.any(special_scores > 0 , dim=1 )
snake_case_ : List[str] = special_care * 0.01
snake_case_ : Optional[int] = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
snake_case_ : Optional[Any] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
snake_case_ : str = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 21 | 0 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=6_4 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Any = seq_length
snake_case_ : str = is_training
snake_case_ : Any = use_input_mask
snake_case_ : Tuple = use_token_type_ids
snake_case_ : Any = use_labels
snake_case_ : Dict = vocab_size
snake_case_ : str = hidden_size
snake_case_ : Optional[int] = embedding_size
snake_case_ : int = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Tuple = intermediate_size
snake_case_ : Optional[int] = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : List[str] = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : Optional[int] = initializer_range
snake_case_ : Optional[int] = num_labels
snake_case_ : List[str] = num_choices
snake_case_ : Optional[Any] = scope
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : List[Any] = None
if self.use_input_mask:
snake_case_ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : int = None
if self.use_token_type_ids:
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Optional[Any] = None
snake_case_ : Any = None
snake_case_ : Any = None
if self.use_labels:
snake_case_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return MegatronBertConfig(
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 , embedding_size=self.embedding_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 UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = MegatronBertModel(config=__A )
model.to(__A )
model.eval()
snake_case_ : List[Any] = model(__A , attention_mask=__A , token_type_ids=__A )
snake_case_ : Tuple = model(__A , token_type_ids=__A )
snake_case_ : 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 UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = MegatronBertForMaskedLM(config=__A )
model.to(__A )
model.eval()
snake_case_ : int = 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 UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> int:
'''simple docstring'''
snake_case_ : Tuple = MegatronBertForCausalLM(config=__A )
model.to(__A )
model.eval()
snake_case_ : List[str] = 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 UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = MegatronBertForNextSentencePrediction(config=__A )
model.to(__A )
model.eval()
snake_case_ : str = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = MegatronBertForPreTraining(config=__A )
model.to(__A )
model.eval()
snake_case_ : Tuple = 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 UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = MegatronBertForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
snake_case_ : Union[str, Any] = 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 UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.num_labels
snake_case_ : Optional[Any] = MegatronBertForSequenceClassification(__A )
model.to(__A )
model.eval()
snake_case_ : str = model(__A , attention_mask=__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = self.num_labels
snake_case_ : str = MegatronBertForTokenClassification(config=__A )
model.to(__A )
model.eval()
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.seq_length, self.num_labels) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.num_choices
snake_case_ : List[str] = MegatronBertForMultipleChoice(config=__A )
model.to(__A )
model.eval()
snake_case_ : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Any = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Dict = model(
__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) : Optional[int] = config_and_inputs
snake_case_ : Optional[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
_lowerCamelCase = (
{
'''feature-extraction''': MegatronBertModel,
'''fill-mask''': MegatronBertForMaskedLM,
'''question-answering''': MegatronBertForQuestionAnswering,
'''text-classification''': MegatronBertForSequenceClassification,
'''text-generation''': MegatronBertForCausalLM,
'''token-classification''': MegatronBertForTokenClassification,
'''zero-shot''': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCamelCase = True
# test_resize_embeddings = False
_lowerCamelCase = False
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase=False ) -> Dict:
'''simple docstring'''
snake_case_ : Any = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class in get_values(__A ):
snake_case_ : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__A )
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : str = MegatronBertModelTester(self )
snake_case_ : Any = ConfigTester(self , config_class=__A , hidden_size=3_7 )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__A )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__A )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__A )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__A )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__A )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__A )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__A )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__A )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
return torch.tensor(
_lowercase , dtype=torch.long , device=_lowercase , )
__lowerCAmelCase : int = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
@unittest.skip("""Model is not available.""" )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = """nvidia/megatron-bert-uncased-345m"""
if "MYDIR" in os.environ:
snake_case_ : Optional[int] = os.path.join(os.environ["""MYDIR"""] , __A )
snake_case_ : List[str] = MegatronBertModel.from_pretrained(__A )
model.to(__A )
model.half()
snake_case_ : List[str] = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
snake_case_ : List[str] = model(__A )[0]
snake_case_ : List[str] = torch.Size((1, 9, 1_0_2_4) )
self.assertEqual(output.shape , __A )
snake_case_ : Optional[Any] = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
snake_case_ : List[Any] = output[0, ii, jj]
snake_case_ : Tuple = expected[3 * ii + jj]
snake_case_ : Union[str, Any] = """ii={} jj={} a={} b={}""".format(__A , __A , __A , __A )
self.assertTrue(math.isclose(__A , __A , rel_tol=__A , abs_tol=__A ) , msg=__A )
| 717 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : List[str] = []
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
F'stage{idx}.patch_embed.proj.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
F'stage{idx}.patch_embed.proj.bias',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
F'stage{idx}.patch_embed.norm.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
F'stage{idx}.patch_embed.norm.bias',
) )
return embed
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : str = []
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
F'stage{idx}.blocks.{cnt}.attn.proj.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
F'stage{idx}.blocks.{cnt}.attn.proj.bias',
) )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias') )
return attention_weights
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : int = []
token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""") )
return token
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = []
head.append(("""layernorm.weight""", """norm.weight""") )
head.append(("""layernorm.bias""", """norm.bias""") )
head.append(("""classifier.weight""", """head.weight""") )
head.append(("""classifier.bias""", """head.bias""") )
return head
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = """imagenet-1k-id2label.json"""
snake_case_ : Optional[Any] = 1_0_0_0
snake_case_ : Any = """huggingface/label-files"""
snake_case_ : Tuple = num_labels
snake_case_ : Dict = json.load(open(cached_download(hf_hub_url(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
snake_case_ : str = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[str] = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
snake_case_ : Dict = CvtConfig(num_labels=__UpperCamelCase , idalabel=__UpperCamelCase , labelaid=__UpperCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "13":
snake_case_ : Any = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "21":
snake_case_ : Any = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ : Optional[int] = [2, 2, 2_0]
snake_case_ : str = [3, 1_2, 1_6]
snake_case_ : Any = [1_9_2, 7_6_8, 1_0_2_4]
snake_case_ : Union[str, Any] = CvtForImageClassification(__UpperCamelCase )
snake_case_ : str = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
snake_case_ : List[Any] = image_size
snake_case_ : str = torch.load(__UpperCamelCase , map_location=torch.device("""cpu""" ) )
snake_case_ : Any = OrderedDict()
snake_case_ : Tuple = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ : Optional[Any] = list_of_state_dict + cls_token(__UpperCamelCase )
snake_case_ : str = list_of_state_dict + embeddings(__UpperCamelCase )
for cnt in range(config.depth[idx] ):
snake_case_ : List[str] = list_of_state_dict + attention(__UpperCamelCase , __UpperCamelCase )
snake_case_ : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__UpperCamelCase )
for i in range(len(__UpperCamelCase ) ):
snake_case_ : Union[str, Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
image_processor.save_pretrained(__UpperCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowerCAmelCase : Dict = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 21 | 0 |
from collections import deque
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Optional[Any] = len(_lowerCamelCase )
snake_case_ : int = deque()
snake_case_ : List[str] = [False for _ in range(_lowerCamelCase )]
snake_case_ : int = [-1 for _ in range(_lowerCamelCase )]
snake_case_ : Optional[Any] = index_of[:]
def strong_connect(__UpperCamelCase : List[str] , __UpperCamelCase : List[str] , __UpperCamelCase : Dict ):
snake_case_ : Tuple = index # the number when this node is seen
snake_case_ : List[str] = index # lowest rank node reachable from here
index += 1
stack.append(_lowerCamelCase )
snake_case_ : Union[str, Any] = True
for w in g[v]:
if index_of[w] == -1:
snake_case_ : Optional[int] = strong_connect(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
snake_case_ : Dict = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
snake_case_ : Optional[int] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
snake_case_ : Union[str, Any] = []
snake_case_ : Any = stack.pop()
snake_case_ : Optional[int] = False
component.append(_lowerCamelCase )
while w != v:
snake_case_ : str = stack.pop()
snake_case_ : Dict = False
component.append(_lowerCamelCase )
components.append(_lowerCamelCase )
return index
snake_case_ : Union[str, Any] = []
for v in range(_lowerCamelCase ):
if index_of[v] == -1:
strong_connect(_lowerCamelCase , 0 , _lowerCamelCase )
return components
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = [[] for _ in range(_lowerCamelCase )]
for u, v in edges:
g[u].append(_lowerCamelCase )
return g
if __name__ == "__main__":
# Test
__lowerCAmelCase : List[str] = 7
__lowerCAmelCase : Optional[Any] = [0, 0, 1, 2, 3, 3, 4, 4, 6]
__lowerCAmelCase : int = [1, 3, 2, 0, 1, 4, 5, 6, 5]
__lowerCAmelCase : Dict = [(u, v) for u, v in zip(source, target)]
__lowerCAmelCase : int = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 718 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MgpstrTokenizer
_lowerCamelCase = False
_lowerCamelCase = {}
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# fmt: off
snake_case_ : Optional[Any] = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
snake_case_ : str = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowercase ) + """\n""" )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = """tester"""
snake_case_ : Tuple = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ : Optional[Any] = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
snake_case_ : Union[str, Any] = tokenizer.encode([special_token] , add_special_tokens=_lowercase )
self.assertEqual(len(_lowercase ) , 1 )
snake_case_ : List[Any] = tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ , snake_case_ : Union[str, Any] = self.get_input_output_texts(_lowercase )
snake_case_ : List[Any] = tokenizer.tokenize(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertNotEqual(len(_lowercase ) , 0 )
snake_case_ : str = tokenizer.decode(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(text_a.replace(""" """ , """""" ) , _lowercase )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
__lowerCAmelCase : Optional[Any] = TypeVar('''T''')
class _lowerCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : int = data
snake_case_ : Optional[int] = self
snake_case_ : int = 0
class _lowerCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> int:
'''simple docstring'''
snake_case_ : dict[T, DisjointSetTreeNode[T]] = {}
def UpperCAmelCase__ ( self , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = DisjointSetTreeNode(A__ )
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = self.map[data]
if elem_ref != elem_ref.parent:
snake_case_ : str = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
if nodea.rank > nodea.rank:
snake_case_ : Tuple = nodea
else:
snake_case_ : str = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
self.link(self.find_set(A__ ) , self.find_set(A__ ) )
class _lowerCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : dict[T, dict[T, int]] = {}
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[Any]:
'''simple docstring'''
if node not in self.connections:
snake_case_ : Dict = {}
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
self.add_node(A__ )
self.add_node(A__ )
snake_case_ : int = weight
snake_case_ : int = weight
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = []
snake_case_ : str = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda _lowercase : x[2] )
# creating the disjoint set
snake_case_ : List[Any] = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(A__ )
# MST generation
snake_case_ : Union[str, Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
snake_case_ : Union[str, Any] = edges[index]
index += 1
snake_case_ : Tuple = disjoint_set.find_set(A__ )
snake_case_ : Union[str, Any] = disjoint_set.find_set(A__ )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(A__ , A__ , A__ )
disjoint_set.union(A__ , A__ )
return graph
| 719 |
"""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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : List[str] = batch_size
snake_case_ : int = seq_length
snake_case_ : List[Any] = is_training
snake_case_ : Optional[int] = use_attention_mask
snake_case_ : Optional[Any] = use_token_type_ids
snake_case_ : Union[str, Any] = use_labels
snake_case_ : str = vocab_size
snake_case_ : List[str] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : List[str] = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : str = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : str = num_choices
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[int] = None
if self.use_token_type_ids:
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = 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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_lowercase )
snake_case_ : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case_ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ : Dict = model(_lowercase )[0]
snake_case_ : Optional[int] = 5_0_0_0_0
snake_case_ : Union[str, Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _lowercase )
snake_case_ : Dict = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
from math import isqrt
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCAmelCase__ ) + 1 ) )
def __lowerCAmelCase ( __UpperCamelCase : int = 1_0**6 ):
'''simple docstring'''
snake_case_ : List[Any] = 0
snake_case_ : List[Any] = 1
snake_case_ : str = 7
while prime_candidate < max_prime:
primes_count += is_prime(lowerCAmelCase__ )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 720 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : int = 1_0
snake_case_ : Any = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
snake_case_ : Tuple = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(__UpperCamelCase ) ),
} , features=__UpperCamelCase , )
return dataset
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return filename
# FILE_CONTENT + files
__lowerCAmelCase : List[Any] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
snake_case_ : Optional[Any] = FILE_CONTENT
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
import bza
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
snake_case_ : Any = bytes(__UpperCamelCase , """utf-8""" )
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
import gzip
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
snake_case_ : List[Any] = bytes(__UpperCamelCase , """utf-8""" )
with gzip.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
snake_case_ : Optional[Any] = bytes(__UpperCamelCase , """utf-8""" )
with lza.frame.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(__UpperCamelCase , """w""" ) as archive:
archive.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple ):
'''simple docstring'''
import tarfile
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
import lzma
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
snake_case_ : str = bytes(__UpperCamelCase , """utf-8""" )
with lzma.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
import zipfile
snake_case_ : Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
snake_case_ : Tuple = bytes(__UpperCamelCase , """utf-8""" )
with zstd.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
snake_case_ : List[str] = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
__lowerCAmelCase : List[str] = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__lowerCAmelCase : Tuple = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCAmelCase : int = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
__lowerCAmelCase : int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
__lowerCAmelCase : Any = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = datasets.Dataset.from_dict(__UpperCamelCase )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(__UpperCamelCase ) ) as con:
snake_case_ : Tuple = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : Optional[Any] = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : str = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : int ):
'''simple docstring'''
import bza
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(__UpperCamelCase , """rb""" ) as f:
snake_case_ : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(__UpperCamelCase , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
snake_case_ : Any = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(__UpperCamelCase , """wb""" ) as f:
snake_case_ : Optional[int] = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase )
snake_case_ : Optional[int] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCamelCase ) )] for k in DATA[0]} , schema=__UpperCamelCase )
writer.write_table(__UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : Any = {"""data""": DATA}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : List[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
import gzip
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] ):
'''simple docstring'''
import gzip
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : List[str] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : str = ["""0""", """1""", """2""", """3"""]
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : int = ["""0""", """1""", """2""", """3"""]
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : List[Any] = ["""0""", """1""", """2""", """3"""]
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 21 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__lowerCAmelCase : Tuple = datasets.utils.logging.get_logger(__name__)
class _lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
"""simple docstring"""
_lowerCamelCase = None
_lowerCamelCase = None
class _lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
"""simple docstring"""
_lowerCamelCase = datasets.Audio()
_lowerCamelCase = "audio"
_lowerCamelCase = AudioFolderConfig
_lowerCamelCase = 42 # definition at the bottom of the script
_lowerCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
__lowerCAmelCase : Optional[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""",
]
__lowerCAmelCase : Tuple = AUDIO_EXTENSIONS
| 721 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ : List[str] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : Union[str, Any] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : int = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X20000 and cp <= 0X2A6DF) #
or (cp >= 0X2A700 and cp <= 0X2B73F) #
or (cp >= 0X2B740 and cp <= 0X2B81F) #
or (cp >= 0X2B820 and cp <= 0X2CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2F800 and cp <= 0X2FA1F) #
): #
return True
return False
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
for char in word:
snake_case_ : str = ord(__UpperCamelCase )
if not _is_chinese_char(__UpperCamelCase ):
return 0
return 1
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Any = set()
for token in tokens:
snake_case_ : List[Any] = len(__UpperCamelCase ) > 1 and is_chinese(__UpperCamelCase )
if chinese_word:
word_set.add(__UpperCamelCase )
snake_case_ : Optional[Any] = list(__UpperCamelCase )
return word_list
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
snake_case_ : int = max([len(__UpperCamelCase ) for w in chinese_word_set] )
snake_case_ : str = bert_tokens
snake_case_ , snake_case_ : Union[str, Any] = 0, len(__UpperCamelCase )
while start < end:
snake_case_ : str = True
if is_chinese(bert_word[start] ):
snake_case_ : Union[str, Any] = min(end - start , __UpperCamelCase )
for i in range(__UpperCamelCase , 1 , -1 ):
snake_case_ : List[str] = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
snake_case_ : Dict = """##""" + bert_word[j]
snake_case_ : Optional[int] = start + i
snake_case_ : List[Any] = False
break
if single_word:
start += 1
return bert_word
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Any , __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Any = []
for i in range(0 , len(__UpperCamelCase ) , 1_0_0 ):
snake_case_ : Dict = ltp_tokenizer.pipeline(lines[i : i + 1_0_0] , tasks=["""cws"""] ).cws
snake_case_ : Optional[Any] = [get_chinese_word(__UpperCamelCase ) for r in res]
ltp_res.extend(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
snake_case_ : List[Any] = []
for i in range(0 , len(__UpperCamelCase ) , 1_0_0 ):
snake_case_ : Optional[Any] = bert_tokenizer(lines[i : i + 1_0_0] , add_special_tokens=__UpperCamelCase , truncation=__UpperCamelCase , max_length=5_1_2 )
bert_res.extend(res["""input_ids"""] )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
snake_case_ : Union[str, Any] = []
for input_ids, chinese_word in zip(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : List[str] = []
for id in input_ids:
snake_case_ : List[Any] = bert_tokenizer._convert_id_to_token(__UpperCamelCase )
input_tokens.append(__UpperCamelCase )
snake_case_ : Tuple = add_sub_symbol(__UpperCamelCase , __UpperCamelCase )
snake_case_ : int = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__UpperCamelCase ):
if token[:2] == "##":
snake_case_ : int = token[2:]
# save chinese tokens' pos
if len(__UpperCamelCase ) == 1 and _is_chinese_char(ord(__UpperCamelCase ) ):
ref_id.append(__UpperCamelCase )
ref_ids.append(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
return ref_ids
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
snake_case_ : Optional[int] = f.readlines()
snake_case_ : List[str] = [line.strip() for line in data if len(__UpperCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
snake_case_ : List[Any] = LTP(args.ltp ) # faster in GPU device
snake_case_ : Dict = BertTokenizer.from_pretrained(args.bert )
snake_case_ : Dict = prepare_ref(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
snake_case_ : Dict = [json.dumps(__UpperCamelCase ) + """\n""" for ref in ref_ids]
f.writelines(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Dict = argparse.ArgumentParser(description='''prepare_chinese_ref''')
parser.add_argument(
'''--file_name''',
required=False,
type=str,
default='''./resources/chinese-demo.txt''',
help='''file need process, same as training data in lm''',
)
parser.add_argument(
'''--ltp''',
required=False,
type=str,
default='''./resources/ltp''',
help='''resources for LTP tokenizer, usually a path''',
)
parser.add_argument(
'''--bert''',
required=False,
type=str,
default='''./resources/robert''',
help='''resources for Bert tokenizer''',
)
parser.add_argument(
'''--save_path''',
required=False,
type=str,
default='''./resources/ref.txt''',
help='''path to save res''',
)
__lowerCAmelCase : Dict = parser.parse_args()
main(args)
| 700 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Optional[Any] = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : int = [
'''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''IBertForMaskedLM''',
'''IBertForMultipleChoice''',
'''IBertForQuestionAnswering''',
'''IBertForSequenceClassification''',
'''IBertForTokenClassification''',
'''IBertModel''',
'''IBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 701 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''nat'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=4 , _lowercase=3 , _lowercase=6_4 , _lowercase=[3, 4, 6, 5] , _lowercase=[2, 4, 8, 1_6] , _lowercase=7 , _lowercase=3.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=0.0 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : Any = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = embed_dim
snake_case_ : Tuple = depths
snake_case_ : int = len(_lowercase )
snake_case_ : Optional[int] = num_heads
snake_case_ : List[str] = kernel_size
snake_case_ : str = mlp_ratio
snake_case_ : str = qkv_bias
snake_case_ : str = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Tuple = drop_path_rate
snake_case_ : Dict = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Tuple = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Union[str, Any] = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Union[str, Any] = layer_scale_init_value
snake_case_ : Optional[Any] = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 21 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = KandinskyVaaImgaImgPipeline
_lowerCamelCase = ['''image_embeds''', '''negative_image_embeds''', '''image''']
_lowerCamelCase = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_lowerCamelCase = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_lowerCamelCase = False
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return self.time_input_dim
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return self.time_input_dim * 4
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return 1_0_0
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[str] = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'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,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
snake_case_ : Union[str, Any] = UNetaDConditionModel(**_lowercase )
return model
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Any = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.dummy_unet
snake_case_ : str = self.dummy_movq
snake_case_ : str = {
'num_train_timesteps': 1_0_0_0,
'beta_schedule': 'linear',
'beta_start': 0.0_0085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
snake_case_ : Tuple = DDIMScheduler(**_lowercase )
snake_case_ : Dict = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def UpperCAmelCase__ ( self , _lowercase , _lowercase=0 ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_lowercase ) ).to(_lowercase )
snake_case_ : Optional[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_lowercase )
# create init_image
snake_case_ : Optional[Any] = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_lowercase ) ).to(_lowercase )
snake_case_ : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case_ : Any = Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((2_5_6, 2_5_6) )
if str(_lowercase ).startswith("""mps""" ):
snake_case_ : int = torch.manual_seed(_lowercase )
else:
snake_case_ : Any = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
snake_case_ : List[str] = {
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 6_4,
'width': 6_4,
'num_inference_steps': 1_0,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = 'cpu'
snake_case_ : Any = self.get_dummy_components()
snake_case_ : str = self.pipeline_class(**_lowercase )
snake_case_ : List[Any] = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : Optional[Any] = pipe(**self.get_dummy_inputs(_lowercase ) )
snake_case_ : Optional[int] = output.images
snake_case_ : int = pipe(
**self.get_dummy_inputs(_lowercase ) , return_dict=_lowercase , )[0]
snake_case_ : List[Any] = image[0, -3:, -3:, -1]
snake_case_ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case_ : Optional[int] = np.array(
[0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_img2img_frog.npy""" )
snake_case_ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
snake_case_ : Optional[int] = 'A red cartoon frog, 4k'
snake_case_ : Dict = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(_lowercase )
snake_case_ : str = KandinskyVaaImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder""" , torch_dtype=torch.floataa )
snake_case_ : Optional[int] = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
snake_case_ : Optional[int] = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case_ : str = pipe_prior(
_lowercase , generator=_lowercase , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
snake_case_ : Optional[int] = pipeline(
image=_lowercase , image_embeds=_lowercase , negative_image_embeds=_lowercase , generator=_lowercase , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="""np""" , )
snake_case_ : Tuple = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_lowercase , _lowercase )
| 702 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__lowerCAmelCase : Optional[Any] = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[Any] = VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(_lowercase )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = 1_2
snake_case_ : Tuple = 1_2
snake_case_ : Tuple = {
"""attention_bias""": True,
"""cross_attention_dim""": 3_2,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 3_2,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
snake_case_ : Optional[Any] = TransformeraDModel(**_lowercase )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = """cpu"""
snake_case_ : List[str] = self.dummy_vqvae
snake_case_ : Any = self.dummy_text_encoder
snake_case_ : Tuple = self.dummy_tokenizer
snake_case_ : int = self.dummy_transformer
snake_case_ : int = VQDiffusionScheduler(self.num_embed )
snake_case_ : Dict = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase )
snake_case_ : Optional[Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : int = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : List[Any] = """teddy bear playing in the pool"""
snake_case_ : Dict = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : List[Any] = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Optional[int] = output.images
snake_case_ : List[Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Dict = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : List[Any] = image[0, -3:, -3:, -1]
snake_case_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : Dict = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
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 UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : int = """cpu"""
snake_case_ : List[Any] = self.dummy_vqvae
snake_case_ : Optional[int] = self.dummy_text_encoder
snake_case_ : List[Any] = self.dummy_tokenizer
snake_case_ : Union[str, Any] = self.dummy_transformer
snake_case_ : str = VQDiffusionScheduler(self.num_embed )
snake_case_ : List[Any] = LearnedClassifierFreeSamplingEmbeddings(
learnable=_lowercase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ : Union[str, Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : Any = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : Tuple = """teddy bear playing in the pool"""
snake_case_ : str = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Tuple = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Dict = output.images
snake_case_ : Union[str, Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Any = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case_ : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : int = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
snake_case_ : str = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
snake_case_ : Optional[Any] = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ : Any = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Optional[int] = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=_lowercase , output_type="""np""" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : dict ):
'''simple docstring'''
snake_case_ : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case_ : set[int] = set()
return any(
node not in visited and depth_first_search(__lowercase , __lowercase , __lowercase , __lowercase )
for node in graph )
def __lowerCAmelCase ( __UpperCamelCase : dict , __UpperCamelCase : int , __UpperCamelCase : set , __UpperCamelCase : set ):
'''simple docstring'''
visited.add(__lowercase )
rec_stk.add(__lowercase )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(__lowercase , __lowercase , __lowercase , __lowercase ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(__lowercase )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 703 |
"""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 __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Any=0 ):
'''simple docstring'''
if name is None:
snake_case_ : Dict = None
else:
snake_case_ : Dict = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(5_0 - spaces ) + """s}"""
snake_case_ : Any = fmt.format(__UpperCamelCase )
# Print and recurse (if needed).
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if msg is not None:
print(__UpperCamelCase )
for k in val.keys():
recursive_print(__UpperCamelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCamelCase , torch.Tensor ):
print(__UpperCamelCase , """:""" , val.size() )
else:
print(__UpperCamelCase , """:""" , __UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Any = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case_ : List[str] = (num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case_ : Tuple = param.view(*__UpperCamelCase )
snake_case_ : Tuple = param.transpose(0 , 2 )
snake_case_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case_ : Optional[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case_ : str = param.view(*__UpperCamelCase )
snake_case_ : Dict = param.transpose(0 , 1 ).contiguous()
snake_case_ : int = param.view(*__UpperCamelCase )
return param
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Dict = {}
# old versions did not store training args
snake_case_ : List[str] = input_state_dict.get("""args""" , __UpperCamelCase )
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))
snake_case_ : Tuple = ds_args.padded_vocab_size
snake_case_ : Optional[int] = ds_args.max_position_embeddings
snake_case_ : Union[str, Any] = ds_args.hidden_size
snake_case_ : Union[str, Any] = ds_args.num_layers
snake_case_ : str = ds_args.num_attention_heads
snake_case_ : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case_ : Union[str, Any] = config.n_head
# The hidden_size per head.
snake_case_ : Optional[Any] = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case_ : Optional[Any] = input_state_dict["""checkpoint_version"""]
else:
snake_case_ : int = 0.0
# The model.
snake_case_ : List[str] = input_state_dict["""model"""]
# The language model.
snake_case_ : str = model["""language_model"""]
# The embeddings.
snake_case_ : Tuple = lm["""embedding"""]
# The word embeddings.
snake_case_ : List[str] = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
snake_case_ : Optional[int] = word_embeddings[: config.vocab_size, :]
snake_case_ : Optional[int] = word_embeddings
# The position embeddings.
snake_case_ : List[Any] = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
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.
snake_case_ : Union[str, Any] = pos_embeddings
# The transformer.
snake_case_ : Optional[Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
snake_case_ : Optional[Any] = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
snake_case_ : List[str] = {
"""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.
snake_case_ : int = layer_re.match(__UpperCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case_ : Tuple = int(m.group(1 ) )
# The name of the operation.
snake_case_ : Any = m.group(2 )
# Is it a weight or a bias?
snake_case_ : Union[str, Any] = m.group(3 )
# The name of the layer.
snake_case_ : str = F'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
snake_case_ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
snake_case_ : Optional[int] = 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.
snake_case_ : Optional[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCamelCase , __UpperCamelCase )
snake_case_ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case_ : str = torch.tensor(-1E4 , dtype=torch.floataa )
snake_case_ : List[Any] = masked_bias
snake_case_ : Optional[int] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case_ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case_ : Tuple = 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":
snake_case_ : Optional[Any] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Store. No change of shape.
snake_case_ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case_ : Any = megatron_to_transformers[op_name]
snake_case_ : str = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case_ : List[str] = megatron_to_transformers[op_name]
snake_case_ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case_ : Dict = transformer["""final_layernorm.weight"""]
snake_case_ : Dict = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case_ : Optional[int] = word_embeddings
# It should be done!
return output_state_dict
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__UpperCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__UpperCamelCase , help="""An optional config json file describing the pre-trained model.""" , )
snake_case_ : str = parser.parse_args()
# Extract the basename.
snake_case_ : Optional[Any] = 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:
snake_case_ : Optional[int] = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
snake_case_ : List[Any] = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
snake_case_ : Any = input_state_dict.get("""args""" , __UpperCamelCase )
# 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:
snake_case_ : Any = """gelu_fast"""
elif ds_args.openai_gelu:
snake_case_ : Tuple = """gelu_new"""
else:
snake_case_ : List[str] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
snake_case_ : Dict = """gelu_new"""
# Spell out all parameters in case the defaults change.
snake_case_ : List[str] = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=__UpperCamelCase , 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=__UpperCamelCase , summary_activation=__UpperCamelCase , summary_proj_to_labels=__UpperCamelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCamelCase , use_cache=__UpperCamelCase , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
snake_case_ : List[Any] = GPTaConfig.from_json_file(args.config_file )
snake_case_ : int = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
snake_case_ : Tuple = convert_megatron_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCamelCase , __UpperCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case_ : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case_ : Optional[Any] = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
snake_case_ : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
else:
snake_case_ : List[str] = """gpt2"""
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(__UpperCamelCase )
snake_case_ : List[str] = type(__UpperCamelCase ).__name__
snake_case_ : Optional[int] = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__UpperCamelCase )
# Save tokenizer based on args
print(F'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(__UpperCamelCase )
# Store the state_dict to file.
snake_case_ : List[Any] = os.path.join(__UpperCamelCase , """pytorch_model.bin""" )
print(F'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(__UpperCamelCase , __UpperCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 21 | 0 |
import math
def __lowerCAmelCase ( __UpperCamelCase : List[str] = 1_0_0 ):
'''simple docstring'''
snake_case_ : List[Any] = sum(i * i for i in range(1 , n + 1 ) )
snake_case_ : Dict = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 704 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : List[Any] = tf.cast(math.pi , x.dtype )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Tuple = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -1_0 , 1_0 )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=-1 ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
__lowerCAmelCase : int = tf.keras.activations.gelu
__lowerCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
__lowerCAmelCase : List[Any] = _gelu
__lowerCAmelCase : Any = _gelu_new
__lowerCAmelCase : Dict = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 21 | 0 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( __a ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=snake_case__ , scheduler=snake_case__ )
@torch.no_grad()
def __call__( self , _lowercase = 1 , _lowercase = None , _lowercase = 5_0 , _lowercase = "pil" , _lowercase = True , **_lowercase , ) -> int:
'''simple docstring'''
snake_case_ : List[Any] = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=snake_case__ , )
snake_case_ : Tuple = image.to(self.device )
# set step values
self.scheduler.set_timesteps(snake_case__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
snake_case_ : List[str] = self.unet(snake_case__ , snake_case__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
snake_case_ : int = self.scheduler.step(snake_case__ , snake_case__ , snake_case__ ).prev_sample
snake_case_ : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
snake_case_ : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case_ : List[Any] = self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=snake_case__ ), "This is a local test"
| 705 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : int = [0] * len(__UpperCamelCase )
snake_case_ : List[str] = []
snake_case_ : Any = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCamelCase )
while queue:
snake_case_ : Optional[int] = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case_ : Union[str, Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__UpperCamelCase )
print(max(__UpperCamelCase ) )
# Adjacency list of Graph
__lowerCAmelCase : str = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 21 | 0 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = ["a", "b", "c"]
# Defaults to last layer if both are None
snake_case_ : Tuple = get_aligned_output_features_output_indices(a_ , a_ , a_ )
self.assertEqual(a_ , ["""c"""] )
self.assertEqual(a_ , [2] )
# Out indices set to match out features
snake_case_ : Optional[int] = get_aligned_output_features_output_indices(["""a""", """c"""] , a_ , a_ )
self.assertEqual(a_ , ["""a""", """c"""] )
self.assertEqual(a_ , [0, 2] )
# Out features set to match out indices
snake_case_ : Tuple = get_aligned_output_features_output_indices(a_ , [0, 2] , a_ )
self.assertEqual(a_ , ["""a""", """c"""] )
self.assertEqual(a_ , [0, 2] )
# Out features selected from negative indices
snake_case_ : Dict = get_aligned_output_features_output_indices(a_ , [-3, -1] , a_ )
self.assertEqual(a_ , ["""a""", """c"""] )
self.assertEqual(a_ , [-3, -1] )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
with self.assertRaises(a_ ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , a_ )
# Out features must be a list
with self.assertRaises(a_ ):
verify_out_features_out_indices(("""a""", """b""") , (0, 1) , ["""a""", """b"""] )
# Out features must be a subset of stage names
with self.assertRaises(a_ ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , ["""a"""] )
# Out indices must be a list or tuple
with self.assertRaises(a_ ):
verify_out_features_out_indices(a_ , 0 , ["""a""", """b"""] )
# Out indices must be a subset of stage names
with self.assertRaises(a_ ):
verify_out_features_out_indices(a_ , (0, 1) , ["""a"""] )
# Out features and out indices must be the same length
with self.assertRaises(a_ ):
verify_out_features_out_indices(["""a""", """b"""] , (0,) , ["""a""", """b""", """c"""] )
# Out features should match out indices
with self.assertRaises(a_ ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 2) , ["""a""", """b""", """c"""] )
# Out features and out indices should be in order
with self.assertRaises(a_ ):
verify_out_features_out_indices(["""b""", """a"""] , (0, 1) , ["""a""", """b"""] )
# Check passes with valid inputs
verify_out_features_out_indices(["""a""", """b""", """d"""] , (0, 1, -1) , ["""a""", """b""", """c""", """d"""] )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = BackboneMixin()
snake_case_ : List[Any] = ["a", "b", "c"]
snake_case_ : Optional[int] = ["a", "c"]
snake_case_ : Dict = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
snake_case_ : Union[str, Any] = ["a", "b"]
self.assertEqual(backbone.out_features , ["""a""", """b"""] )
self.assertEqual(backbone.out_indices , [0, 1] )
snake_case_ : str = [-3, -1]
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 706 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(__UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return binomial_coefficient(2 * node_count , __UpperCamelCase ) // (node_count + 1)
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
snake_case_ : Optional[int] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return catalan_number(__UpperCamelCase ) * factorial(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 21 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = {
'''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
'''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''xlm-roberta-xl'''
def __init__( self , _lowercase=2_5_0_8_8_0 , _lowercase=2_5_6_0 , _lowercase=3_6 , _lowercase=3_2 , _lowercase=1_0_2_4_0 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_4 , _lowercase=1 , _lowercase=0.02 , _lowercase=1E-05 , _lowercase=1 , _lowercase=0 , _lowercase=2 , _lowercase="absolute" , _lowercase=True , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
snake_case_ : Tuple = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : str = hidden_act
snake_case_ : Dict = intermediate_size
snake_case_ : Any = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : int = max_position_embeddings
snake_case_ : Dict = type_vocab_size
snake_case_ : Optional[int] = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : Union[str, Any] = position_embedding_type
snake_case_ : Union[str, Any] = use_cache
snake_case_ : List[str] = classifier_dropout
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case_ : Dict = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case_ : str = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 707 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''swin'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=2_2_4 , _lowercase=4 , _lowercase=3 , _lowercase=9_6 , _lowercase=[2, 2, 6, 2] , _lowercase=[3, 6, 1_2, 2_4] , _lowercase=7 , _lowercase=4.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=3_2 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : str = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = embed_dim
snake_case_ : Optional[int] = depths
snake_case_ : Optional[int] = len(_lowercase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : Optional[Any] = window_size
snake_case_ : Optional[Any] = mlp_ratio
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = drop_path_rate
snake_case_ : List[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Any = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Tuple = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Tuple = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 21 | 0 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def __lowerCAmelCase ( __UpperCamelCase : Callable , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float ):
'''simple docstring'''
snake_case_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_size ) )
snake_case_ : Union[str, Any] = np.zeros((n + 1,) )
snake_case_ : str = ya
snake_case_ : Any = xa
for k in range(_A ):
snake_case_ : Dict = y[k] + step_size * ode_func(_A , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 708 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
snake_case_ : List[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
snake_case_ : Dict = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
snake_case_ : List[str] = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
snake_case_ : Optional[Any] = shift_tokens_right(_lowercase , model.config.pad_token_id , model.config.decoder_start_token_id )
snake_case_ : Tuple = model(_lowercase , decoder_input_ids=_lowercase ).logits
snake_case_ : Tuple = optax.softmax_cross_entropy(_lowercase , onehot(_lowercase , logits.shape[-1] ) ).mean()
snake_case_ : List[str] = -(labels.shape[-1] * loss.item())
snake_case_ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( _A ):
"""simple docstring"""
_lowerCamelCase = ['''image_processor''', '''tokenizer''']
_lowerCamelCase = '''AutoImageProcessor'''
_lowerCamelCase = '''AutoTokenizer'''
def __init__( self , _lowercase=None , _lowercase=None , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , UpperCamelCase__ , )
snake_case_ : List[Any] = kwargs.pop("""feature_extractor""" )
snake_case_ : int = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
snake_case_ : Optional[Any] = self.image_processor
snake_case_ : Any = False
def __call__( self , *_lowercase , **_lowercase ) -> Any:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*UpperCamelCase__ , **UpperCamelCase__ )
snake_case_ : Dict = kwargs.pop("""images""" , UpperCamelCase__ )
snake_case_ : Any = kwargs.pop("""text""" , UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
snake_case_ : List[Any] = args[0]
snake_case_ : Tuple = args[1:]
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
snake_case_ : Any = self.image_processor(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
if text is not None:
snake_case_ : List[str] = self.tokenizer(UpperCamelCase__ , **UpperCamelCase__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case_ : List[str] = encodings["""input_ids"""]
return inputs
def UpperCAmelCase__ ( self , *_lowercase , **_lowercase ) -> List[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def UpperCAmelCase__ ( self , *_lowercase , **_lowercase ) -> Any:
'''simple docstring'''
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@contextmanager
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your images inputs, or in a separate call.""" )
snake_case_ : Optional[Any] = True
snake_case_ : Optional[Any] = self.tokenizer
yield
snake_case_ : List[Any] = self.image_processor
snake_case_ : Any = False
def UpperCAmelCase__ ( self , _lowercase , _lowercase=False , _lowercase=None ) -> Optional[Any]:
'''simple docstring'''
if added_vocab is None:
snake_case_ : Tuple = self.tokenizer.get_added_vocab()
snake_case_ : Optional[Any] = {}
while tokens:
snake_case_ : Optional[Any] = re.search(R"""<s_(.*?)>""" , UpperCamelCase__ , re.IGNORECASE )
if start_token is None:
break
snake_case_ : Any = start_token.group(1 )
snake_case_ : Optional[Any] = re.search(Rf'</s_{key}>' , UpperCamelCase__ , re.IGNORECASE )
snake_case_ : Optional[int] = start_token.group()
if end_token is None:
snake_case_ : Any = tokens.replace(UpperCamelCase__ , """""" )
else:
snake_case_ : Tuple = end_token.group()
snake_case_ : Dict = re.escape(UpperCamelCase__ )
snake_case_ : int = re.escape(UpperCamelCase__ )
snake_case_ : Any = re.search(f'{start_token_escaped}(.*?){end_token_escaped}' , UpperCamelCase__ , re.IGNORECASE )
if content is not None:
snake_case_ : str = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
snake_case_ : List[str] = self.tokenajson(UpperCamelCase__ , is_inner_value=UpperCamelCase__ , added_vocab=UpperCamelCase__ )
if value:
if len(UpperCamelCase__ ) == 1:
snake_case_ : Optional[Any] = value[0]
snake_case_ : List[Any] = value
else: # leaf nodes
snake_case_ : Dict = []
for leaf in content.split(R"""<sep/>""" ):
snake_case_ : Tuple = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
snake_case_ : List[Any] = leaf[1:-2] # for categorical special tokens
output[key].append(UpperCamelCase__ )
if len(output[key] ) == 1:
snake_case_ : List[Any] = output[key][0]
snake_case_ : Tuple = tokens[tokens.find(UpperCamelCase__ ) + len(UpperCamelCase__ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=UpperCamelCase__ , added_vocab=UpperCamelCase__ )
if len(UpperCamelCase__ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , UpperCamelCase__ , )
return self.image_processor_class
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , UpperCamelCase__ , )
return self.image_processor
| 709 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 21 | 0 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : int = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
snake_case_ : List[Any] = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
snake_case_ : Union[str, Any] = model.state_dict()
def to_tf_var_name(__UpperCamelCase : List[str] ):
for patt, repl in iter(__UpperCamelCase ):
snake_case_ : Optional[Any] = name.replace(__UpperCamelCase , __UpperCamelCase )
return F'bert/{name}'
def create_tf_var(__UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : List[Any] = tf.get_variable(dtype=__UpperCamelCase , shape=tensor.shape , name=__UpperCamelCase , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__UpperCamelCase )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[Any] = to_tf_var_name(__UpperCamelCase )
snake_case_ : List[Any] = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : Any = torch_tensor.T
snake_case_ : int = create_tf_var(tensor=__UpperCamelCase , name=__UpperCamelCase , session=__UpperCamelCase )
tf.keras.backend.set_value(__UpperCamelCase , __UpperCamelCase )
snake_case_ : List[Any] = session.run(__UpperCamelCase )
print(F'Successfully created {tf_name}: {np.allclose(__UpperCamelCase , __UpperCamelCase )}' )
snake_case_ : Optional[int] = tf.train.Saver(tf.trainable_variables() )
saver.save(__UpperCamelCase , os.path.join(__UpperCamelCase , model_name.replace("""-""" , """_""" ) + """.ckpt""" ) )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any]=None ):
'''simple docstring'''
snake_case_ : int = argparse.ArgumentParser()
parser.add_argument("""--model_name""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""model name e.g. bert-base-uncased""" )
parser.add_argument(
"""--cache_dir""" , type=__UpperCamelCase , default=__UpperCamelCase , required=__UpperCamelCase , help="""Directory containing pytorch model""" )
parser.add_argument("""--pytorch_model_path""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""/path/to/<pytorch-model-name>.bin""" )
parser.add_argument("""--tf_cache_dir""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""Directory in which to save tensorflow model""" )
snake_case_ : Tuple = parser.parse_args(__UpperCamelCase )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=__UpperCamelCase , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 710 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCAmelCase : List[str] = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
__lowerCAmelCase : Optional[Any] = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
__lowerCAmelCase : str = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Any ):
'''simple docstring'''
return float((preds == labels).mean() )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : str="binary" ):
'''simple docstring'''
snake_case_ : Optional[Any] = simple_accuracy(__UpperCamelCase , __UpperCamelCase )
snake_case_ : Dict = float(fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average=__UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[Any] = {}
for id_pred, label in zip(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : Optional[int] = F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
snake_case_ : Union[str, Any] = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
snake_case_ : str = [(pred, label)]
snake_case_ , snake_case_ : List[str] = [], []
for question, preds_labels in question_map.items():
snake_case_ , snake_case_ : Optional[Any] = zip(*__UpperCamelCase )
snake_case_ : int = fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average="""macro""" )
fas.append(__UpperCamelCase )
snake_case_ : Dict = int(sum(pred == label for pred, label in preds_labels ) == len(__UpperCamelCase ) )
ems.append(__UpperCamelCase )
snake_case_ : Optional[int] = float(sum(__UpperCamelCase ) / len(__UpperCamelCase ) )
snake_case_ : Any = sum(__UpperCamelCase ) / len(__UpperCamelCase )
snake_case_ : int = float(fa_score(y_true=__UpperCamelCase , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_lowercase , _lowercase )}
elif self.config_name == "cb":
return acc_and_fa(_lowercase , _lowercase , fa_avg="""macro""" )
elif self.config_name == "record":
snake_case_ : Optional[Any] = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
snake_case_ : Dict = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(_lowercase , _lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_lowercase , _lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase : Any = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EncodecConfig''',
],
'''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Tuple = [
'''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EncodecModel''',
'''EncodecPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 711 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Dict = batch_size
snake_case_ : Any = seq_length
snake_case_ : Tuple = is_training
snake_case_ : Dict = use_attention_mask
snake_case_ : int = use_token_type_ids
snake_case_ : List[Any] = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : str = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : int = max_position_embeddings
snake_case_ : Dict = type_vocab_size
snake_case_ : Dict = type_sequence_label_size
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Tuple = num_choices
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : List[str] = None
if self.use_token_type_ids:
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Tuple = RobertaPreLayerNormConfig(
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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : int = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Any = config_and_inputs
snake_case_ : Union[str, Any] = True
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : List[str] = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : str = model(_lowercase )[0]
snake_case_ : int = [1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ) , _lowercase )
# compare the actual values for a slice.
snake_case_ : Tuple = np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
@slow
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Dict = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : Any = model(_lowercase )[0]
# compare the actual values for a slice.
snake_case_ : Optional[Any] = np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
def wrapper(*__UpperCamelCase : Optional[Any] , **__UpperCamelCase : Dict ):
snake_case_ : int = timeit.default_timer()
snake_case_ : List[Any] = func(*lowerCamelCase_ , **lowerCamelCase_ )
snake_case_ : List[Any] = timeit.default_timer() - starttime
return delta
snake_case_ : Dict = func.__name__
return wrapper
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Tuple=1_0_0 , __UpperCamelCase : Optional[Any]=None ):
'''simple docstring'''
snake_case_ : Any = []
snake_case_ : Optional[Any] = seq_shapes or {}
for i in range(lowerCamelCase_ ):
snake_case_ : int = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowerCamelCase_ , _ArrayXD ):
snake_case_ : int = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowerCamelCase_ , datasets.Value ):
if v.dtype == "string":
snake_case_ : Dict = """The small grey turtle was surprisingly fast when challenged."""
else:
snake_case_ : str = np.random.randint(1_0 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowerCamelCase_ , datasets.Sequence ):
while isinstance(lowerCamelCase_ , datasets.Sequence ):
snake_case_ : Optional[Any] = v.feature
snake_case_ : Dict = seq_shapes[k]
snake_case_ : List[str] = np.random.rand(*lowerCamelCase_ ).astype(v.dtype )
snake_case_ : Any = data
dummy_data.append((i, example) )
return dummy_data
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : int=1_0_0 , __UpperCamelCase : Any=None ):
'''simple docstring'''
snake_case_ : List[str] = generate_examples(lowerCamelCase_ , num_examples=lowerCamelCase_ , seq_shapes=lowerCamelCase_ )
with ArrowWriter(features=lowerCamelCase_ , path=lowerCamelCase_ ) as writer:
for key, record in dummy_data:
snake_case_ : Tuple = features.encode_example(lowerCamelCase_ )
writer.write(lowerCamelCase_ )
snake_case_ : Union[str, Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.' )
snake_case_ : Optional[int] = datasets.Dataset.from_file(filename=lowerCamelCase_ , info=datasets.DatasetInfo(features=lowerCamelCase_ ) )
return dataset
| 712 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCAmelCase : Optional[Any] = parser.parse_args()
__lowerCAmelCase : Dict = '''cpu'''
__lowerCAmelCase : Optional[Any] = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__lowerCAmelCase : Tuple = '''path-to-your-trained-model'''
__lowerCAmelCase : List[Any] = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__lowerCAmelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase : List[Any] = pipe.to(device)
# to channels last
__lowerCAmelCase : Optional[Any] = pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase : List[str] = pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase : Optional[Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__lowerCAmelCase : Dict = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__lowerCAmelCase : Tuple = torch.randn(2, 4, 64, 64)
__lowerCAmelCase : Any = torch.rand(1) * 999
__lowerCAmelCase : List[str] = torch.randn(2, 77, 768)
__lowerCAmelCase : Optional[int] = (sample, timestep, encoder_hidden_status)
try:
__lowerCAmelCase : List[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__lowerCAmelCase : Optional[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : Any = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : int = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__lowerCAmelCase : Union[str, Any] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__lowerCAmelCase : List[str] = 666
__lowerCAmelCase : Optional[int] = torch.Generator(device).manual_seed(seed)
__lowerCAmelCase : List[Any] = {'''generator''': generator}
if args.steps is not None:
__lowerCAmelCase : Any = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__lowerCAmelCase : str = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : str = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 713 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = RoFormerTokenizer
_lowerCamelCase = RoFormerTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
def UpperCAmelCase__ ( self , **_lowercase ) -> str:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = """永和服装饰品有限公司,今天天气非常好"""
snake_case_ : int = """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ , snake_case_ : Optional[Any] = self.get_chinese_input_output_texts()
snake_case_ : List[str] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : str = tokens + [tokenizer.unk_token]
snake_case_ : Tuple = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ , snake_case_ : List[Any] = self.get_chinese_input_output_texts()
snake_case_ : Union[str, Any] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : Optional[int] = tokens + [tokenizer.unk_token]
snake_case_ : Union[str, Any] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
__lowerCAmelCase : str = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowerCAmelCase : int = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowerCAmelCase : int = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 714 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
snake_case_ : List[Any] = [
2_0_4_7,
1_3_7_3_6_5_3,
2_5_3_2_6_0_0_1,
3_2_1_5_0_3_1_7_5_1,
2_1_5_2_3_0_2_8_9_8_7_4_7,
3_4_7_4_7_4_9_6_6_0_3_8_3,
3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
1,
3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
1,
1,
3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
]
snake_case_ : Dict = [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
for idx, _p in enumerate(__UpperCamelCase , 1 ):
if n < _p:
# then we have our last prime to check
snake_case_ : Optional[int] = primes[:idx]
break
snake_case_ , snake_case_ : Tuple = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
snake_case_ : List[str] = False
for r in range(__UpperCamelCase ):
snake_case_ : int = pow(__UpperCamelCase , d * 2**r , __UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
snake_case_ : Optional[Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def __lowerCAmelCase ( ):
'''simple docstring'''
assert not miller_rabin(5_6_1 )
assert miller_rabin(5_6_3 )
# 2047
assert not miller_rabin(8_3_8_2_0_1 )
assert miller_rabin(8_3_8_2_0_7 )
# 1_373_653
assert not miller_rabin(1_7_3_1_6_0_0_1 )
assert miller_rabin(1_7_3_1_6_0_1_7 )
# 25_326_001
assert not miller_rabin(3_0_7_8_3_8_6_6_4_1 )
assert miller_rabin(3_0_7_8_3_8_6_6_5_3 )
# 3_215_031_751
assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1 )
assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9 )
# 2_152_302_898_747
assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7 )
assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7 )
# 3_474_749_660_383
assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1 )
assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7 )
# 341_550_071_728_321
assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1 )
assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7 )
assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3 )
assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 21 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=4 , _lowercase="gelu" , _lowercase=0.0 , _lowercase=0.1 , _lowercase=True , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ) -> List[str]:
'''simple docstring'''
snake_case_ : str = parent
snake_case_ : Any = batch_size
snake_case_ : Any = seq_length
snake_case_ : str = is_training
snake_case_ : Any = use_input_mask
snake_case_ : int = use_token_type_ids
snake_case_ : int = use_labels
snake_case_ : str = vocab_size
snake_case_ : List[str] = hidden_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Any = intermediate_multiple_size
snake_case_ : Dict = hidden_act
snake_case_ : Optional[int] = hidden_dropout
snake_case_ : Optional[Any] = attention_dropout
snake_case_ : Tuple = weight_tying
snake_case_ : Tuple = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : str = type_sequence_label_size
snake_case_ : Any = initializer_range
snake_case_ : str = num_labels
snake_case_ : int = num_choices
snake_case_ : Optional[int] = scope
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[Any] = None
if self.use_labels:
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return GPTNeoXJapaneseConfig(
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_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase_ , initializer_range=self.initializer_range , )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ , snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ : Optional[int] = True
return config, input_ids, input_mask, token_labels
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> int:
'''simple docstring'''
snake_case_ : Optional[int] = GPTNeoXJapaneseModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Union[str, Any] = model(lowercase_ , attention_mask=lowercase_ )
snake_case_ : Union[str, Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : int = True
snake_case_ : Tuple = GPTNeoXJapaneseModel(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Union[str, Any] = model(lowercase_ , attention_mask=lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = GPTNeoXJapaneseForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ : Optional[int] = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : Any = True
snake_case_ : Optional[int] = GPTNeoXJapaneseForCausalLM(config=lowercase_ )
model.to(lowercase_ )
model.eval()
# first forward pass
snake_case_ : Dict = model(lowercase_ , attention_mask=lowercase_ , use_cache=lowercase_ )
snake_case_ : Any = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ : Tuple = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case_ : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ : int = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case_ : int = model(lowercase_ , attention_mask=lowercase_ , output_hidden_states=lowercase_ )
snake_case_ : List[str] = output_from_no_past["""hidden_states"""][0]
snake_case_ : Optional[int] = model(
lowercase_ , attention_mask=lowercase_ , past_key_values=lowercase_ , output_hidden_states=lowercase_ , )["""hidden_states"""][0]
# select random slice
snake_case_ : Union[str, Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase_ , lowercase_ , atol=1E-3 ) )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[str] = config_and_inputs
snake_case_ : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
_lowerCamelCase = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
_lowerCamelCase = (
{'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = GPTNeoXJapaneseModelTester(self )
snake_case_ : Dict = ConfigTester(self , config_class=lowercase_ , hidden_size=3_7 )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowercase_ , lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowercase_ , lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
snake_case_ : List[Any] = None
self.model_tester.create_and_check_model_as_decoder(lowercase_ , lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ , snake_case_ , snake_case_ , snake_case_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_ , lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowercase_ )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = """abeja/gpt-neox-japanese-2.7b"""
snake_case_ : Tuple = ["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]
snake_case_ : Any = [
"""データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
"""100年後に必要とされる会社は、「人」が中心の会社です。""",
"""フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
"""国境の長いトンネルを抜けると、そこは雪国だった。""",
"""美味しい日本食といえば、やっぱりお寿司ですよね。""",
]
snake_case_ : int = GPTNeoXJapaneseTokenizer.from_pretrained(lowercase_ )
snake_case_ : Any = GPTNeoXJapaneseForCausalLM.from_pretrained(lowercase_ )
snake_case_ : Any = []
for prompt in prompts:
snake_case_ : int = tokenizer(lowercase_ , return_tensors="""pt""" ).input_ids
snake_case_ : Union[str, Any] = model.generate(lowercase_ , max_length=5_0 )
snake_case_ : str = tokenizer.batch_decode(lowercase_ , skip_special_tokens=lowercase_ )
predicted_outputs += generated_string
self.assertListEqual(lowercase_ , lowercase_ )
| 715 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def is_in_circle(__UpperCamelCase : float , __UpperCamelCase : float ) -> bool:
snake_case_ : Dict = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
snake_case_ : Tuple = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
snake_case_ : Union[str, Any] = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Callable[[float], float] , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 , ):
'''simple docstring'''
return mean(
function_to_integrate(uniform(__UpperCamelCase , __UpperCamelCase ) ) for _ in range(__UpperCamelCase ) ) * (max_value - min_value)
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ):
'''simple docstring'''
def identity_function(__UpperCamelCase : float ) -> float:
return x
snake_case_ : int = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : str = (max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("""******************""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def function_to_integrate(__UpperCamelCase : float ) -> float:
return sqrt(4.0 - x * x )
snake_case_ : List[Any] = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(42)
__lowerCAmelCase : List[str] = '''bert-base-cased'''
__lowerCAmelCase : Optional[Any] = '''fp16'''
__lowerCAmelCase : Optional[Any] = '''bf16'''
__lowerCAmelCase : Any = [FPaa, BFaa]
@require_fsdp
@require_cuda
class _lowerCAmelCase ( __lowerCAmelCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
snake_case_ : Union[str, Any] = dict(
ACCELERATE_USE_FSDP="""true""" , MASTER_ADDR="""localhost""" , MASTER_PORT="""10999""" , RANK="""0""" , LOCAL_RANK="""0""" , WORLD_SIZE="""1""" , )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(lowerCAmelCase_ ):
snake_case_ : Tuple = self.dist_env.copy()
snake_case_ : Any = f'{i + 1}'
snake_case_ : str = strategy
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : Any = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy , ShardingStrategy(i + 1 ) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(lowerCAmelCase_ ):
snake_case_ : List[str] = self.dist_env.copy()
snake_case_ : Optional[int] = prefetch_policy
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : Tuple = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch , BackwardPrefetch(i + 1 ) )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(lowerCAmelCase_ ):
snake_case_ : Optional[Any] = self.dist_env.copy()
snake_case_ : Union[str, Any] = state_dict_type
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : str = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type , StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = AutoModel.from_pretrained(lowerCAmelCase_ )
for policy in FSDP_AUTO_WRAP_POLICY:
snake_case_ : int = self.dist_env.copy()
snake_case_ : Optional[Any] = policy
if policy == "TRANSFORMER_BASED_WRAP":
snake_case_ : Dict = """BertLayer"""
elif policy == "SIZE_BASED_WRAP":
snake_case_ : Tuple = """2000"""
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : Tuple = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(lowerCAmelCase_ )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
snake_case_ : str = self.dist_env.copy()
snake_case_ : Dict = """TRANSFORMER_BASED_WRAP"""
snake_case_ : str = """T5Layer"""
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : List[str] = FullyShardedDataParallelPlugin()
with self.assertRaises(lowerCAmelCase_ ) as cm:
fsdp_plugin.set_auto_wrap_policy(lowerCAmelCase_ )
self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception ) )
snake_case_ : Dict = self.dist_env.copy()
snake_case_ : Dict = """SIZE_BASED_WRAP"""
snake_case_ : Dict = """0"""
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : Union[str, Any] = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(lowerCAmelCase_ )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
snake_case_ : Optional[int] = self.dist_env.copy()
snake_case_ : Any = mp_dtype
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : Optional[int] = Accelerator()
if mp_dtype == "fp16":
snake_case_ : int = torch.floataa
elif mp_dtype == "bf16":
snake_case_ : List[Any] = torch.bfloataa
snake_case_ : Dict = MixedPrecision(param_dtype=lowerCAmelCase_ , reduce_dtype=lowerCAmelCase_ , buffer_dtype=lowerCAmelCase_ )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy , lowerCAmelCase_ )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler , lowerCAmelCase_ ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(lowerCAmelCase_ )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
snake_case_ : Tuple = self.dist_env.copy()
snake_case_ : List[str] = str(lowerCAmelCase_ ).lower()
with mockenv_context(**lowerCAmelCase_ ):
snake_case_ : Optional[Any] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload , CPUOffload(offload_params=lowerCAmelCase_ ) )
@require_fsdp
@require_multi_gpu
@slow
class _lowerCAmelCase ( __lowerCAmelCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
snake_case_ : Any = 0.82
snake_case_ : List[Any] = [
"""fsdp_shard_grad_op_transformer_based_wrap""",
"""fsdp_full_shard_transformer_based_wrap""",
]
snake_case_ : List[Any] = {
"""multi_gpu_fp16""": 3_2_0_0,
"""fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2_0_0_0,
"""fsdp_full_shard_transformer_based_wrap_fp16""": 1_9_0_0,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
snake_case_ : int = 1_6_0
snake_case_ : int = 1_6_0
snake_case_ : List[str] = inspect.getfile(accelerate.test_utils )
snake_case_ : str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps"""] )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = os.path.join(self.test_scripts_folder , """test_performance.py""" )
snake_case_ : List[str] = ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
for config in self.performance_configs:
snake_case_ : Optional[int] = cmd.copy()
for i, strategy in enumerate(lowerCAmelCase_ ):
if strategy.lower() in config:
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
break
if "fp32" in config:
cmd_config.append("""--mixed_precision=no""" )
else:
cmd_config.append("""--mixed_precision=fp16""" )
if "cpu_offload" in config:
cmd_config.append("""--fsdp_offload_params=True""" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(f'--fsdp_auto_wrap_policy={policy}' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer""" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("""--fsdp_min_num_params=2000""" )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
f'--performance_lower_bound={self.performance_lower_bound}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase_ , env=os.environ.copy() )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = os.path.join(self.test_scripts_folder , """test_checkpointing.py""" )
snake_case_ : Optional[int] = [
"""accelerate""",
"""launch""",
"""--num_processes=2""",
"""--num_machines=1""",
"""--machine_rank=0""",
"""--use_fsdp""",
"""--mixed_precision=fp16""",
"""--fsdp_transformer_layer_cls_to_wrap=BertLayer""",
]
for i, strategy in enumerate(lowerCAmelCase_ ):
snake_case_ : List[Any] = cmd.copy()
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
if strategy != "FULL_SHARD":
continue
snake_case_ : int = len(lowerCAmelCase_ )
for state_dict_type in FSDP_STATE_DICT_TYPE:
snake_case_ : Any = cmd_config[:state_dict_config_index]
cmd_config.append(f'--fsdp_state_dict_type={state_dict_type}' )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
"""--partial_train_epoch=1""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase_ , env=os.environ.copy() )
snake_case_ : List[str] = cmd_config[:-1]
snake_case_ : Any = os.path.join(self.tmpdir , """epoch_0""" )
cmd_config.extend(
[
f'--resume_from_checkpoint={resume_from_checkpoint}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase_ , env=os.environ.copy() )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = os.path.join(self.test_scripts_folder , """test_peak_memory_usage.py""" )
snake_case_ : List[Any] = [
"""accelerate""",
"""launch""",
"""--num_processes=2""",
"""--num_machines=1""",
"""--machine_rank=0""",
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
snake_case_ : Tuple = cmd.copy()
if "fp16" in spec:
cmd_config.extend(["""--mixed_precision=fp16"""] )
else:
cmd_config.extend(["""--mixed_precision=no"""] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(["""--use_fsdp"""] )
for i, strategy in enumerate(lowerCAmelCase_ ):
if strategy.lower() in spec:
cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
break
if "cpu_offload" in spec:
cmd_config.append("""--fsdp_offload_params=True""" )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(f'--fsdp_auto_wrap_policy={policy}' )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer""" )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append("""--fsdp_min_num_params=2000""" )
cmd_config.extend(
[
self.test_file_path,
f'--output_dir={self.tmpdir}',
f'--peak_memory_upper_bound={peak_mem_upper_bound}',
f'--n_train={self.n_train}',
f'--n_val={self.n_val}',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowerCAmelCase_ , env=os.environ.copy() )
| 716 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = nn.functional.normalize(__UpperCamelCase )
snake_case_ : Tuple = nn.functional.normalize(__UpperCamelCase )
return torch.mm(__UpperCamelCase , normalized_text_embeds.t() )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = CLIPConfig
_lowerCamelCase = ['''CLIPEncoderLayer''']
def __init__( self , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(_lowercase )
snake_case_ : Tuple = CLIPVisionModel(config.vision_config )
snake_case_ : int = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=_lowercase )
snake_case_ : Optional[Any] = nn.Parameter(torch.ones(1_7 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Dict = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Any = nn.Parameter(torch.ones(1_7 ) , requires_grad=_lowercase )
snake_case_ : List[str] = nn.Parameter(torch.ones(3 ) , requires_grad=_lowercase )
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : int = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : str = self.visual_projection(_lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Dict = cosine_distance(_lowercase , self.special_care_embeds ).cpu().float().numpy()
snake_case_ : List[str] = cosine_distance(_lowercase , self.concept_embeds ).cpu().float().numpy()
snake_case_ : Any = []
snake_case_ : Any = image_embeds.shape[0]
for i in range(_lowercase ):
snake_case_ : List[Any] = {"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : int = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
snake_case_ : List[str] = special_cos_dist[i][concept_idx]
snake_case_ : Union[str, Any] = self.special_care_embeds_weights[concept_idx].item()
snake_case_ : Tuple = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]} )
snake_case_ : Dict = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
snake_case_ : int = cos_dist[i][concept_idx]
snake_case_ : List[Any] = self.concept_embeds_weights[concept_idx].item()
snake_case_ : List[str] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(_lowercase )
result.append(_lowercase )
snake_case_ : Union[str, Any] = [len(res["""bad_concepts"""] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : List[str] = self.visual_projection(_lowercase )
snake_case_ : str = cosine_distance(_lowercase , self.special_care_embeds )
snake_case_ : Optional[int] = cosine_distance(_lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : Tuple = 0.0
snake_case_ : List[Any] = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
snake_case_ : str = torch.any(special_scores > 0 , dim=1 )
snake_case_ : List[str] = special_care * 0.01
snake_case_ : Optional[int] = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
snake_case_ : Optional[Any] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
snake_case_ : str = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 21 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = {
'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
__lowerCAmelCase : Tuple = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int , __UpperCamelCase : Dict , __UpperCamelCase : Dict ):
'''simple docstring'''
for attribute in key.split(""".""" ):
snake_case_ : int = getattr(__UpperCamelCase , __UpperCamelCase )
if weight_type is not None:
snake_case_ : Optional[int] = getattr(__UpperCamelCase , __UpperCamelCase ).shape
else:
snake_case_ : List[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":
snake_case_ : str = value
elif weight_type == "weight_g":
snake_case_ : List[Any] = value
elif weight_type == "weight_v":
snake_case_ : Tuple = value
elif weight_type == "bias":
snake_case_ : List[Any] = value
else:
snake_case_ : Union[str, Any] = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Dict = []
snake_case_ : Optional[Any] = fairseq_model.state_dict()
snake_case_ : Optional[Any] = hf_model.feature_extractor
snake_case_ : Tuple = hf_model.adapter
for name, value in fairseq_dict.items():
snake_case_ : Tuple = False
if "conv_layers" in name:
load_conv_layer(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , hf_model.config.feat_extract_norm == """group""" , )
snake_case_ : Optional[Any] = True
elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""] ):
load_adapter(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : Any = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
snake_case_ : Tuple = True
if "*" in mapped_key:
snake_case_ : Optional[int] = name.split(__UpperCamelCase )[0].split(""".""" )[-2]
snake_case_ : Dict = mapped_key.replace("""*""" , __UpperCamelCase )
if "weight_g" in name:
snake_case_ : Tuple = '''weight_g'''
elif "weight_v" in name:
snake_case_ : Tuple = '''weight_v'''
elif "bias" in name:
snake_case_ : Union[str, Any] = '''bias'''
elif "weight" in name:
snake_case_ : Union[str, Any] = '''weight'''
else:
snake_case_ : List[Any] = None
set_recursively(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
continue
if not is_used:
unused_weights.append(__UpperCamelCase )
logger.warning(F'Unused weights: {unused_weights}' )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = full_name.split("""conv_layers.""" )[-1]
snake_case_ : Tuple = name.split(""".""" )
snake_case_ : List[Any] = int(items[0] )
snake_case_ : int = 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.'
)
snake_case_ : int = 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.'
)
snake_case_ : List[Any] = 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."
)
snake_case_ : List[str] = 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.'
)
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(__UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Any = full_name.split("""adaptor.""" )[-1]
snake_case_ : Union[str, Any] = name.split(""".""" )
if items[1].isdigit():
snake_case_ : Tuple = int(items[1] )
else:
snake_case_ : int = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
snake_case_ : Dict = value
logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
snake_case_ : int = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
snake_case_ : List[str] = value
logger.info(F'Adapter proj layer bias was initialized from {full_name}.' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
snake_case_ : Optional[Any] = value
logger.info(F'Adapter proj layer weight was initialized from {full_name}.' )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
snake_case_ : List[Any] = value
logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
snake_case_ : int = value
logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.' )
else:
unused_weights.append(__UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Optional[Any] = emb.weight.shape
snake_case_ : List[str] = nn.Linear(__UpperCamelCase , __UpperCamelCase , bias=__UpperCamelCase )
snake_case_ : Tuple = emb.weight.data
return lin_layer
@torch.no_grad()
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : List[str] , __UpperCamelCase : str , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Tuple , __UpperCamelCase : int , __UpperCamelCase : Any , __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : List[str] , ):
'''simple docstring'''
snake_case_ : int = WavaVecaConfig.from_pretrained(
__UpperCamelCase , add_adapter=__UpperCamelCase , adapter_stride=__UpperCamelCase , adapter_kernel_size=__UpperCamelCase , use_auth_token=__UpperCamelCase , output_hidden_size=__UpperCamelCase , )
snake_case_ : Union[str, Any] = MBartConfig.from_pretrained(__UpperCamelCase )
# load model
snake_case_ : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
"""config_yaml""": config_yaml_path,
"""data""": """/""".join(dict_path.split("""/""" )[:-1] ),
"""w2v_path""": checkpoint_path,
"""load_pretrained_decoder_from""": None,
} , )
snake_case_ : List[Any] = model[0].eval()
# load feature extractor
snake_case_ : str = WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase , use_auth_token=__UpperCamelCase )
# set weights for wav2vec2 encoder
snake_case_ : Dict = WavaVecaModel(__UpperCamelCase )
recursively_load_weights_wavaveca(model.encoder , __UpperCamelCase )
# load decoder weights
snake_case_ : Optional[int] = MBartForCausalLM(__UpperCamelCase )
snake_case_ : Tuple = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__UpperCamelCase )
logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}' )
logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}' )
snake_case_ : Optional[Any] = SpeechEncoderDecoderModel(encoder=__UpperCamelCase , decoder=__UpperCamelCase )
snake_case_ : Union[str, Any] = False
snake_case_ : Union[str, Any] = MBartaaTokenizer(__UpperCamelCase )
tokenizer.save_pretrained(__UpperCamelCase )
snake_case_ : List[Any] = hf_wavavec.config.to_dict()
snake_case_ : List[Any] = tokenizer.pad_token_id
snake_case_ : Dict = tokenizer.bos_token_id
snake_case_ : List[str] = tokenizer.eos_token_id
snake_case_ : str = '''mbart50'''
snake_case_ : Optional[int] = '''wav2vec2'''
snake_case_ : str = tokenizer.eos_token_id
snake_case_ : Optional[int] = 2_5_0_0_0_4
snake_case_ : Union[str, Any] = tokenizer.eos_token_id
snake_case_ : List[Any] = SpeechEncoderDecoderConfig.from_dict(__UpperCamelCase )
hf_wavavec.save_pretrained(__UpperCamelCase )
feature_extractor.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Tuple = 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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-xls-r-1b''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/mbart-large-50-one-to-many-mmt''',
type=str,
help='''Path to hf decoder checkpoint config''',
)
parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')
__lowerCAmelCase : List[Any] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 717 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : List[str] = []
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
F'stage{idx}.patch_embed.proj.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
F'stage{idx}.patch_embed.proj.bias',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
F'stage{idx}.patch_embed.norm.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
F'stage{idx}.patch_embed.norm.bias',
) )
return embed
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : str = []
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
F'stage{idx}.blocks.{cnt}.attn.proj.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
F'stage{idx}.blocks.{cnt}.attn.proj.bias',
) )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias') )
return attention_weights
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : int = []
token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""") )
return token
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = []
head.append(("""layernorm.weight""", """norm.weight""") )
head.append(("""layernorm.bias""", """norm.bias""") )
head.append(("""classifier.weight""", """head.weight""") )
head.append(("""classifier.bias""", """head.bias""") )
return head
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = """imagenet-1k-id2label.json"""
snake_case_ : Optional[Any] = 1_0_0_0
snake_case_ : Any = """huggingface/label-files"""
snake_case_ : Tuple = num_labels
snake_case_ : Dict = json.load(open(cached_download(hf_hub_url(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
snake_case_ : str = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[str] = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
snake_case_ : Dict = CvtConfig(num_labels=__UpperCamelCase , idalabel=__UpperCamelCase , labelaid=__UpperCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "13":
snake_case_ : Any = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "21":
snake_case_ : Any = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ : Optional[int] = [2, 2, 2_0]
snake_case_ : str = [3, 1_2, 1_6]
snake_case_ : Any = [1_9_2, 7_6_8, 1_0_2_4]
snake_case_ : Union[str, Any] = CvtForImageClassification(__UpperCamelCase )
snake_case_ : str = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
snake_case_ : List[Any] = image_size
snake_case_ : str = torch.load(__UpperCamelCase , map_location=torch.device("""cpu""" ) )
snake_case_ : Any = OrderedDict()
snake_case_ : Tuple = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ : Optional[Any] = list_of_state_dict + cls_token(__UpperCamelCase )
snake_case_ : str = list_of_state_dict + embeddings(__UpperCamelCase )
for cnt in range(config.depth[idx] ):
snake_case_ : List[str] = list_of_state_dict + attention(__UpperCamelCase , __UpperCamelCase )
snake_case_ : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__UpperCamelCase )
for i in range(len(__UpperCamelCase ) ):
snake_case_ : Union[str, Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
image_processor.save_pretrained(__UpperCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowerCAmelCase : Dict = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 21 | 0 |
from __future__ import annotations
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Dict = [True] * limit
snake_case_ : Any = False
snake_case_ : Dict = False
snake_case_ : Optional[int] = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
snake_case_ : List[Any] = i * 2
while index < limit:
snake_case_ : Dict = False
snake_case_ : int = index + i
snake_case_ : Optional[Any] = [2]
for i in range(3 , __UpperCamelCase , 2 ):
if is_prime[i]:
primes.append(__UpperCamelCase )
return primes
def __lowerCAmelCase ( __UpperCamelCase : Tuple = 1_0_0_0_0_0_0 ):
'''simple docstring'''
snake_case_ : str = prime_sieve(__UpperCamelCase )
snake_case_ : Optional[int] = 0
snake_case_ : Optional[Any] = 0
for i in range(len(__UpperCamelCase ) ):
for j in range(i + length , len(__UpperCamelCase ) ):
snake_case_ : Tuple = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
snake_case_ : str = j - i
snake_case_ : Dict = sol
return largest
if __name__ == "__main__":
print(F'''{solution() = }''')
| 718 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MgpstrTokenizer
_lowerCamelCase = False
_lowerCamelCase = {}
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# fmt: off
snake_case_ : Optional[Any] = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
snake_case_ : str = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowercase ) + """\n""" )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = """tester"""
snake_case_ : Tuple = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ : Optional[Any] = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
snake_case_ : Union[str, Any] = tokenizer.encode([special_token] , add_special_tokens=_lowercase )
self.assertEqual(len(_lowercase ) , 1 )
snake_case_ : List[Any] = tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ , snake_case_ : Union[str, Any] = self.get_input_output_texts(_lowercase )
snake_case_ : List[Any] = tokenizer.tokenize(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertNotEqual(len(_lowercase ) , 0 )
snake_case_ : str = tokenizer.decode(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(text_a.replace(""" """ , """""" ) , _lowercase )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=__a , default="""biencoder-nq-dev.json""" , help="""Path to raw DPR training data""" , )
parser.add_argument(
"""--evaluation_set""" , type=__a , help="""where to store parsed evaluation_set file""" , )
parser.add_argument(
"""--gold_data_path""" , type=__a , help="""where to store parsed gold_data_path file""" , )
snake_case_ : Dict = parser.parse_args()
with open(args.src_path , """r""" ) as src_file, open(args.evaluation_set , """w""" ) as eval_file, open(
args.gold_data_path , """w""" ) as gold_file:
snake_case_ : List[Any] = json.load(__a )
for dpr_record in tqdm(__a ):
snake_case_ : List[Any] = dpr_record["""question"""]
snake_case_ : int = [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
eval_file.write(question + """\n""" )
gold_file.write("""\t""".join(__a ) + """\n""" )
if __name__ == "__main__":
main()
| 719 |
"""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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : List[str] = batch_size
snake_case_ : int = seq_length
snake_case_ : List[Any] = is_training
snake_case_ : Optional[int] = use_attention_mask
snake_case_ : Optional[Any] = use_token_type_ids
snake_case_ : Union[str, Any] = use_labels
snake_case_ : str = vocab_size
snake_case_ : List[str] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : List[str] = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : str = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : str = num_choices
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[int] = None
if self.use_token_type_ids:
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = 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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_lowercase )
snake_case_ : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case_ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ : Dict = model(_lowercase )[0]
snake_case_ : Optional[int] = 5_0_0_0_0
snake_case_ : Union[str, Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _lowercase )
snake_case_ : Dict = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : Optional[int] = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2StructConfig""",
"""Pix2StructTextConfig""",
"""Pix2StructVisionConfig""",
],
"""processing_pix2struct""": ["""Pix2StructProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[Any] = ["""Pix2StructImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Dict = [
"""PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Pix2StructPreTrainedModel""",
"""Pix2StructForConditionalGeneration""",
"""Pix2StructVisionModel""",
"""Pix2StructTextModel""",
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__lowerCAmelCase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 720 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : int = 1_0
snake_case_ : Any = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
snake_case_ : Tuple = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(__UpperCamelCase ) ),
} , features=__UpperCamelCase , )
return dataset
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return filename
# FILE_CONTENT + files
__lowerCAmelCase : List[Any] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
snake_case_ : Optional[Any] = FILE_CONTENT
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
import bza
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
snake_case_ : Any = bytes(__UpperCamelCase , """utf-8""" )
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
import gzip
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
snake_case_ : List[Any] = bytes(__UpperCamelCase , """utf-8""" )
with gzip.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
snake_case_ : Optional[Any] = bytes(__UpperCamelCase , """utf-8""" )
with lza.frame.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(__UpperCamelCase , """w""" ) as archive:
archive.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple ):
'''simple docstring'''
import tarfile
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
import lzma
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
snake_case_ : str = bytes(__UpperCamelCase , """utf-8""" )
with lzma.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
import zipfile
snake_case_ : Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
snake_case_ : Tuple = bytes(__UpperCamelCase , """utf-8""" )
with zstd.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
snake_case_ : List[str] = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
__lowerCAmelCase : List[str] = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__lowerCAmelCase : Tuple = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCAmelCase : int = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
__lowerCAmelCase : int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
__lowerCAmelCase : Any = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = datasets.Dataset.from_dict(__UpperCamelCase )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(__UpperCamelCase ) ) as con:
snake_case_ : Tuple = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : Optional[Any] = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : str = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : int ):
'''simple docstring'''
import bza
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(__UpperCamelCase , """rb""" ) as f:
snake_case_ : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(__UpperCamelCase , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
snake_case_ : Any = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(__UpperCamelCase , """wb""" ) as f:
snake_case_ : Optional[int] = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase )
snake_case_ : Optional[int] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCamelCase ) )] for k in DATA[0]} , schema=__UpperCamelCase )
writer.write_table(__UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : Any = {"""data""": DATA}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : List[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
import gzip
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] ):
'''simple docstring'''
import gzip
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : List[str] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : str = ["""0""", """1""", """2""", """3"""]
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : int = ["""0""", """1""", """2""", """3"""]
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : List[Any] = ["""0""", """1""", """2""", """3"""]
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 21 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase=0.0 , _lowercase = None , _lowercase = "geglu" , _lowercase = None , _lowercase = False , _lowercase = False , _lowercase = False , _lowercase = False , _lowercase = True , _lowercase = "layer_norm" , _lowercase = False , ) -> List[str]:
'''simple docstring'''
super().__init__()
snake_case_ : int = only_cross_attention
snake_case_ : Tuple = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
snake_case_ : Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f'`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'
f' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.' )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
snake_case_ : Union[str, Any] = AdaLayerNorm(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.use_ada_layer_norm_zero:
snake_case_ : List[Any] = AdaLayerNormZero(UpperCAmelCase__ , UpperCAmelCase__ )
else:
snake_case_ : Tuple = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
snake_case_ : List[Any] = Attention(
query_dim=UpperCAmelCase__ , heads=UpperCAmelCase__ , dim_head=UpperCAmelCase__ , dropout=UpperCAmelCase__ , bias=UpperCAmelCase__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=UpperCAmelCase__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
snake_case_ : List[Any] = (
AdaLayerNorm(UpperCAmelCase__ , UpperCAmelCase__ )
if self.use_ada_layer_norm
else nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
)
snake_case_ : Union[str, Any] = Attention(
query_dim=UpperCAmelCase__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=UpperCAmelCase__ , dim_head=UpperCAmelCase__ , dropout=UpperCAmelCase__ , bias=UpperCAmelCase__ , upcast_attention=UpperCAmelCase__ , ) # is self-attn if encoder_hidden_states is none
else:
snake_case_ : Union[str, Any] = None
snake_case_ : Optional[Any] = None
# 3. Feed-forward
snake_case_ : Dict = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
snake_case_ : str = FeedForward(UpperCAmelCase__ , dropout=UpperCAmelCase__ , activation_fn=UpperCAmelCase__ , final_dropout=UpperCAmelCase__ )
# let chunk size default to None
snake_case_ : List[str] = None
snake_case_ : List[str] = 0
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[Any] = chunk_size
snake_case_ : Optional[Any] = dim
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , ) -> int:
'''simple docstring'''
if self.use_ada_layer_norm:
snake_case_ : Dict = self.norma(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.use_ada_layer_norm_zero:
snake_case_ : str = self.norma(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , hidden_dtype=hidden_states.dtype )
else:
snake_case_ : Tuple = self.norma(UpperCAmelCase__ )
snake_case_ : List[str] = cross_attention_kwargs if cross_attention_kwargs is not None else {}
snake_case_ : Optional[Any] = self.attna(
UpperCAmelCase__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=UpperCAmelCase__ , **UpperCAmelCase__ , )
if self.use_ada_layer_norm_zero:
snake_case_ : Optional[Any] = gate_msa.unsqueeze(1 ) * attn_output
snake_case_ : Any = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
snake_case_ : str = (
self.norma(UpperCAmelCase__ , UpperCAmelCase__ ) if self.use_ada_layer_norm else self.norma(UpperCAmelCase__ )
)
snake_case_ : int = self.attna(
UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , **UpperCAmelCase__ , )
snake_case_ : Tuple = attn_output + hidden_states
# 3. Feed-forward
snake_case_ : List[str] = self.norma(UpperCAmelCase__ )
if self.use_ada_layer_norm_zero:
snake_case_ : Union[str, Any] = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f'`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.' )
snake_case_ : Any = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
snake_case_ : List[str] = torch.cat(
[self.ff(UpperCAmelCase__ ) for hid_slice in norm_hidden_states.chunk(UpperCAmelCase__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
snake_case_ : str = self.ff(UpperCAmelCase__ )
if self.use_ada_layer_norm_zero:
snake_case_ : int = gate_mlp.unsqueeze(1 ) * ff_output
snake_case_ : Any = ff_output + hidden_states
return hidden_states
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase = None , _lowercase = 4 , _lowercase = 0.0 , _lowercase = "geglu" , _lowercase = False , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
snake_case_ : List[Any] = int(dim * mult )
snake_case_ : Optional[int] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
snake_case_ : List[Any] = GELU(UpperCAmelCase__ , UpperCAmelCase__ )
if activation_fn == "gelu-approximate":
snake_case_ : int = GELU(UpperCAmelCase__ , UpperCAmelCase__ , approximate="""tanh""" )
elif activation_fn == "geglu":
snake_case_ : Optional[int] = GEGLU(UpperCAmelCase__ , UpperCAmelCase__ )
elif activation_fn == "geglu-approximate":
snake_case_ : Tuple = ApproximateGELU(UpperCAmelCase__ , UpperCAmelCase__ )
snake_case_ : Optional[Any] = nn.ModuleList([] )
# project in
self.net.append(UpperCAmelCase__ )
# project dropout
self.net.append(nn.Dropout(UpperCAmelCase__ ) )
# project out
self.net.append(nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( self , _lowercase ) -> int:
'''simple docstring'''
for module in self.net:
snake_case_ : List[Any] = module(UpperCAmelCase__ )
return hidden_states
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase = "none" ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
snake_case_ : str = nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ )
snake_case_ : Union[str, Any] = approximate
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[int]:
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(UpperCAmelCase__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def UpperCAmelCase__ ( self , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = self.proj(UpperCAmelCase__ )
snake_case_ : Optional[Any] = self.gelu(UpperCAmelCase__ )
return hidden_states
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase ) -> int:
'''simple docstring'''
super().__init__()
snake_case_ : List[Any] = nn.Linear(UpperCAmelCase__ , dim_out * 2 )
def UpperCAmelCase__ ( self , _lowercase ) -> Dict:
'''simple docstring'''
if gate.device.type != "mps":
return F.gelu(UpperCAmelCase__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def UpperCAmelCase__ ( self , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.proj(UpperCAmelCase__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(UpperCAmelCase__ )
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
snake_case_ : Tuple = nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase__ ( self , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Any = self.proj(UpperCAmelCase__ )
return x * torch.sigmoid(1.702 * x )
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase ) -> Dict:
'''simple docstring'''
super().__init__()
snake_case_ : Tuple = nn.Embedding(UpperCAmelCase__ , UpperCAmelCase__ )
snake_case_ : Dict = nn.SiLU()
snake_case_ : int = nn.Linear(UpperCAmelCase__ , embedding_dim * 2 )
snake_case_ : List[Any] = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ )
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.linear(self.silu(self.emb(UpperCAmelCase__ ) ) )
snake_case_ : Any = torch.chunk(UpperCAmelCase__ , 2 )
snake_case_ : Tuple = self.norm(UpperCAmelCase__ ) * (1 + scale) + shift
return x
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase ) -> str:
'''simple docstring'''
super().__init__()
snake_case_ : int = CombinedTimestepLabelEmbeddings(UpperCAmelCase__ , UpperCAmelCase__ )
snake_case_ : Any = nn.SiLU()
snake_case_ : Optional[Any] = nn.Linear(UpperCAmelCase__ , 6 * embedding_dim , bias=UpperCAmelCase__ )
snake_case_ : Optional[int] = nn.LayerNorm(UpperCAmelCase__ , elementwise_affine=UpperCAmelCase__ , eps=1E-6 )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase=None ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.linear(self.silu(self.emb(UpperCAmelCase__ , UpperCAmelCase__ , hidden_dtype=UpperCAmelCase__ ) ) )
snake_case_ : int = emb.chunk(6 , dim=1 )
snake_case_ : Dict = self.norm(UpperCAmelCase__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = 1E-5 ) -> Optional[int]:
'''simple docstring'''
super().__init__()
snake_case_ : Tuple = num_groups
snake_case_ : List[Any] = eps
if act_fn is None:
snake_case_ : Tuple = None
else:
snake_case_ : Optional[int] = get_activation(UpperCAmelCase__ )
snake_case_ : Optional[Any] = nn.Linear(UpperCAmelCase__ , out_dim * 2 )
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[Any]:
'''simple docstring'''
if self.act:
snake_case_ : List[Any] = self.act(UpperCAmelCase__ )
snake_case_ : Dict = self.linear(UpperCAmelCase__ )
snake_case_ : int = emb[:, :, None, None]
snake_case_ : List[Any] = emb.chunk(2 , dim=1 )
snake_case_ : str = F.group_norm(UpperCAmelCase__ , self.num_groups , eps=self.eps )
snake_case_ : int = x * (1 + scale) + shift
return x
| 721 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ : List[str] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : Union[str, Any] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : int = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
import random
class _lowerCAmelCase :
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( _lowercase ) -> str:
'''simple docstring'''
snake_case_ : List[str] = [ord(_lowerCamelCase ) for i in text]
snake_case_ : int = []
snake_case_ : int = []
for i in plain:
snake_case_ : Any = random.randint(1 , 3_0_0 )
snake_case_ : str = (i + k) * k
cipher.append(_lowerCamelCase )
key.append(_lowerCamelCase )
return cipher, key
@staticmethod
def UpperCAmelCase__ ( _lowercase , _lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = []
for i in range(len(_lowerCamelCase ) ):
snake_case_ : Union[str, Any] = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(_lowerCamelCase ) )
return "".join(_lowerCamelCase )
if __name__ == "__main__":
__lowerCAmelCase , __lowerCAmelCase : Tuple = Onepad().encrypt('''Hello''')
print(c, k)
print(Onepad().decrypt(c, k))
| 700 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
_lowerCamelCase = ['''pixel_values''']
def __init__( self , _lowercase = True , _lowercase = None , _lowercase = PILImageResampling.BILINEAR , _lowercase = True , _lowercase = None , _lowercase = True , _lowercase = 1 / 2_5_5 , _lowercase = True , _lowercase = None , _lowercase = None , **_lowercase , ) -> None:
'''simple docstring'''
super().__init__(**__lowerCamelCase )
snake_case_ : List[Any] = size if size is not None else {'''shortest_edge''': 2_5_6}
snake_case_ : Any = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
snake_case_ : Optional[Any] = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
snake_case_ : Any = get_size_dict(__lowerCamelCase , param_name="""crop_size""" )
snake_case_ : Dict = do_resize
snake_case_ : str = size
snake_case_ : Tuple = resample
snake_case_ : List[Any] = do_center_crop
snake_case_ : Dict = crop_size
snake_case_ : Optional[int] = do_rescale
snake_case_ : List[str] = rescale_factor
snake_case_ : List[Any] = do_normalize
snake_case_ : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case_ : str = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase = PILImageResampling.BICUBIC , _lowercase = None , **_lowercase , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Dict = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
snake_case_ : str = get_resize_output_image_size(__lowerCamelCase , size=size["""shortest_edge"""] , default_to_square=__lowerCamelCase )
return resize(__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Union[str, Any] = get_size_dict(__lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
return center_crop(__lowerCamelCase , size=(size["""height"""], size["""width"""]) , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase ) -> np.ndarray:
'''simple docstring'''
return rescale(__lowerCamelCase , scale=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase = None , **_lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = ChannelDimension.FIRST , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case_ : Optional[int] = size if size is not None else self.size
snake_case_ : Union[str, Any] = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
snake_case_ : Dict = resample if resample is not None else self.resample
snake_case_ : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case_ : Any = crop_size if crop_size is not None else self.crop_size
snake_case_ : List[str] = get_size_dict(__lowerCamelCase , param_name="""crop_size""" )
snake_case_ : List[str] = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : int = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Optional[Any] = image_mean if image_mean is not None else self.image_mean
snake_case_ : List[Any] = image_std if image_std is not None else self.image_std
snake_case_ : Any = make_list_of_images(__lowerCamelCase )
if not valid_images(__lowerCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
snake_case_ : Union[str, Any] = [to_numpy_array(__lowerCamelCase ) for image in images]
if do_resize:
snake_case_ : str = [self.resize(image=__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase ) for image in images]
if do_center_crop:
snake_case_ : List[Any] = [self.center_crop(image=__lowerCamelCase , size=__lowerCamelCase ) for image in images]
if do_rescale:
snake_case_ : Any = [self.rescale(image=__lowerCamelCase , scale=__lowerCamelCase ) for image in images]
if do_normalize:
snake_case_ : Tuple = [self.normalize(image=__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase ) for image in images]
snake_case_ : int = [to_channel_dimension_format(__lowerCamelCase , __lowerCamelCase ) for image in images]
snake_case_ : int = {'''pixel_values''': images}
return BatchFeature(data=__lowerCamelCase , tensor_type=__lowerCamelCase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(__lowerCamelCase ):
snake_case_ : str = target_sizes.numpy()
snake_case_ : Optional[Any] = []
for idx in range(len(__lowerCamelCase ) ):
snake_case_ : Optional[int] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=__lowerCamelCase )
snake_case_ : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__lowerCamelCase )
else:
snake_case_ : Optional[Any] = logits.argmax(dim=1 )
snake_case_ : int = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 701 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''nat'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=4 , _lowercase=3 , _lowercase=6_4 , _lowercase=[3, 4, 6, 5] , _lowercase=[2, 4, 8, 1_6] , _lowercase=7 , _lowercase=3.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=0.0 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : Any = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = embed_dim
snake_case_ : Tuple = depths
snake_case_ : int = len(_lowercase )
snake_case_ : Optional[int] = num_heads
snake_case_ : List[str] = kernel_size
snake_case_ : str = mlp_ratio
snake_case_ : str = qkv_bias
snake_case_ : str = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Tuple = drop_path_rate
snake_case_ : Dict = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Tuple = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Union[str, Any] = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Union[str, Any] = layer_scale_init_value
snake_case_ : Optional[Any] = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 21 | 0 |
"""simple docstring"""
import os
from collections.abc import Iterator
def __lowerCAmelCase ( __UpperCamelCase : List[str] = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(UpperCamelCase__ ):
snake_case_ : Optional[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(UpperCamelCase__ )[1] in (".py", ".ipynb"):
yield os.path.join(UpperCamelCase__ , UpperCamelCase__ ).lstrip("""./""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(UpperCamelCase__ ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(UpperCamelCase__ )} {new_part.replace("_" , " " ).title()}' )
return new_path
def __lowerCAmelCase ( __UpperCamelCase : List[Any] = "." ):
'''simple docstring'''
snake_case_ : List[Any] = """"""
for filepath in sorted(good_file_paths(UpperCamelCase__ ) ):
snake_case_ , snake_case_ : List[str] = os.path.split(UpperCamelCase__ )
if filepath != old_path:
snake_case_ : Optional[int] = print_path(UpperCamelCase__ , UpperCamelCase__ )
snake_case_ : Union[str, Any] = (filepath.count(os.sep ) + 1) if filepath else 0
snake_case_ : Optional[Any] = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
snake_case_ : Union[str, Any] = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(UpperCamelCase__ )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md('''.''')
| 702 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__lowerCAmelCase : Optional[Any] = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[Any] = VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(_lowercase )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = 1_2
snake_case_ : Tuple = 1_2
snake_case_ : Tuple = {
"""attention_bias""": True,
"""cross_attention_dim""": 3_2,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 3_2,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
snake_case_ : Optional[Any] = TransformeraDModel(**_lowercase )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = """cpu"""
snake_case_ : List[str] = self.dummy_vqvae
snake_case_ : Any = self.dummy_text_encoder
snake_case_ : Tuple = self.dummy_tokenizer
snake_case_ : int = self.dummy_transformer
snake_case_ : int = VQDiffusionScheduler(self.num_embed )
snake_case_ : Dict = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase )
snake_case_ : Optional[Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : int = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : List[Any] = """teddy bear playing in the pool"""
snake_case_ : Dict = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : List[Any] = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Optional[int] = output.images
snake_case_ : List[Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Dict = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : List[Any] = image[0, -3:, -3:, -1]
snake_case_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : Dict = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
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 UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : int = """cpu"""
snake_case_ : List[Any] = self.dummy_vqvae
snake_case_ : Optional[int] = self.dummy_text_encoder
snake_case_ : List[Any] = self.dummy_tokenizer
snake_case_ : Union[str, Any] = self.dummy_transformer
snake_case_ : str = VQDiffusionScheduler(self.num_embed )
snake_case_ : List[Any] = LearnedClassifierFreeSamplingEmbeddings(
learnable=_lowercase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ : Union[str, Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : Any = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : Tuple = """teddy bear playing in the pool"""
snake_case_ : str = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Tuple = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Dict = output.images
snake_case_ : Union[str, Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Any = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case_ : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : int = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
snake_case_ : str = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
snake_case_ : Optional[Any] = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ : Any = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Optional[int] = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=_lowercase , output_type="""np""" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 21 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : List[Any] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCAmelCase : Tuple = {
'''tokenizer_file''': {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
},
}
__lowerCAmelCase : int = {
'''gpt-neox-20b''': 2048,
}
class _lowerCAmelCase ( snake_case__ ):
"""simple docstring"""
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase="<|endoftext|>" , _lowercase="<|endoftext|>" , _lowercase="<|endoftext|>" , _lowercase=False , **_lowercase , ) -> Dict:
'''simple docstring'''
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , add_prefix_space=lowercase_ , **lowercase_ , )
snake_case_ : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , lowercase_ ) != add_prefix_space:
snake_case_ : List[Any] = getattr(lowercase_ , pre_tok_state.pop("""type""" ) )
snake_case_ : Optional[int] = add_prefix_space
snake_case_ : Union[str, Any] = pre_tok_class(**lowercase_ )
snake_case_ : Optional[Any] = add_prefix_space
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
def UpperCAmelCase__ ( self , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase_ , add_special_tokens=lowercase_ ) + [self.eos_token_id] )
if len(lowercase_ ) > self.model_max_length:
snake_case_ : List[Any] = input_ids[-self.model_max_length :]
return input_ids
| 703 |
"""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 __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Any=0 ):
'''simple docstring'''
if name is None:
snake_case_ : Dict = None
else:
snake_case_ : Dict = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(5_0 - spaces ) + """s}"""
snake_case_ : Any = fmt.format(__UpperCamelCase )
# Print and recurse (if needed).
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if msg is not None:
print(__UpperCamelCase )
for k in val.keys():
recursive_print(__UpperCamelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCamelCase , torch.Tensor ):
print(__UpperCamelCase , """:""" , val.size() )
else:
print(__UpperCamelCase , """:""" , __UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Any = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case_ : List[str] = (num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case_ : Tuple = param.view(*__UpperCamelCase )
snake_case_ : Tuple = param.transpose(0 , 2 )
snake_case_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case_ : Optional[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case_ : str = param.view(*__UpperCamelCase )
snake_case_ : Dict = param.transpose(0 , 1 ).contiguous()
snake_case_ : int = param.view(*__UpperCamelCase )
return param
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Dict = {}
# old versions did not store training args
snake_case_ : List[str] = input_state_dict.get("""args""" , __UpperCamelCase )
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))
snake_case_ : Tuple = ds_args.padded_vocab_size
snake_case_ : Optional[int] = ds_args.max_position_embeddings
snake_case_ : Union[str, Any] = ds_args.hidden_size
snake_case_ : Union[str, Any] = ds_args.num_layers
snake_case_ : str = ds_args.num_attention_heads
snake_case_ : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case_ : Union[str, Any] = config.n_head
# The hidden_size per head.
snake_case_ : Optional[Any] = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case_ : Optional[Any] = input_state_dict["""checkpoint_version"""]
else:
snake_case_ : int = 0.0
# The model.
snake_case_ : List[str] = input_state_dict["""model"""]
# The language model.
snake_case_ : str = model["""language_model"""]
# The embeddings.
snake_case_ : Tuple = lm["""embedding"""]
# The word embeddings.
snake_case_ : List[str] = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
snake_case_ : Optional[int] = word_embeddings[: config.vocab_size, :]
snake_case_ : Optional[int] = word_embeddings
# The position embeddings.
snake_case_ : List[Any] = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
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.
snake_case_ : Union[str, Any] = pos_embeddings
# The transformer.
snake_case_ : Optional[Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
snake_case_ : Optional[Any] = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
snake_case_ : List[str] = {
"""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.
snake_case_ : int = layer_re.match(__UpperCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case_ : Tuple = int(m.group(1 ) )
# The name of the operation.
snake_case_ : Any = m.group(2 )
# Is it a weight or a bias?
snake_case_ : Union[str, Any] = m.group(3 )
# The name of the layer.
snake_case_ : str = F'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
snake_case_ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
snake_case_ : Optional[int] = 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.
snake_case_ : Optional[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCamelCase , __UpperCamelCase )
snake_case_ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case_ : str = torch.tensor(-1E4 , dtype=torch.floataa )
snake_case_ : List[Any] = masked_bias
snake_case_ : Optional[int] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case_ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case_ : Tuple = 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":
snake_case_ : Optional[Any] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Store. No change of shape.
snake_case_ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case_ : Any = megatron_to_transformers[op_name]
snake_case_ : str = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case_ : List[str] = megatron_to_transformers[op_name]
snake_case_ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case_ : Dict = transformer["""final_layernorm.weight"""]
snake_case_ : Dict = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case_ : Optional[int] = word_embeddings
# It should be done!
return output_state_dict
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__UpperCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__UpperCamelCase , help="""An optional config json file describing the pre-trained model.""" , )
snake_case_ : str = parser.parse_args()
# Extract the basename.
snake_case_ : Optional[Any] = 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:
snake_case_ : Optional[int] = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
snake_case_ : List[Any] = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
snake_case_ : Any = input_state_dict.get("""args""" , __UpperCamelCase )
# 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:
snake_case_ : Any = """gelu_fast"""
elif ds_args.openai_gelu:
snake_case_ : Tuple = """gelu_new"""
else:
snake_case_ : List[str] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
snake_case_ : Dict = """gelu_new"""
# Spell out all parameters in case the defaults change.
snake_case_ : List[str] = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=__UpperCamelCase , 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=__UpperCamelCase , summary_activation=__UpperCamelCase , summary_proj_to_labels=__UpperCamelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCamelCase , use_cache=__UpperCamelCase , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
snake_case_ : List[Any] = GPTaConfig.from_json_file(args.config_file )
snake_case_ : int = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
snake_case_ : Tuple = convert_megatron_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCamelCase , __UpperCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case_ : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case_ : Optional[Any] = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
snake_case_ : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
else:
snake_case_ : List[str] = """gpt2"""
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(__UpperCamelCase )
snake_case_ : List[str] = type(__UpperCamelCase ).__name__
snake_case_ : Optional[int] = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__UpperCamelCase )
# Save tokenizer based on args
print(F'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(__UpperCamelCase )
# Store the state_dict to file.
snake_case_ : List[Any] = os.path.join(__UpperCamelCase , """pytorch_model.bin""" )
print(F'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(__UpperCamelCase , __UpperCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 21 | 0 |
from __future__ import annotations
from typing import Any
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase = 6 ) -> None:
'''simple docstring'''
snake_case_ : Union[str, Any] = None
snake_case_ : Optional[Any] = None
self.create_linked_list(A_ )
def UpperCAmelCase__ ( self , _lowercase ) -> None:
'''simple docstring'''
snake_case_ : int = Node()
snake_case_ : List[str] = current_node
snake_case_ : List[str] = current_node
snake_case_ : Union[str, Any] = current_node
for _ in range(1 , A_ ):
snake_case_ : Dict = Node()
snake_case_ : Any = current_node
snake_case_ : Dict = previous_node
snake_case_ : str = current_node
snake_case_ : str = self.front
snake_case_ : str = previous_node
def UpperCAmelCase__ ( self ) -> bool:
'''simple docstring'''
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def UpperCAmelCase__ ( self ) -> Any | None:
'''simple docstring'''
self.check_can_perform_operation()
return self.front.data if self.front else None
def UpperCAmelCase__ ( self , _lowercase ) -> None:
'''simple docstring'''
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
snake_case_ : int = self.rear.next
if self.rear:
snake_case_ : Optional[int] = data
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
snake_case_ : str = self.front.data
snake_case_ : Union[str, Any] = None
return data
snake_case_ : Any = self.front
snake_case_ : Optional[int] = old_front.next
snake_case_ : List[str] = old_front.data
snake_case_ : Tuple = None
return data
def UpperCAmelCase__ ( self ) -> None:
'''simple docstring'''
if self.is_empty():
raise Exception("""Empty Queue""" )
def UpperCAmelCase__ ( self ) -> None:
'''simple docstring'''
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> None:
'''simple docstring'''
snake_case_ : str = None
snake_case_ : Tuple = None
snake_case_ : str = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : List[Any] = tf.cast(math.pi , x.dtype )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Tuple = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -1_0 , 1_0 )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=-1 ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
__lowerCAmelCase : int = tf.keras.activations.gelu
__lowerCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
__lowerCAmelCase : List[Any] = _gelu
__lowerCAmelCase : Any = _gelu_new
__lowerCAmelCase : Dict = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 21 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class _lowerCAmelCase ( __lowercase ):
"""simple docstring"""
_lowerCamelCase = '''decision_transformer'''
_lowerCamelCase = ['''past_key_values''']
_lowerCamelCase = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=1_7 , _lowercase=4 , _lowercase=1_2_8 , _lowercase=4_0_9_6 , _lowercase=True , _lowercase=1 , _lowercase=1_0_2_4 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1E-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=5_0_2_5_6 , _lowercase=5_0_2_5_6 , _lowercase=False , _lowercase=False , **_lowercase , ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = state_dim
snake_case_ : str = act_dim
snake_case_ : str = hidden_size
snake_case_ : List[str] = max_ep_len
snake_case_ : List[str] = action_tanh
snake_case_ : Optional[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : List[str] = n_layer
snake_case_ : Tuple = n_head
snake_case_ : int = n_inner
snake_case_ : Any = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[int] = embd_pdrop
snake_case_ : Optional[int] = attn_pdrop
snake_case_ : Tuple = layer_norm_epsilon
snake_case_ : int = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : str = use_cache
snake_case_ : int = scale_attn_by_inverse_layer_idx
snake_case_ : Any = reorder_and_upcast_attn
snake_case_ : Optional[Any] = bos_token_id
snake_case_ : Union[str, Any] = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 705 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : int = [0] * len(__UpperCamelCase )
snake_case_ : List[str] = []
snake_case_ : Any = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCamelCase )
while queue:
snake_case_ : Optional[int] = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case_ : Union[str, Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__UpperCamelCase )
print(max(__UpperCamelCase ) )
# Adjacency list of Graph
__lowerCAmelCase : str = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 21 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''realm'''
def __init__( self , _lowercase=3_0_5_2_2 , _lowercase=7_6_8 , _lowercase=1_2_8 , _lowercase=1_2 , _lowercase=1_2 , _lowercase=8 , _lowercase=3_0_7_2 , _lowercase="gelu_new" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=2 , _lowercase=0.02 , _lowercase=1E-12 , _lowercase=2_5_6 , _lowercase=1_0 , _lowercase=1E-3 , _lowercase=5 , _lowercase=3_2_0 , _lowercase=1_3_3_5_3_7_1_8 , _lowercase=5_0_0_0 , _lowercase=1 , _lowercase=0 , _lowercase=2 , **_lowercase , ) -> List[Any]:
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
# Common config
snake_case_ : List[str] = vocab_size
snake_case_ : Tuple = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : int = retriever_proj_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : List[Any] = num_candidates
snake_case_ : Dict = intermediate_size
snake_case_ : int = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Tuple = initializer_range
snake_case_ : Any = type_vocab_size
snake_case_ : str = layer_norm_eps
# Reader config
snake_case_ : Tuple = span_hidden_size
snake_case_ : Optional[int] = max_span_width
snake_case_ : Dict = reader_layer_norm_eps
snake_case_ : Any = reader_beam_size
snake_case_ : List[str] = reader_seq_len
# Retrieval config
snake_case_ : Optional[Any] = num_block_records
snake_case_ : str = searcher_beam_size
| 706 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(__UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return binomial_coefficient(2 * node_count , __UpperCamelCase ) // (node_count + 1)
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
snake_case_ : Optional[int] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return catalan_number(__UpperCamelCase ) * factorial(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 21 | 0 |
"""simple docstring"""
import enum
import shutil
import sys
__lowerCAmelCase , __lowerCAmelCase : List[Any] = shutil.get_terminal_size()
__lowerCAmelCase : Any = {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}
class _lowerCAmelCase ( enum.Enum ):
"""simple docstring"""
_lowerCamelCase = 0
_lowerCamelCase = 1
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Any="" ):
'''simple docstring'''
sys.stdout.write(str(__UpperCamelCase ) + end )
sys.stdout.flush()
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Dict , __UpperCamelCase : str="" ):
'''simple docstring'''
forceWrite(F'\u001b[{color}m{content}\u001b[0m' , __UpperCamelCase )
def __lowerCAmelCase ( ):
'''simple docstring'''
forceWrite("""\r""" )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : str ):
'''simple docstring'''
forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}' )
def __lowerCAmelCase ( ):
'''simple docstring'''
forceWrite(""" """ * TERMINAL_WIDTH )
reset_cursor()
def __lowerCAmelCase ( ):
'''simple docstring'''
reset_cursor()
forceWrite("""-""" * TERMINAL_WIDTH )
| 707 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''swin'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=2_2_4 , _lowercase=4 , _lowercase=3 , _lowercase=9_6 , _lowercase=[2, 2, 6, 2] , _lowercase=[3, 6, 1_2, 2_4] , _lowercase=7 , _lowercase=4.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=3_2 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : str = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = embed_dim
snake_case_ : Optional[int] = depths
snake_case_ : Optional[int] = len(_lowercase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : Optional[Any] = window_size
snake_case_ : Optional[Any] = mlp_ratio
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = drop_path_rate
snake_case_ : List[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Any = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Tuple = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Tuple = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
snake_case_ : int = 1
snake_case_ : Optional[int] = 1
snake_case_ : Optional[Any] = {1: 1}
for inputa in range(2 , lowerCAmelCase__ ):
snake_case_ : Dict = 0
snake_case_ : Any = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
snake_case_ : Any = (3 * number) + 1
counter += 1
if inputa not in counters:
snake_case_ : Optional[int] = counter
if counter > pre_counter:
snake_case_ : List[str] = inputa
snake_case_ : Union[str, Any] = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 708 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
snake_case_ : List[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
snake_case_ : Dict = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
snake_case_ : List[str] = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
snake_case_ : Optional[Any] = shift_tokens_right(_lowercase , model.config.pad_token_id , model.config.decoder_start_token_id )
snake_case_ : Tuple = model(_lowercase , decoder_input_ids=_lowercase ).logits
snake_case_ : Tuple = optax.softmax_cross_entropy(_lowercase , onehot(_lowercase , logits.shape[-1] ) ).mean()
snake_case_ : List[str] = -(labels.shape[-1] * loss.item())
snake_case_ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Dict = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Dict = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
snake_case_ : Tuple = 1_2_8
elif "12-12" in model_name:
snake_case_ : Optional[Any] = 1_2
snake_case_ : int = 1_2
elif "14-14" in model_name:
snake_case_ : List[Any] = 1_4
snake_case_ : Any = 1_4
elif "16-16" in model_name:
snake_case_ : Optional[Any] = 1_6
snake_case_ : Optional[Any] = 1_6
else:
raise ValueError("""Model not supported""" )
snake_case_ : Any = """huggingface/label-files"""
if "speech-commands" in model_name:
snake_case_ : List[Any] = 3_5
snake_case_ : Any = """speech-commands-v2-id2label.json"""
else:
snake_case_ : Optional[int] = 5_2_7
snake_case_ : str = """audioset-id2label.json"""
snake_case_ : Dict = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
snake_case_ : Union[str, Any] = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : int = idalabel
snake_case_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if "module.v" in name:
snake_case_ : Union[str, Any] = name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
snake_case_ : Tuple = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
snake_case_ : Tuple = name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
snake_case_ : Tuple = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
snake_case_ : Any = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
snake_case_ : str = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
snake_case_ : int = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
snake_case_ : Union[str, Any] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
snake_case_ : Union[str, Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
snake_case_ : List[str] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
snake_case_ : str = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
snake_case_ : List[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
snake_case_ : List[str] = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
snake_case_ : List[str] = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
snake_case_ : Tuple = name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
snake_case_ : Optional[int] = orig_state_dict.pop(__UpperCamelCase )
if "qkv" in key:
snake_case_ : str = key.split(""".""" )
snake_case_ : Optional[int] = int(key_split[3] )
snake_case_ : List[Any] = config.hidden_size
if "weight" in key:
snake_case_ : Dict = val[:dim, :]
snake_case_ : str = val[dim : dim * 2, :]
snake_case_ : Dict = val[-dim:, :]
else:
snake_case_ : Optional[int] = val[:dim]
snake_case_ : Union[str, Any] = val[dim : dim * 2]
snake_case_ : Tuple = val[-dim:]
else:
snake_case_ : Union[str, Any] = val
return orig_state_dict
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : int = [
"""module.v.head.weight""",
"""module.v.head.bias""",
"""module.v.head_dist.weight""",
"""module.v.head_dist.bias""",
]
for k in ignore_keys:
state_dict.pop(__UpperCamelCase , __UpperCamelCase )
@torch.no_grad()
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Union[str, Any]=False ):
'''simple docstring'''
snake_case_ : Any = get_audio_spectrogram_transformer_config(__UpperCamelCase )
snake_case_ : Optional[int] = {
"""ast-finetuned-audioset-10-10-0.4593""": (
"""https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.450""": (
"""https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448""": (
"""https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448-v2""": (
"""https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
),
"""ast-finetuned-audioset-12-12-0.447""": (
"""https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
),
"""ast-finetuned-audioset-14-14-0.443""": (
"""https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
),
"""ast-finetuned-audioset-16-16-0.442""": (
"""https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
),
"""ast-finetuned-speech-commands-v2""": (
"""https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
),
}
# load original state_dict
snake_case_ : Any = model_name_to_url[model_name]
snake_case_ : str = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location="""cpu""" )
# remove some keys
remove_keys(__UpperCamelCase )
# rename some keys
snake_case_ : Tuple = convert_state_dict(__UpperCamelCase , __UpperCamelCase )
# load 🤗 model
snake_case_ : List[str] = ASTForAudioClassification(__UpperCamelCase )
model.eval()
model.load_state_dict(__UpperCamelCase )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
snake_case_ : List[str] = -4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
snake_case_ : Optional[Any] = 4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
snake_case_ : List[Any] = 1_0_2_4 if """speech-commands""" not in model_name else 1_2_8
snake_case_ : Dict = ASTFeatureExtractor(mean=__UpperCamelCase , std=__UpperCamelCase , max_length=__UpperCamelCase )
if "speech-commands" in model_name:
snake_case_ : List[Any] = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
snake_case_ : Dict = dataset[0]["""audio"""]["""array"""]
else:
snake_case_ : List[str] = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
snake_case_ , snake_case_ : Union[str, Any] = torchaudio.load(__UpperCamelCase )
snake_case_ : Union[str, Any] = waveform.squeeze().numpy()
snake_case_ : Optional[int] = feature_extractor(__UpperCamelCase , sampling_rate=1_6_0_0_0 , return_tensors="""pt""" )
# forward pass
snake_case_ : List[str] = model(**__UpperCamelCase )
snake_case_ : Tuple = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
snake_case_ : Dict = torch.tensor([-0.8_760, -7.0_042, -8.6_602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
snake_case_ : List[Any] = torch.tensor([-1.1_986, -7.0_903, -8.2_718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
snake_case_ : List[str] = torch.tensor([-2.6_128, -8.0_080, -9.4_344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
snake_case_ : Dict = torch.tensor([-1.5_080, -7.4_534, -8.8_917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
snake_case_ : Tuple = torch.tensor([-0.5_050, -6.5_833, -8.0_843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
snake_case_ : List[str] = torch.tensor([-0.3_826, -7.0_336, -8.2_413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
snake_case_ : Tuple = torch.tensor([-1.2_113, -6.9_101, -8.3_470] )
elif model_name == "ast-finetuned-speech-commands-v2":
snake_case_ : Union[str, Any] = torch.tensor([6.1_589, -8.0_566, -8.7_984] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , __UpperCamelCase , atol=1E-4 ):
raise ValueError("""Logits don\'t match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__UpperCamelCase )
print(F'Saving feature extractor to {pytorch_dump_folder_path}' )
feature_extractor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(F'MIT/{model_name}' )
feature_extractor.push_to_hub(F'MIT/{model_name}' )
if __name__ == "__main__":
__lowerCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''ast-finetuned-audioset-10-10-0.4593''',
type=str,
help='''Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.'''
)
__lowerCAmelCase : Tuple = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 709 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 21 | 0 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
__lowerCAmelCase : Dict = {
'''b0''': {
'''hidden_dim''': 1280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Optional[Any] = EfficientNetConfig()
snake_case_ : Dict = CONFIG_MAP[model_name]["""hidden_dim"""]
snake_case_ : Optional[Any] = CONFIG_MAP[model_name]["""width_coef"""]
snake_case_ : Tuple = CONFIG_MAP[model_name]["""depth_coef"""]
snake_case_ : Any = CONFIG_MAP[model_name]["""image_size"""]
snake_case_ : Optional[int] = CONFIG_MAP[model_name]["""dropout_rate"""]
snake_case_ : int = CONFIG_MAP[model_name]["""dw_padding"""]
snake_case_ : Optional[Any] = """huggingface/label-files"""
snake_case_ : Tuple = """imagenet-1k-id2label.json"""
snake_case_ : str = 1_0_0_0
snake_case_ : Any = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
snake_case_ : List[str] = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[Any] = idalabel
snake_case_ : Tuple = {v: k for k, v in idalabel.items()}
return config
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Any = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case_ : str = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[str] = CONFIG_MAP[model_name]["""image_size"""]
snake_case_ : Optional[Any] = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] , do_center_crop=__UpperCamelCase , )
return preprocessor
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[str] = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
snake_case_ : Union[str, Any] = sorted(set(__UpperCamelCase ) )
snake_case_ : Optional[Any] = len(__UpperCamelCase )
snake_case_ : int = {b: str(__UpperCamelCase ) for b, i in zip(__UpperCamelCase , range(__UpperCamelCase ) )}
snake_case_ : Optional[int] = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
snake_case_ : str = block_name_mapping[b]
rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
rename_keys.append(
(F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
rename_keys.append(
(F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
rename_keys.append(
(F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
rename_keys.append(
(F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
rename_keys.append(
(F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )
rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
rename_keys.append(
(F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight') )
rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight') )
rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias') )
rename_keys.append(
(F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
rename_keys.append(
(F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
snake_case_ : List[Any] = {}
for item in rename_keys:
if item[0] in original_param_names:
snake_case_ : str = """efficientnet.""" + item[1]
snake_case_ : str = """classifier.weight"""
snake_case_ : List[Any] = """classifier.bias"""
return key_mapping
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : Dict ):
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
snake_case_ : Union[str, Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
snake_case_ : Dict = torch.from_numpy(__UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
snake_case_ : Optional[int] = torch.from_numpy(__UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
snake_case_ : int = torch.from_numpy(np.transpose(__UpperCamelCase ) )
else:
snake_case_ : str = torch.from_numpy(__UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(__UpperCamelCase )
@torch.no_grad()
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : List[Any] = model_classes[model_name](
include_top=__UpperCamelCase , weights="""imagenet""" , input_tensor=__UpperCamelCase , input_shape=__UpperCamelCase , pooling=__UpperCamelCase , classes=1_0_0_0 , classifier_activation="""softmax""" , )
snake_case_ : str = original_model.trainable_variables
snake_case_ : Union[str, Any] = original_model.non_trainable_variables
snake_case_ : Any = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
snake_case_ : Dict = param.numpy()
snake_case_ : Dict = list(tf_params.keys() )
# Load HuggingFace model
snake_case_ : List[Any] = get_efficientnet_config(__UpperCamelCase )
snake_case_ : int = EfficientNetForImageClassification(__UpperCamelCase ).eval()
snake_case_ : Any = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
snake_case_ : Optional[int] = rename_keys(__UpperCamelCase )
replace_params(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Initialize preprocessor and preprocess input image
snake_case_ : Optional[int] = convert_image_processor(__UpperCamelCase )
snake_case_ : Any = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
snake_case_ : int = hf_model(**__UpperCamelCase )
snake_case_ : int = outputs.logits.detach().numpy()
# Original model inference
snake_case_ : Union[str, Any] = False
snake_case_ : Optional[int] = CONFIG_MAP[model_name]["""image_size"""]
snake_case_ : List[Any] = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
snake_case_ : str = image.img_to_array(__UpperCamelCase )
snake_case_ : Union[str, Any] = np.expand_dims(__UpperCamelCase , axis=0 )
snake_case_ : List[Any] = original_model.predict(__UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(__UpperCamelCase ):
os.mkdir(__UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(__UpperCamelCase )
preprocessor.save_pretrained(__UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(F'Pushing converted {model_name} to the hub...' )
snake_case_ : Any = F'efficientnet-{model_name}'
preprocessor.push_to_hub(__UpperCamelCase )
hf_model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 710 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCAmelCase : List[str] = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
__lowerCAmelCase : Optional[Any] = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
__lowerCAmelCase : str = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Any ):
'''simple docstring'''
return float((preds == labels).mean() )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : str="binary" ):
'''simple docstring'''
snake_case_ : Optional[Any] = simple_accuracy(__UpperCamelCase , __UpperCamelCase )
snake_case_ : Dict = float(fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average=__UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[Any] = {}
for id_pred, label in zip(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : Optional[int] = F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
snake_case_ : Union[str, Any] = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
snake_case_ : str = [(pred, label)]
snake_case_ , snake_case_ : List[str] = [], []
for question, preds_labels in question_map.items():
snake_case_ , snake_case_ : Optional[Any] = zip(*__UpperCamelCase )
snake_case_ : int = fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average="""macro""" )
fas.append(__UpperCamelCase )
snake_case_ : Dict = int(sum(pred == label for pred, label in preds_labels ) == len(__UpperCamelCase ) )
ems.append(__UpperCamelCase )
snake_case_ : Optional[int] = float(sum(__UpperCamelCase ) / len(__UpperCamelCase ) )
snake_case_ : Any = sum(__UpperCamelCase ) / len(__UpperCamelCase )
snake_case_ : int = float(fa_score(y_true=__UpperCamelCase , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_lowercase , _lowercase )}
elif self.config_name == "cb":
return acc_and_fa(_lowercase , _lowercase , fa_avg="""macro""" )
elif self.config_name == "record":
snake_case_ : Optional[Any] = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
snake_case_ : Dict = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(_lowercase , _lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_lowercase , _lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 21 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class _lowerCAmelCase ( __UpperCAmelCase ):
"""simple docstring"""
_lowerCamelCase = '''mgp-str'''
def __init__( self , _lowercase=[3_2, 1_2_8] , _lowercase=4 , _lowercase=3 , _lowercase=2_7 , _lowercase=3_8 , _lowercase=5_0_2_5_7 , _lowercase=3_0_5_2_2 , _lowercase=7_6_8 , _lowercase=1_2 , _lowercase=1_2 , _lowercase=4.0 , _lowercase=True , _lowercase=False , _lowercase=1E-5 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=False , _lowercase=0.02 , **_lowercase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**lowerCAmelCase_ )
snake_case_ : Union[str, Any] = image_size
snake_case_ : str = patch_size
snake_case_ : Union[str, Any] = num_channels
snake_case_ : int = max_token_length
snake_case_ : List[str] = num_character_labels
snake_case_ : List[Any] = num_bpe_labels
snake_case_ : Optional[Any] = num_wordpiece_labels
snake_case_ : int = hidden_size
snake_case_ : int = num_hidden_layers
snake_case_ : Optional[int] = num_attention_heads
snake_case_ : Optional[int] = mlp_ratio
snake_case_ : List[str] = distilled
snake_case_ : Dict = layer_norm_eps
snake_case_ : Optional[Any] = drop_rate
snake_case_ : str = qkv_bias
snake_case_ : Union[str, Any] = attn_drop_rate
snake_case_ : List[Any] = drop_path_rate
snake_case_ : Any = output_aa_attentions
snake_case_ : Dict = initializer_range
| 711 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Dict = batch_size
snake_case_ : Any = seq_length
snake_case_ : Tuple = is_training
snake_case_ : Dict = use_attention_mask
snake_case_ : int = use_token_type_ids
snake_case_ : List[Any] = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : str = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : int = max_position_embeddings
snake_case_ : Dict = type_vocab_size
snake_case_ : Dict = type_sequence_label_size
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Tuple = num_choices
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : List[str] = None
if self.use_token_type_ids:
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Tuple = RobertaPreLayerNormConfig(
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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : int = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Any = config_and_inputs
snake_case_ : Union[str, Any] = True
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : List[str] = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : str = model(_lowercase )[0]
snake_case_ : int = [1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ) , _lowercase )
# compare the actual values for a slice.
snake_case_ : Tuple = np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
@slow
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Dict = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : Any = model(_lowercase )[0]
# compare the actual values for a slice.
snake_case_ : Optional[Any] = np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
),
}
class _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
_lowerCamelCase = '''swin2sr'''
_lowerCamelCase = {
'''hidden_size''': '''embed_dim''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=6_4 , _lowercase=1 , _lowercase=3 , _lowercase=1_8_0 , _lowercase=[6, 6, 6, 6, 6, 6] , _lowercase=[6, 6, 6, 6, 6, 6] , _lowercase=8 , _lowercase=2.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=2 , _lowercase=1.0 , _lowercase="1conv" , _lowercase="pixelshuffle" , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_UpperCAmelCase )
snake_case_ : List[Any] = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[str] = embed_dim
snake_case_ : List[str] = depths
snake_case_ : Optional[int] = len(_UpperCAmelCase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : List[Any] = window_size
snake_case_ : List[Any] = mlp_ratio
snake_case_ : List[str] = qkv_bias
snake_case_ : Dict = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Optional[int] = drop_path_rate
snake_case_ : str = hidden_act
snake_case_ : Any = use_absolute_embeddings
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = initializer_range
snake_case_ : Any = upscale
snake_case_ : Union[str, Any] = img_range
snake_case_ : int = resi_connection
snake_case_ : Dict = upsampler
| 712 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCAmelCase : Optional[Any] = parser.parse_args()
__lowerCAmelCase : Dict = '''cpu'''
__lowerCAmelCase : Optional[Any] = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__lowerCAmelCase : Tuple = '''path-to-your-trained-model'''
__lowerCAmelCase : List[Any] = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__lowerCAmelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase : List[Any] = pipe.to(device)
# to channels last
__lowerCAmelCase : Optional[Any] = pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase : List[str] = pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase : Optional[Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__lowerCAmelCase : Dict = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__lowerCAmelCase : Tuple = torch.randn(2, 4, 64, 64)
__lowerCAmelCase : Any = torch.rand(1) * 999
__lowerCAmelCase : List[str] = torch.randn(2, 77, 768)
__lowerCAmelCase : Optional[int] = (sample, timestep, encoder_hidden_status)
try:
__lowerCAmelCase : List[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__lowerCAmelCase : Optional[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : Any = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : int = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__lowerCAmelCase : Union[str, Any] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__lowerCAmelCase : List[str] = 666
__lowerCAmelCase : Optional[int] = torch.Generator(device).manual_seed(seed)
__lowerCAmelCase : List[Any] = {'''generator''': generator}
if args.steps is not None:
__lowerCAmelCase : Any = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__lowerCAmelCase : str = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 21 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : int ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(UpperCAmelCase__ ) )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = 0.0
for coeff in reversed(UpperCAmelCase__ ):
snake_case_ : int = result * x + coeff
return result
if __name__ == "__main__":
__lowerCAmelCase : str = (0.0, 0.0, 5.0, 9.3, 7.0)
__lowerCAmelCase : Dict = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 713 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = RoFormerTokenizer
_lowerCamelCase = RoFormerTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
def UpperCAmelCase__ ( self , **_lowercase ) -> str:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = """永和服装饰品有限公司,今天天气非常好"""
snake_case_ : int = """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ , snake_case_ : Optional[Any] = self.get_chinese_input_output_texts()
snake_case_ : List[str] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : str = tokens + [tokenizer.unk_token]
snake_case_ : Tuple = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ , snake_case_ : List[Any] = self.get_chinese_input_output_texts()
snake_case_ : Union[str, Any] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : Optional[int] = tokens + [tokenizer.unk_token]
snake_case_ : Union[str, Any] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = DanceDiffusionPipeline
_lowerCamelCase = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
_lowerCamelCase = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
_lowerCamelCase = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[str] = UNetaDModel(
block_out_channels=(3_2, 3_2, 6_4) , extra_in_channels=1_6 , sample_size=5_1_2 , sample_rate=1_6_0_0_0 , in_channels=2 , out_channels=2 , flip_sin_to_cos=__UpperCamelCase , use_timestep_embedding=__UpperCamelCase , time_embedding_type="""fourier""" , mid_block_type="""UNetMidBlock1D""" , down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""") , up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""") , )
snake_case_ : Dict = IPNDMScheduler()
snake_case_ : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
}
return components
def UpperCAmelCase__ ( self , _lowercase , _lowercase=0 ) -> Tuple:
'''simple docstring'''
if str(__UpperCamelCase ).startswith("""mps""" ):
snake_case_ : Dict = torch.manual_seed(__UpperCamelCase )
else:
snake_case_ : Tuple = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
snake_case_ : Optional[int] = {
"""batch_size""": 1,
"""generator""": generator,
"""num_inference_steps""": 4,
}
return inputs
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case_ : Optional[int] = self.get_dummy_components()
snake_case_ : Optional[Any] = DanceDiffusionPipeline(**__UpperCamelCase )
snake_case_ : List[Any] = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ : Optional[Any] = self.get_dummy_inputs(__UpperCamelCase )
snake_case_ : str = pipe(**__UpperCamelCase )
snake_case_ : Dict = output.audios
snake_case_ : List[str] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
snake_case_ : str = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Tuple = torch_device
snake_case_ : Union[str, Any] = DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k""" )
snake_case_ : List[Any] = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ : Optional[Any] = torch.manual_seed(0 )
snake_case_ : Optional[Any] = pipe(generator=__UpperCamelCase , num_inference_steps=1_0_0 , audio_length_in_s=4.096 )
snake_case_ : Optional[int] = output.audios
snake_case_ : Union[str, Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
snake_case_ : Optional[int] = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : List[str] = torch_device
snake_case_ : Any = DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k""" , torch_dtype=torch.floataa )
snake_case_ : Optional[int] = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
snake_case_ : List[Any] = torch.manual_seed(0 )
snake_case_ : str = pipe(generator=__UpperCamelCase , num_inference_steps=1_0_0 , audio_length_in_s=4.096 )
snake_case_ : int = output.audios
snake_case_ : List[Any] = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
snake_case_ : List[Any] = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 714 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
snake_case_ : List[Any] = [
2_0_4_7,
1_3_7_3_6_5_3,
2_5_3_2_6_0_0_1,
3_2_1_5_0_3_1_7_5_1,
2_1_5_2_3_0_2_8_9_8_7_4_7,
3_4_7_4_7_4_9_6_6_0_3_8_3,
3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
1,
3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
1,
1,
3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
]
snake_case_ : Dict = [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
for idx, _p in enumerate(__UpperCamelCase , 1 ):
if n < _p:
# then we have our last prime to check
snake_case_ : Optional[int] = primes[:idx]
break
snake_case_ , snake_case_ : Tuple = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
snake_case_ : List[str] = False
for r in range(__UpperCamelCase ):
snake_case_ : int = pow(__UpperCamelCase , d * 2**r , __UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
snake_case_ : Optional[Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def __lowerCAmelCase ( ):
'''simple docstring'''
assert not miller_rabin(5_6_1 )
assert miller_rabin(5_6_3 )
# 2047
assert not miller_rabin(8_3_8_2_0_1 )
assert miller_rabin(8_3_8_2_0_7 )
# 1_373_653
assert not miller_rabin(1_7_3_1_6_0_0_1 )
assert miller_rabin(1_7_3_1_6_0_1_7 )
# 25_326_001
assert not miller_rabin(3_0_7_8_3_8_6_6_4_1 )
assert miller_rabin(3_0_7_8_3_8_6_6_5_3 )
# 3_215_031_751
assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1 )
assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9 )
# 2_152_302_898_747
assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7 )
assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7 )
# 3_474_749_660_383
assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1 )
assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7 )
# 341_550_071_728_321
assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1 )
assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7 )
assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3 )
assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError("""only integers accepted as input""" )
else:
snake_case_ : Union[str, Any] = str(abs(_UpperCamelCase ) )
snake_case_ : Tuple = [list(_UpperCamelCase ) for char in range(len(_UpperCamelCase ) )]
for index in range(len(_UpperCamelCase ) ):
num_transpositions[index].pop(_UpperCamelCase )
return max(
int("""""".join(list(_UpperCamelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 715 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def is_in_circle(__UpperCamelCase : float , __UpperCamelCase : float ) -> bool:
snake_case_ : Dict = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
snake_case_ : Tuple = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
snake_case_ : Union[str, Any] = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Callable[[float], float] , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 , ):
'''simple docstring'''
return mean(
function_to_integrate(uniform(__UpperCamelCase , __UpperCamelCase ) ) for _ in range(__UpperCamelCase ) ) * (max_value - min_value)
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ):
'''simple docstring'''
def identity_function(__UpperCamelCase : float ) -> float:
return x
snake_case_ : int = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : str = (max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("""******************""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def function_to_integrate(__UpperCamelCase : float ) -> float:
return sqrt(4.0 - x * x )
snake_case_ : List[Any] = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = 3
snake_case_ : int = 2_5_0
snake_case_ : List[str] = ids_tensor((batch_size, length) , _lowercase )
snake_case_ : List[Any] = torch.ones((batch_size, length) , device=_lowercase , dtype=torch.float ) / length
return input_ids, scores
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = self._get_tensors(5 )
snake_case_ : Optional[int] = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=1_0 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ , snake_case_ : Union[str, Any] = self._get_tensors(9 )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ , snake_case_ : Optional[Any] = self._get_tensors(1_0 )
self.assertTrue(criteria(_lowercase , _lowercase ) )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : int = MaxLengthCriteria(max_length=1_0 )
snake_case_ , snake_case_ : Any = self._get_tensors(5 )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ , snake_case_ : Dict = self._get_tensors(9 )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ , snake_case_ : str = self._get_tensors(1_0 )
self.assertTrue(criteria(_lowercase , _lowercase ) )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
snake_case_ , snake_case_ : Tuple = self._get_tensors(5 )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ , snake_case_ : Tuple = self._get_tensors(9 )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ , snake_case_ : Union[str, Any] = self._get_tensors(1_0 )
self.assertTrue(criteria(_lowercase , _lowercase ) )
snake_case_ : Optional[Any] = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 1_0 )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Optional[Any] = self._get_tensors(5 )
snake_case_ : Optional[int] = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(_lowercase , _lowercase ) )
snake_case_ : List[str] = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(_lowercase , _lowercase ) )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] ) , 1_0 )
with self.assertWarns(_lowercase ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0 )] ) , 1_1 )
snake_case_ : List[str] = validate_stopping_criteria(StoppingCriteriaList() , 1_1 )
self.assertEqual(len(_lowercase ) , 1 )
| 716 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = nn.functional.normalize(__UpperCamelCase )
snake_case_ : Tuple = nn.functional.normalize(__UpperCamelCase )
return torch.mm(__UpperCamelCase , normalized_text_embeds.t() )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = CLIPConfig
_lowerCamelCase = ['''CLIPEncoderLayer''']
def __init__( self , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(_lowercase )
snake_case_ : Tuple = CLIPVisionModel(config.vision_config )
snake_case_ : int = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=_lowercase )
snake_case_ : Optional[Any] = nn.Parameter(torch.ones(1_7 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Dict = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Any = nn.Parameter(torch.ones(1_7 ) , requires_grad=_lowercase )
snake_case_ : List[str] = nn.Parameter(torch.ones(3 ) , requires_grad=_lowercase )
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : int = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : str = self.visual_projection(_lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Dict = cosine_distance(_lowercase , self.special_care_embeds ).cpu().float().numpy()
snake_case_ : List[str] = cosine_distance(_lowercase , self.concept_embeds ).cpu().float().numpy()
snake_case_ : Any = []
snake_case_ : Any = image_embeds.shape[0]
for i in range(_lowercase ):
snake_case_ : List[Any] = {"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : int = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
snake_case_ : List[str] = special_cos_dist[i][concept_idx]
snake_case_ : Union[str, Any] = self.special_care_embeds_weights[concept_idx].item()
snake_case_ : Tuple = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]} )
snake_case_ : Dict = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
snake_case_ : int = cos_dist[i][concept_idx]
snake_case_ : List[Any] = self.concept_embeds_weights[concept_idx].item()
snake_case_ : List[str] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(_lowercase )
result.append(_lowercase )
snake_case_ : Union[str, Any] = [len(res["""bad_concepts"""] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : List[str] = self.visual_projection(_lowercase )
snake_case_ : str = cosine_distance(_lowercase , self.special_care_embeds )
snake_case_ : Optional[int] = cosine_distance(_lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : Tuple = 0.0
snake_case_ : List[Any] = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
snake_case_ : str = torch.any(special_scores > 0 , dim=1 )
snake_case_ : List[str] = special_care * 0.01
snake_case_ : Optional[int] = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
snake_case_ : Optional[Any] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
snake_case_ : str = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 21 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : int = mock.Mock()
snake_case_ : Tuple = 5_0_0
snake_case_ : Dict = {}
snake_case_ : Dict = HTTPError
snake_case_ : Optional[Any] = {}
# Download this model to make sure it's in the cache.
snake_case_ : List[Any] = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("""requests.Session.request""" , return_value=lowerCamelCase__ ) as mock_head:
snake_case_ : Any = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = mock.Mock()
snake_case_ : Any = 5_0_0
snake_case_ : str = {}
snake_case_ : Optional[Any] = HTTPError
snake_case_ : Tuple = {}
# Download this model to make sure it's in the cache.
snake_case_ : Optional[Any] = GPTaTokenizerFast.from_pretrained("""gpt2""" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("""requests.Session.request""" , return_value=lowerCamelCase__ ) as mock_head:
snake_case_ : str = GPTaTokenizerFast.from_pretrained("""gpt2""" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
try:
snake_case_ : Dict = tempfile.mktemp()
with open(lowerCamelCase__ , """wb""" ) as f:
http_get("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model""" , lowerCamelCase__ )
snake_case_ : Dict = AlbertTokenizer.from_pretrained(lowerCamelCase__ )
finally:
os.remove(lowerCamelCase__ )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("""tokenizer.json""" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("""tokenizer.json""" , """wb""" ) as f:
http_get("""https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json""" , lowerCamelCase__ )
snake_case_ : Union[str, Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 1_0_0_0 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("""tokenizer.json""" )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = AlbertTokenizer.from_pretrained("""https://huggingface.co/albert-base-v1/resolve/main/spiece.model""" )
@is_staging_test
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def UpperCAmelCase__ ( cls ) -> int:
'''simple docstring'''
snake_case_ : List[str] = TOKEN
HfFolder.save_token(lowerCamelCase__ )
@classmethod
def UpperCAmelCase__ ( cls ) -> Optional[Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="""test-tokenizer""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-tokenizer-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-tokenizer""" )
except HTTPError:
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : int = os.path.join(lowerCamelCase__ , """vocab.txt""" )
with open(lowerCamelCase__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
snake_case_ : Tuple = BertTokenizer(lowerCamelCase__ )
tokenizer.push_to_hub("""test-tokenizer""" , use_auth_token=self._token )
snake_case_ : Union[str, Any] = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id="""test-tokenizer""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(lowerCamelCase__ , repo_id="""test-tokenizer""" , push_to_hub=lowerCamelCase__ , use_auth_token=self._token )
snake_case_ : str = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : Dict = os.path.join(lowerCamelCase__ , """vocab.txt""" )
with open(lowerCamelCase__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
snake_case_ : List[str] = BertTokenizer(lowerCamelCase__ )
tokenizer.push_to_hub("""valid_org/test-tokenizer-org""" , use_auth_token=self._token )
snake_case_ : Optional[Any] = BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-tokenizer-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
lowerCamelCase__ , repo_id="""valid_org/test-tokenizer-org""" , push_to_hub=lowerCamelCase__ , use_auth_token=self._token )
snake_case_ : Union[str, Any] = BertTokenizer.from_pretrained("""valid_org/test-tokenizer-org""" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : List[Any] = os.path.join(lowerCamelCase__ , """vocab.txt""" )
with open(lowerCamelCase__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
snake_case_ : Any = CustomTokenizer(lowerCamelCase__ )
# No fast custom tokenizer
tokenizer.push_to_hub("""test-dynamic-tokenizer""" , use_auth_token=self._token )
snake_case_ : str = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' , trust_remote_code=lowerCamelCase__ )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , """CustomTokenizer""" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : List[Any] = os.path.join(lowerCamelCase__ , """vocab.txt""" )
with open(lowerCamelCase__ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
snake_case_ : List[Any] = BertTokenizerFast.from_pretrained(lowerCamelCase__ )
bert_tokenizer.save_pretrained(lowerCamelCase__ )
snake_case_ : Optional[int] = CustomTokenizerFast.from_pretrained(lowerCamelCase__ )
tokenizer.push_to_hub("""test-dynamic-tokenizer""" , use_auth_token=self._token )
snake_case_ : str = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' , trust_remote_code=lowerCamelCase__ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , """CustomTokenizerFast""" )
snake_case_ : int = AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' , use_fast=lowerCamelCase__ , trust_remote_code=lowerCamelCase__ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , """CustomTokenizer""" )
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = Trie()
trie.add("""Hello 友達""" )
self.assertEqual(trie.data , {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {""" """: {"""友""": {"""達""": {"""""": 1}}}}}}}}} )
trie.add("""Hello""" )
trie.data
self.assertEqual(trie.data , {"""H""": {"""e""": {"""l""": {"""l""": {"""o""": {"""""": 1, """ """: {"""友""": {"""達""": {"""""": 1}}}}}}}}} )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Dict = Trie()
self.assertEqual(trie.split("""[CLS] This is a extra_id_100""" ) , ["""[CLS] This is a extra_id_100"""] )
trie.add("""[CLS]""" )
trie.add("""extra_id_1""" )
trie.add("""extra_id_100""" )
self.assertEqual(trie.split("""[CLS] This is a extra_id_100""" ) , ["""[CLS]""", """ This is a """, """extra_id_100"""] )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = Trie()
trie.add("""A""" )
self.assertEqual(trie.split("""ABC""" ) , ["""A""", """BC"""] )
self.assertEqual(trie.split("""BCA""" ) , ["""BC""", """A"""] )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = Trie()
trie.add("""TOKEN]""" )
trie.add("""[SPECIAL_TOKEN]""" )
self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]""" ) , ["""This is something """, """[SPECIAL_TOKEN]"""] )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = Trie()
trie.add("""A""" )
trie.add("""P""" )
trie.add("""[SPECIAL_TOKEN]""" )
self.assertEqual(trie.split("""This is something [SPECIAL_TOKEN]""" ) , ["""This is something """, """[SPECIAL_TOKEN]"""] )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = Trie()
trie.add("""AB""" )
trie.add("""B""" )
trie.add("""C""" )
self.assertEqual(trie.split("""ABC""" ) , ["""AB""", """C"""] )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : int = Trie()
trie.add("""ABC""" )
trie.add("""B""" )
trie.add("""CD""" )
self.assertEqual(trie.split("""ABCD""" ) , ["""ABC""", """D"""] )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = Trie()
snake_case_ : Optional[Any] = trie.cut_text("""ABC""" , [0, 0, 2, 1, 2, 3] )
self.assertEqual(lowerCamelCase__ , ["""AB""", """C"""] )
| 717 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : List[str] = []
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
F'stage{idx}.patch_embed.proj.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
F'stage{idx}.patch_embed.proj.bias',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
F'stage{idx}.patch_embed.norm.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
F'stage{idx}.patch_embed.norm.bias',
) )
return embed
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : str = []
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
F'stage{idx}.blocks.{cnt}.attn.proj.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
F'stage{idx}.blocks.{cnt}.attn.proj.bias',
) )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias') )
return attention_weights
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : int = []
token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""") )
return token
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = []
head.append(("""layernorm.weight""", """norm.weight""") )
head.append(("""layernorm.bias""", """norm.bias""") )
head.append(("""classifier.weight""", """head.weight""") )
head.append(("""classifier.bias""", """head.bias""") )
return head
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = """imagenet-1k-id2label.json"""
snake_case_ : Optional[Any] = 1_0_0_0
snake_case_ : Any = """huggingface/label-files"""
snake_case_ : Tuple = num_labels
snake_case_ : Dict = json.load(open(cached_download(hf_hub_url(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
snake_case_ : str = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[str] = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
snake_case_ : Dict = CvtConfig(num_labels=__UpperCamelCase , idalabel=__UpperCamelCase , labelaid=__UpperCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "13":
snake_case_ : Any = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "21":
snake_case_ : Any = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ : Optional[int] = [2, 2, 2_0]
snake_case_ : str = [3, 1_2, 1_6]
snake_case_ : Any = [1_9_2, 7_6_8, 1_0_2_4]
snake_case_ : Union[str, Any] = CvtForImageClassification(__UpperCamelCase )
snake_case_ : str = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
snake_case_ : List[Any] = image_size
snake_case_ : str = torch.load(__UpperCamelCase , map_location=torch.device("""cpu""" ) )
snake_case_ : Any = OrderedDict()
snake_case_ : Tuple = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ : Optional[Any] = list_of_state_dict + cls_token(__UpperCamelCase )
snake_case_ : str = list_of_state_dict + embeddings(__UpperCamelCase )
for cnt in range(config.depth[idx] ):
snake_case_ : List[str] = list_of_state_dict + attention(__UpperCamelCase , __UpperCamelCase )
snake_case_ : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__UpperCamelCase )
for i in range(len(__UpperCamelCase ) ):
snake_case_ : Union[str, Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
image_processor.save_pretrained(__UpperCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowerCAmelCase : Dict = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 21 | 0 |
import os
import pytest
from attr import dataclass
__lowerCAmelCase : Optional[Any] = 'us-east-1' # defaults region
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_lowerCamelCase = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 16,
'''per_device_eval_batch_size''': 16,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 500,
'''save_steps''': 5_500,
}
_lowerCamelCase = {**hyperparameters, '''max_steps''': 1_000}
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return f'{self.framework}-transfromers-test'
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return f'./tests/sagemaker/scripts/{self.framework}'
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Any = SageMakerTestEnvironment(framework=request.cls.framework )
| 718 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MgpstrTokenizer
_lowerCamelCase = False
_lowerCamelCase = {}
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# fmt: off
snake_case_ : Optional[Any] = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
snake_case_ : str = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowercase ) + """\n""" )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = """tester"""
snake_case_ : Tuple = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ : Optional[Any] = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
snake_case_ : Union[str, Any] = tokenizer.encode([special_token] , add_special_tokens=_lowercase )
self.assertEqual(len(_lowercase ) , 1 )
snake_case_ : List[Any] = tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ , snake_case_ : Union[str, Any] = self.get_input_output_texts(_lowercase )
snake_case_ : List[Any] = tokenizer.tokenize(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertNotEqual(len(_lowercase ) , 0 )
snake_case_ : str = tokenizer.decode(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(text_a.replace(""" """ , """""" ) , _lowercase )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : Dict = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : int = ["""DeiTFeatureExtractor"""]
__lowerCAmelCase : Any = ["""DeiTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[str] = [
"""DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DeiTForImageClassification""",
"""DeiTForImageClassificationWithTeacher""",
"""DeiTForMaskedImageModeling""",
"""DeiTModel""",
"""DeiTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any = [
"""TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDeiTForImageClassification""",
"""TFDeiTForImageClassificationWithTeacher""",
"""TFDeiTForMaskedImageModeling""",
"""TFDeiTModel""",
"""TFDeiTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 719 |
"""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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : List[str] = batch_size
snake_case_ : int = seq_length
snake_case_ : List[Any] = is_training
snake_case_ : Optional[int] = use_attention_mask
snake_case_ : Optional[Any] = use_token_type_ids
snake_case_ : Union[str, Any] = use_labels
snake_case_ : str = vocab_size
snake_case_ : List[str] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : List[str] = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : str = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : str = num_choices
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[int] = None
if self.use_token_type_ids:
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = 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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_lowercase )
snake_case_ : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case_ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ : Dict = model(_lowercase )[0]
snake_case_ : Optional[int] = 5_0_0_0_0
snake_case_ : Union[str, Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _lowercase )
snake_case_ : Dict = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__lowerCAmelCase : Union[str, Any] = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__lowerCAmelCase : Optional[Any] = {'''facebook/blenderbot-3B''': 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Optional[int] = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
snake_case_ : Tuple = bs[:]
snake_case_ : Any = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCamelCase__ )
cs.append(2**8 + n )
n += 1
snake_case_ : List[str] = [chr(lowerCamelCase__ ) for n in cs]
return dict(zip(lowerCamelCase__ , lowerCamelCase__ ) )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = set()
snake_case_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case_ : Dict = char
return pairs
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self , _lowercase , _lowercase , _lowercase="replace" , _lowercase="<s>" , _lowercase="</s>" , _lowercase="</s>" , _lowercase="<s>" , _lowercase="<unk>" , _lowercase="<pad>" , _lowercase="<mask>" , _lowercase=False , **_lowercase , ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else bos_token
snake_case_ : Optional[Any] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else eos_token
snake_case_ : Optional[int] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else sep_token
snake_case_ : List[Any] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else cls_token
snake_case_ : List[Any] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else unk_token
snake_case_ : List[str] = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ : str = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
errors=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , cls_token=_lowercase , pad_token=_lowercase , mask_token=_lowercase , add_prefix_space=_lowercase , **_lowercase , )
with open(_lowercase , encoding="""utf-8""" ) as vocab_handle:
snake_case_ : List[Any] = json.load(_lowercase )
snake_case_ : List[str] = {v: k for k, v in self.encoder.items()}
snake_case_ : Dict = errors # how to handle errors in decoding
snake_case_ : Union[str, Any] = bytes_to_unicode()
snake_case_ : Union[str, Any] = {v: k for k, v in self.byte_encoder.items()}
with open(_lowercase , encoding="""utf-8""" ) as merges_handle:
snake_case_ : List[str] = merges_handle.read().split("""\n""" )[1:-1]
snake_case_ : str = [tuple(merge.split() ) for merge in bpe_merges]
snake_case_ : Optional[int] = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : List[Any] = {}
snake_case_ : Any = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
snake_case_ : List[Any] = re.compile(R"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self , _lowercase ) -> List[Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
snake_case_ : int = tuple(_lowercase )
snake_case_ : str = get_pairs(_lowercase )
if not pairs:
return token
while True:
snake_case_ : List[Any] = min(_lowercase , key=lambda _lowercase : self.bpe_ranks.get(_lowercase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case_ , snake_case_ : int = bigram
snake_case_ : Any = []
snake_case_ : Any = 0
while i < len(_lowercase ):
try:
snake_case_ : Any = word.index(_lowercase , _lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case_ : Any = j
if word[i] == first and i < len(_lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case_ : Optional[Any] = tuple(_lowercase )
snake_case_ : List[str] = new_word
if len(_lowercase ) == 1:
break
else:
snake_case_ : List[Any] = get_pairs(_lowercase )
snake_case_ : str = """ """.join(_lowercase )
snake_case_ : Optional[Any] = word
return word
def UpperCAmelCase__ ( self , _lowercase ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = []
for token in re.findall(self.pat , _lowercase ):
snake_case_ : Union[str, Any] = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase ).split(""" """ ) )
return bpe_tokens
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[Any]:
'''simple docstring'''
return self.encoder.get(_lowercase , self.encoder.get(self.unk_token ) )
def UpperCAmelCase__ ( self , _lowercase ) -> List[str]:
'''simple docstring'''
return self.decoder.get(_lowercase )
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = """""".join(_lowercase )
snake_case_ : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
snake_case_ : List[Any] = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
snake_case_ : List[Any] = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowercase , ensure_ascii=_lowercase ) + """\n""" )
snake_case_ : Dict = 0
with open(_lowercase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
snake_case_ : Union[str, Any] = token_index
writer.write(""" """.join(_lowercase ) + """\n""" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase )
if token_ids_a is None:
return [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> List[int]:
'''simple docstring'''
snake_case_ : List[Any] = [self.sep_token_id]
snake_case_ : Optional[int] = [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 UpperCAmelCase__ ( self , _lowercase , _lowercase=False , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_lowercase ) > 0 and not text[0].isspace()):
snake_case_ : Union[str, Any] = """ """ + text
return (text, kwargs)
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> Dict:
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self , _lowercase ) -> List[int]:
'''simple docstring'''
snake_case_ : Optional[int] = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(""" """ + text )
else:
# Generated responses should contain them already.
inputs.append(_lowercase )
snake_case_ : List[str] = """ """.join(_lowercase )
snake_case_ : Tuple = self.encode(_lowercase )
if len(_lowercase ) > self.model_max_length:
snake_case_ : Dict = input_ids[-self.model_max_length :]
logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
return input_ids
| 720 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : int = 1_0
snake_case_ : Any = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
snake_case_ : Tuple = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(__UpperCamelCase ) ),
} , features=__UpperCamelCase , )
return dataset
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return filename
# FILE_CONTENT + files
__lowerCAmelCase : List[Any] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
snake_case_ : Optional[Any] = FILE_CONTENT
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
import bza
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
snake_case_ : Any = bytes(__UpperCamelCase , """utf-8""" )
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
import gzip
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
snake_case_ : List[Any] = bytes(__UpperCamelCase , """utf-8""" )
with gzip.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
snake_case_ : Optional[Any] = bytes(__UpperCamelCase , """utf-8""" )
with lza.frame.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(__UpperCamelCase , """w""" ) as archive:
archive.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple ):
'''simple docstring'''
import tarfile
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
import lzma
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
snake_case_ : str = bytes(__UpperCamelCase , """utf-8""" )
with lzma.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
import zipfile
snake_case_ : Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
snake_case_ : Tuple = bytes(__UpperCamelCase , """utf-8""" )
with zstd.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
snake_case_ : List[str] = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
__lowerCAmelCase : List[str] = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__lowerCAmelCase : Tuple = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCAmelCase : int = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
__lowerCAmelCase : int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
__lowerCAmelCase : Any = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = datasets.Dataset.from_dict(__UpperCamelCase )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(__UpperCamelCase ) ) as con:
snake_case_ : Tuple = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : Optional[Any] = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : str = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : int ):
'''simple docstring'''
import bza
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(__UpperCamelCase , """rb""" ) as f:
snake_case_ : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(__UpperCamelCase , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
snake_case_ : Any = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(__UpperCamelCase , """wb""" ) as f:
snake_case_ : Optional[int] = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase )
snake_case_ : Optional[int] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCamelCase ) )] for k in DATA[0]} , schema=__UpperCamelCase )
writer.write_table(__UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : Any = {"""data""": DATA}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : List[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
import gzip
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] ):
'''simple docstring'''
import gzip
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : List[str] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : str = ["""0""", """1""", """2""", """3"""]
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : int = ["""0""", """1""", """2""", """3"""]
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : List[Any] = ["""0""", """1""", """2""", """3"""]
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 21 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : int = {
'''nvidia/segformer-b0-finetuned-ade-512-512''': (
'''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class _lowerCAmelCase ( snake_case__ ):
"""simple docstring"""
_lowerCamelCase = '''segformer'''
def __init__( self , _lowercase=3 , _lowercase=4 , _lowercase=[2, 2, 2, 2] , _lowercase=[8, 4, 2, 1] , _lowercase=[3_2, 6_4, 1_6_0, 2_5_6] , _lowercase=[7, 3, 3, 3] , _lowercase=[4, 2, 2, 2] , _lowercase=[1, 2, 5, 8] , _lowercase=[4, 4, 4, 4] , _lowercase="gelu" , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase=0.02 , _lowercase=0.1 , _lowercase=1E-6 , _lowercase=2_5_6 , _lowercase=2_5_5 , **_lowercase , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , _SCREAMING_SNAKE_CASE , )
snake_case_ : Optional[Any] = num_channels
snake_case_ : List[str] = num_encoder_blocks
snake_case_ : str = depths
snake_case_ : Optional[Any] = sr_ratios
snake_case_ : Dict = hidden_sizes
snake_case_ : Tuple = patch_sizes
snake_case_ : Optional[Any] = strides
snake_case_ : List[Any] = mlp_ratios
snake_case_ : Dict = num_attention_heads
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Dict = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Optional[int] = classifier_dropout_prob
snake_case_ : int = initializer_range
snake_case_ : str = drop_path_rate
snake_case_ : Optional[int] = layer_norm_eps
snake_case_ : str = decoder_hidden_size
snake_case_ : Any = kwargs.get("""reshape_last_stage""" , _SCREAMING_SNAKE_CASE )
snake_case_ : str = semantic_loss_ignore_index
class _lowerCAmelCase ( snake_case__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return 1_2
| 721 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ : List[str] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : Union[str, Any] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : int = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@register_to_config
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = False , ) -> Dict:
'''simple docstring'''
super().__init__()
snake_case_ : List[Any] = nn.Embedding(_lowercase , _lowercase )
snake_case_ : str = nn.Embedding(_lowercase , _lowercase )
snake_case_ : Dict = False
snake_case_ : Optional[int] = nn.Dropout(p=_lowercase )
snake_case_ : Any = TaConfig(
vocab_size=_lowercase , d_model=_lowercase , num_heads=_lowercase , d_kv=_lowercase , d_ff=_lowercase , dropout_rate=_lowercase , feed_forward_proj=_lowercase , is_decoder=_lowercase , is_encoder_decoder=_lowercase , )
snake_case_ : Optional[Any] = nn.ModuleList()
for lyr_num in range(_lowercase ):
snake_case_ : Dict = TaBlock(_lowercase )
self.encoders.append(_lowercase )
snake_case_ : Tuple = TaLayerNorm(_lowercase )
snake_case_ : List[str] = nn.Dropout(p=_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.token_embedder(_lowercase )
snake_case_ : Optional[Any] = encoder_input_tokens.shape[1]
snake_case_ : str = torch.arange(_lowercase , device=encoder_input_tokens.device )
x += self.position_encoding(_lowercase )
snake_case_ : Optional[Any] = self.dropout_pre(_lowercase )
# inverted the attention mask
snake_case_ : Optional[int] = encoder_input_tokens.size()
snake_case_ : Optional[int] = self.get_extended_attention_mask(_lowercase , _lowercase )
for lyr in self.encoders:
snake_case_ : Union[str, Any] = lyr(_lowercase , _lowercase )[0]
snake_case_ : str = self.layer_norm(_lowercase )
return self.dropout_post(_lowercase ), encoder_inputs_mask
| 700 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"""compression_format, is_archive""" , [
("""7z""", True),
("""bz2""", False),
("""gzip""", False),
("""lz4""", False),
("""tar""", True),
("""xz""", False),
("""zip""", True),
("""zstd""", False),
] , )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Any , __UpperCamelCase : str , __UpperCamelCase : Any , __UpperCamelCase : Any , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] , ):
'''simple docstring'''
snake_case_ : int = {
"""7z""": (seven_zip_file, SevenZipExtractor),
"""bz2""": (bza_file, BzipaExtractor),
"""gzip""": (gz_file, GzipExtractor),
"""lz4""": (lza_file, LzaExtractor),
"""tar""": (tar_file, TarExtractor),
"""xz""": (xz_file, XzExtractor),
"""zip""": (zip_file, ZipExtractor),
"""zstd""": (zstd_file, ZstdExtractor),
}
snake_case_ : List[Any] = input_paths_and_base_extractors[compression_format]
if input_path is None:
snake_case_ : Optional[Any] = F'for \'{compression_format}\' compression_format, '
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__UpperCamelCase )
assert base_extractor.is_extractable(__UpperCamelCase )
snake_case_ : Tuple = tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
base_extractor.extract(__UpperCamelCase , __UpperCamelCase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case_ : Optional[Any] = file_path.read_text(encoding="""utf-8""" )
else:
snake_case_ : Dict = output_path.read_text(encoding="""utf-8""" )
snake_case_ : Any = text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"""compression_format, is_archive""" , [
("""7z""", True),
("""bz2""", False),
("""gzip""", False),
("""lz4""", False),
("""tar""", True),
("""xz""", False),
("""zip""", True),
("""zstd""", False),
] , )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Tuple , __UpperCamelCase : str , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str , __UpperCamelCase : Any , __UpperCamelCase : Dict , __UpperCamelCase : int , ):
'''simple docstring'''
snake_case_ : int = {
"""7z""": seven_zip_file,
"""bz2""": bza_file,
"""gzip""": gz_file,
"""lz4""": lza_file,
"""tar""": tar_file,
"""xz""": xz_file,
"""zip""": zip_file,
"""zstd""": zstd_file,
}
snake_case_ : Tuple = input_paths[compression_format]
if input_path is None:
snake_case_ : str = F'for \'{compression_format}\' compression_format, '
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__UpperCamelCase )
snake_case_ : str = Extractor.infer_extractor_format(__UpperCamelCase )
assert extractor_format is not None
snake_case_ : List[Any] = tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
Extractor.extract(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
snake_case_ : Optional[int] = file_path.read_text(encoding="""utf-8""" )
else:
snake_case_ : str = output_path.read_text(encoding="""utf-8""" )
snake_case_ : Union[str, Any] = text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Dict ):
'''simple docstring'''
import tarfile
snake_case_ : Any = tmp_path / """data_dot_dot"""
directory.mkdir()
snake_case_ : Dict = directory / """tar_file_with_dot_dot.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join("""..""" , text_file.name ) )
return path
@pytest.fixture
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
import tarfile
snake_case_ : List[str] = tmp_path / """data_sym_link"""
directory.mkdir()
snake_case_ : Optional[Any] = directory / """tar_file_with_sym_link.tar"""
os.symlink("""..""" , directory / """subdir""" , target_is_directory=__UpperCamelCase )
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(str(directory / """subdir""" ) , arcname="""subdir""" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"""insecure_tar_file, error_log""" , [("""tar_file_with_dot_dot""", """illegal path"""), ("""tar_file_with_sym_link""", """Symlink""")] , )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = {
"""tar_file_with_dot_dot""": tar_file_with_dot_dot,
"""tar_file_with_sym_link""": tar_file_with_sym_link,
}
snake_case_ : Dict = insecure_tar_files[insecure_tar_file]
snake_case_ : str = tmp_path / """extracted"""
TarExtractor.extract(__UpperCamelCase , __UpperCamelCase )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = tmpdir / """not_a_zip_file"""
# From: https://github.com/python/cpython/pull/5053
snake_case_ : Optional[Any] = (
B"""\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"""
B"""\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"""
B"""DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"""
B"""\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"""
)
with not_a_zip_file.open("""wb""" ) as f:
f.write(__UpperCamelCase )
assert zipfile.is_zipfile(str(__UpperCamelCase ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__UpperCamelCase ) # but we're right
| 701 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''nat'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=4 , _lowercase=3 , _lowercase=6_4 , _lowercase=[3, 4, 6, 5] , _lowercase=[2, 4, 8, 1_6] , _lowercase=7 , _lowercase=3.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=0.0 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : Any = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = embed_dim
snake_case_ : Tuple = depths
snake_case_ : int = len(_lowercase )
snake_case_ : Optional[int] = num_heads
snake_case_ : List[str] = kernel_size
snake_case_ : str = mlp_ratio
snake_case_ : str = qkv_bias
snake_case_ : str = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Tuple = drop_path_rate
snake_case_ : Dict = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Tuple = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Union[str, Any] = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Union[str, Any] = layer_scale_init_value
snake_case_ : Optional[Any] = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 21 | 0 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCAmelCase : List[Any] = '''src/transformers'''
__lowerCAmelCase : Dict = '''docs/source/en'''
__lowerCAmelCase : Optional[Any] = '''.'''
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
with open(__UpperCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
snake_case_ : int = f.readlines()
# Find the start prompt.
snake_case_ : int = 0
while not lines[start_index].startswith(__UpperCamelCase ):
start_index += 1
start_index += 1
snake_case_ : Union[str, Any] = start_index
while not lines[end_index].startswith(__UpperCamelCase ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
__lowerCAmelCase : int = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase : int = re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCAmelCase : Any = re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase : List[str] = re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase : int = direct_transformers_import(TRANSFORMERS_PATH)
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : int = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , __UpperCamelCase )
return [m.group(0 ) for m in matches]
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Dict = 2 if text == """✅""" or text == """❌""" else len(__UpperCamelCase )
snake_case_ : Tuple = (width - text_length) // 2
snake_case_ : Any = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : List[Any] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
snake_case_ : Optional[Any] = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
snake_case_ : List[str] = {name: config.replace("""Config""" , """""" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
snake_case_ : Dict = collections.defaultdict(__UpperCamelCase )
snake_case_ : List[Any] = collections.defaultdict(__UpperCamelCase )
snake_case_ : str = collections.defaultdict(__UpperCamelCase )
snake_case_ : str = collections.defaultdict(__UpperCamelCase )
snake_case_ : Dict = collections.defaultdict(__UpperCamelCase )
# Let's lookup through all transformers object (once).
for attr_name in dir(__UpperCamelCase ):
snake_case_ : Union[str, Any] = None
if attr_name.endswith("""Tokenizer""" ):
snake_case_ : Dict = slow_tokenizers
snake_case_ : Any = attr_name[:-9]
elif attr_name.endswith("""TokenizerFast""" ):
snake_case_ : Optional[Any] = fast_tokenizers
snake_case_ : str = attr_name[:-1_3]
elif _re_tf_models.match(__UpperCamelCase ) is not None:
snake_case_ : List[Any] = tf_models
snake_case_ : Tuple = _re_tf_models.match(__UpperCamelCase ).groups()[0]
elif _re_flax_models.match(__UpperCamelCase ) is not None:
snake_case_ : Dict = flax_models
snake_case_ : Tuple = _re_flax_models.match(__UpperCamelCase ).groups()[0]
elif _re_pt_models.match(__UpperCamelCase ) is not None:
snake_case_ : List[Any] = pt_models
snake_case_ : Any = _re_pt_models.match(__UpperCamelCase ).groups()[0]
if lookup_dict is not None:
while len(__UpperCamelCase ) > 0:
if attr_name in model_name_to_prefix.values():
snake_case_ : Any = True
break
# Try again after removing the last word in the name
snake_case_ : Tuple = """""".join(camel_case_split(__UpperCamelCase )[:-1] )
# Let's build that table!
snake_case_ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
snake_case_ : str = ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
snake_case_ : str = [len(__UpperCamelCase ) + 2 for c in columns]
snake_case_ : Dict = max([len(__UpperCamelCase ) for name in model_names] ) + 2
# Build the table per se
snake_case_ : List[Any] = """|""" + """|""".join([_center_text(__UpperCamelCase , __UpperCamelCase ) for c, w in zip(__UpperCamelCase , __UpperCamelCase )] ) + """|\n"""
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths] ) + "|\n"
snake_case_ : List[Any] = {True: """✅""", False: """❌"""}
for name in model_names:
snake_case_ : Union[str, Any] = model_name_to_prefix[name]
snake_case_ : Optional[int] = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__UpperCamelCase , __UpperCamelCase ) for l, w in zip(__UpperCamelCase , __UpperCamelCase )] ) + "|\n"
return table
def __lowerCAmelCase ( __UpperCamelCase : Any=False ):
'''simple docstring'''
snake_case_ : Optional[int] = _find_text_in_file(
filename=os.path.join(__UpperCamelCase , """index.md""" ) , start_prompt="""<!--This table is updated automatically from the auto modules""" , end_prompt="""<!-- End table-->""" , )
snake_case_ : int = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__UpperCamelCase , """index.md""" ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"""The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.""" )
if __name__ == "__main__":
__lowerCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__lowerCAmelCase : Any = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 702 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__lowerCAmelCase : Optional[Any] = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[Any] = VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(_lowercase )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = 1_2
snake_case_ : Tuple = 1_2
snake_case_ : Tuple = {
"""attention_bias""": True,
"""cross_attention_dim""": 3_2,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 3_2,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
snake_case_ : Optional[Any] = TransformeraDModel(**_lowercase )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = """cpu"""
snake_case_ : List[str] = self.dummy_vqvae
snake_case_ : Any = self.dummy_text_encoder
snake_case_ : Tuple = self.dummy_tokenizer
snake_case_ : int = self.dummy_transformer
snake_case_ : int = VQDiffusionScheduler(self.num_embed )
snake_case_ : Dict = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase )
snake_case_ : Optional[Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : int = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : List[Any] = """teddy bear playing in the pool"""
snake_case_ : Dict = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : List[Any] = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Optional[int] = output.images
snake_case_ : List[Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Dict = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : List[Any] = image[0, -3:, -3:, -1]
snake_case_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : Dict = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
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 UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : int = """cpu"""
snake_case_ : List[Any] = self.dummy_vqvae
snake_case_ : Optional[int] = self.dummy_text_encoder
snake_case_ : List[Any] = self.dummy_tokenizer
snake_case_ : Union[str, Any] = self.dummy_transformer
snake_case_ : str = VQDiffusionScheduler(self.num_embed )
snake_case_ : List[Any] = LearnedClassifierFreeSamplingEmbeddings(
learnable=_lowercase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ : Union[str, Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : Any = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : Tuple = """teddy bear playing in the pool"""
snake_case_ : str = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Tuple = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Dict = output.images
snake_case_ : Union[str, Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Any = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case_ : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : int = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
snake_case_ : str = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
snake_case_ : Optional[Any] = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ : Any = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Optional[int] = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=_lowercase , output_type="""np""" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 21 | 0 |
"""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
__lowerCAmelCase : List[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 __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = ["""layers""", """blocks"""]
for k in ignore_keys:
state_dict.pop(__UpperCamelCase , __UpperCamelCase )
__lowerCAmelCase : Dict = {
'''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 __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = list(s_dict.keys() )
for key in keys:
snake_case_ : Optional[int] = key
for k, v in WHISPER_MAPPING.items():
if k in key:
snake_case_ : Optional[Any] = new_key.replace(__UpperCamelCase , __UpperCamelCase )
print(F'{key} -> {new_key}' )
snake_case_ : Tuple = s_dict.pop(__UpperCamelCase )
return s_dict
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : str = emb.weight.shape
snake_case_ : List[str] = nn.Linear(__UpperCamelCase , __UpperCamelCase , bias=__UpperCamelCase )
snake_case_ : Optional[Any] = emb.weight.data
return lin_layer
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : str ):
'''simple docstring'''
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
snake_case_ : Optional[int] = os.path.basename(__UpperCamelCase )
snake_case_ : List[str] = url.split("""/""" )[-2]
snake_case_ : Tuple = os.path.join(__UpperCamelCase , __UpperCamelCase )
if os.path.exists(__UpperCamelCase ) and not os.path.isfile(__UpperCamelCase ):
raise RuntimeError(F'{download_target} exists and is not a regular file' )
if os.path.isfile(__UpperCamelCase ):
snake_case_ : Optional[Any] = open(__UpperCamelCase , """rb""" ).read()
if hashlib.shaaaa(__UpperCamelCase ).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(__UpperCamelCase ) as source, open(__UpperCamelCase , """wb""" ) as output:
with tqdm(
total=int(source.info().get("""Content-Length""" ) ) , ncols=8_0 , unit="""iB""" , unit_scale=__UpperCamelCase , unit_divisor=1_0_2_4 ) as loop:
while True:
snake_case_ : Optional[int] = source.read(8_1_9_2 )
if not buffer:
break
output.write(__UpperCamelCase )
loop.update(len(__UpperCamelCase ) )
snake_case_ : List[Any] = open(__UpperCamelCase , """rb""" ).read()
if hashlib.shaaaa(__UpperCamelCase ).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 __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : str ):
'''simple docstring'''
if ".pt" not in checkpoint_path:
snake_case_ : int = _download(_MODELS[checkpoint_path] )
else:
snake_case_ : Optional[int] = torch.load(__UpperCamelCase , map_location="""cpu""" )
snake_case_ : Optional[int] = original_checkpoint["""dims"""]
snake_case_ : int = original_checkpoint["""model_state_dict"""]
snake_case_ : List[str] = state_dict["""decoder.token_embedding.weight"""]
remove_ignore_keys_(__UpperCamelCase )
rename_keys(__UpperCamelCase )
snake_case_ : Union[str, Any] = True
snake_case_ : int = state_dict["""decoder.layers.0.fc1.weight"""].shape[0]
snake_case_ : Dict = WhisperConfig(
vocab_size=dimensions["""n_vocab"""] , encoder_ffn_dim=__UpperCamelCase , decoder_ffn_dim=__UpperCamelCase , 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"""] , )
snake_case_ : Optional[Any] = WhisperForConditionalGeneration(__UpperCamelCase )
snake_case_ : Dict = model.model.load_state_dict(__UpperCamelCase , strict=__UpperCamelCase )
if len(__UpperCamelCase ) > 0 and not set(__UpperCamelCase ) <= {
"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:
snake_case_ : Optional[int] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ : Any = proj_out_weights
model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = 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.''')
__lowerCAmelCase : List[str] = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 703 |
"""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 __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Any=0 ):
'''simple docstring'''
if name is None:
snake_case_ : Dict = None
else:
snake_case_ : Dict = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(5_0 - spaces ) + """s}"""
snake_case_ : Any = fmt.format(__UpperCamelCase )
# Print and recurse (if needed).
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if msg is not None:
print(__UpperCamelCase )
for k in val.keys():
recursive_print(__UpperCamelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCamelCase , torch.Tensor ):
print(__UpperCamelCase , """:""" , val.size() )
else:
print(__UpperCamelCase , """:""" , __UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Any = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case_ : List[str] = (num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case_ : Tuple = param.view(*__UpperCamelCase )
snake_case_ : Tuple = param.transpose(0 , 2 )
snake_case_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case_ : Optional[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case_ : str = param.view(*__UpperCamelCase )
snake_case_ : Dict = param.transpose(0 , 1 ).contiguous()
snake_case_ : int = param.view(*__UpperCamelCase )
return param
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Dict = {}
# old versions did not store training args
snake_case_ : List[str] = input_state_dict.get("""args""" , __UpperCamelCase )
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))
snake_case_ : Tuple = ds_args.padded_vocab_size
snake_case_ : Optional[int] = ds_args.max_position_embeddings
snake_case_ : Union[str, Any] = ds_args.hidden_size
snake_case_ : Union[str, Any] = ds_args.num_layers
snake_case_ : str = ds_args.num_attention_heads
snake_case_ : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case_ : Union[str, Any] = config.n_head
# The hidden_size per head.
snake_case_ : Optional[Any] = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case_ : Optional[Any] = input_state_dict["""checkpoint_version"""]
else:
snake_case_ : int = 0.0
# The model.
snake_case_ : List[str] = input_state_dict["""model"""]
# The language model.
snake_case_ : str = model["""language_model"""]
# The embeddings.
snake_case_ : Tuple = lm["""embedding"""]
# The word embeddings.
snake_case_ : List[str] = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
snake_case_ : Optional[int] = word_embeddings[: config.vocab_size, :]
snake_case_ : Optional[int] = word_embeddings
# The position embeddings.
snake_case_ : List[Any] = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
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.
snake_case_ : Union[str, Any] = pos_embeddings
# The transformer.
snake_case_ : Optional[Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
snake_case_ : Optional[Any] = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
snake_case_ : List[str] = {
"""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.
snake_case_ : int = layer_re.match(__UpperCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case_ : Tuple = int(m.group(1 ) )
# The name of the operation.
snake_case_ : Any = m.group(2 )
# Is it a weight or a bias?
snake_case_ : Union[str, Any] = m.group(3 )
# The name of the layer.
snake_case_ : str = F'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
snake_case_ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
snake_case_ : Optional[int] = 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.
snake_case_ : Optional[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCamelCase , __UpperCamelCase )
snake_case_ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case_ : str = torch.tensor(-1E4 , dtype=torch.floataa )
snake_case_ : List[Any] = masked_bias
snake_case_ : Optional[int] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case_ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case_ : Tuple = 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":
snake_case_ : Optional[Any] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Store. No change of shape.
snake_case_ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case_ : Any = megatron_to_transformers[op_name]
snake_case_ : str = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case_ : List[str] = megatron_to_transformers[op_name]
snake_case_ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case_ : Dict = transformer["""final_layernorm.weight"""]
snake_case_ : Dict = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case_ : Optional[int] = word_embeddings
# It should be done!
return output_state_dict
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__UpperCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__UpperCamelCase , help="""An optional config json file describing the pre-trained model.""" , )
snake_case_ : str = parser.parse_args()
# Extract the basename.
snake_case_ : Optional[Any] = 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:
snake_case_ : Optional[int] = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
snake_case_ : List[Any] = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
snake_case_ : Any = input_state_dict.get("""args""" , __UpperCamelCase )
# 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:
snake_case_ : Any = """gelu_fast"""
elif ds_args.openai_gelu:
snake_case_ : Tuple = """gelu_new"""
else:
snake_case_ : List[str] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
snake_case_ : Dict = """gelu_new"""
# Spell out all parameters in case the defaults change.
snake_case_ : List[str] = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=__UpperCamelCase , 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=__UpperCamelCase , summary_activation=__UpperCamelCase , summary_proj_to_labels=__UpperCamelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCamelCase , use_cache=__UpperCamelCase , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
snake_case_ : List[Any] = GPTaConfig.from_json_file(args.config_file )
snake_case_ : int = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
snake_case_ : Tuple = convert_megatron_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCamelCase , __UpperCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case_ : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case_ : Optional[Any] = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
snake_case_ : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
else:
snake_case_ : List[str] = """gpt2"""
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(__UpperCamelCase )
snake_case_ : List[str] = type(__UpperCamelCase ).__name__
snake_case_ : Optional[int] = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__UpperCamelCase )
# Save tokenizer based on args
print(F'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(__UpperCamelCase )
# Store the state_dict to file.
snake_case_ : List[Any] = os.path.join(__UpperCamelCase , """pytorch_model.bin""" )
print(F'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(__UpperCamelCase , __UpperCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 21 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : str = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=_lowercase , dtype=jnp.bfloataa )
snake_case_ : Tuple = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_lowercase , from_pt=_lowercase , dtype=jnp.bfloataa )
snake_case_ : Tuple = controlnet_params
snake_case_ : int = """bird"""
snake_case_ : Any = jax.device_count()
snake_case_ : List[str] = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
snake_case_ : Any = pipe.prepare_image_inputs([canny_image] * num_samples )
snake_case_ : List[Any] = jax.random.PRNGKey(0 )
snake_case_ : int = jax.random.split(_lowercase , jax.device_count() )
snake_case_ : List[str] = replicate(_lowercase )
snake_case_ : Optional[int] = shard(_lowercase )
snake_case_ : Optional[Any] = shard(_lowercase )
snake_case_ : str = pipe(
prompt_ids=_lowercase , image=_lowercase , params=_lowercase , prng_seed=_lowercase , num_inference_steps=5_0 , jit=_lowercase , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
snake_case_ : int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case_ : Dict = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
snake_case_ : Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case_ : Dict = jnp.array(
[0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=_lowercase , dtype=jnp.bfloataa )
snake_case_ : Dict = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_lowercase , from_pt=_lowercase , dtype=jnp.bfloataa )
snake_case_ : str = controlnet_params
snake_case_ : List[str] = """Chef in the kitchen"""
snake_case_ : int = jax.device_count()
snake_case_ : int = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case_ : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
snake_case_ : Tuple = pipe.prepare_image_inputs([pose_image] * num_samples )
snake_case_ : Tuple = jax.random.PRNGKey(0 )
snake_case_ : Optional[Any] = jax.random.split(_lowercase , jax.device_count() )
snake_case_ : Dict = replicate(_lowercase )
snake_case_ : Union[str, Any] = shard(_lowercase )
snake_case_ : Optional[int] = shard(_lowercase )
snake_case_ : Dict = pipe(
prompt_ids=_lowercase , image=_lowercase , params=_lowercase , prng_seed=_lowercase , num_inference_steps=5_0 , jit=_lowercase , ).images
assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)
snake_case_ : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case_ : Dict = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
snake_case_ : Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case_ : Tuple = jnp.array(
[[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 704 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : List[Any] = tf.cast(math.pi , x.dtype )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Tuple = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -1_0 , 1_0 )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=-1 ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
__lowerCAmelCase : int = tf.keras.activations.gelu
__lowerCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
__lowerCAmelCase : List[Any] = _gelu
__lowerCAmelCase : Any = _gelu_new
__lowerCAmelCase : Dict = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 21 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=7 , _lowercase=3 , _lowercase=1_8 , _lowercase=3_0 , _lowercase=4_0_0 , _lowercase=True , _lowercase=None , _lowercase=True , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = size if size is not None else {"""height""": 1_8, """width""": 1_8}
snake_case_ : str = parent
snake_case_ : List[str] = batch_size
snake_case_ : str = num_channels
snake_case_ : Dict = image_size
snake_case_ : Dict = min_resolution
snake_case_ : str = max_resolution
snake_case_ : Dict = do_resize
snake_case_ : List[str] = size
snake_case_ : Union[str, Any] = apply_ocr
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = LayoutLMvaImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , """do_resize""" ) )
self.assertTrue(hasattr(_lowercase , """size""" ) )
self.assertTrue(hasattr(_lowercase , """apply_ocr""" ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 1_8, """width""": 1_8} )
snake_case_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {"""height""": 4_2, """width""": 4_2} )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
self.assertIsInstance(encoding.words , _lowercase )
self.assertIsInstance(encoding.boxes , _lowercase )
# Test batched
snake_case_ : Union[str, Any] = image_processing(_lowercase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
snake_case_ : Union[str, Any] = image_processing(_lowercase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
snake_case_ : int = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
snake_case_ : Dict = image_processing(_lowercase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[Any] = LayoutLMvaImageProcessor()
from datasets import load_dataset
snake_case_ : Dict = load_dataset("""hf-internal-testing/fixtures_docvqa""" , split="""test""" )
snake_case_ : Any = Image.open(ds[0]["""file"""] ).convert("""RGB""" )
snake_case_ : List[str] = image_processing(_lowercase , return_tensors="""pt""" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
snake_case_ : Optional[int] = [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]] # noqa: E231
snake_case_ : int = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , _lowercase )
self.assertListEqual(encoding.boxes , _lowercase )
# with apply_OCR = False
snake_case_ : int = LayoutLMvaImageProcessor(apply_ocr=_lowercase )
snake_case_ : int = image_processing(_lowercase , return_tensors="""pt""" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
| 705 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : int = [0] * len(__UpperCamelCase )
snake_case_ : List[str] = []
snake_case_ : Any = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCamelCase )
while queue:
snake_case_ : Optional[int] = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case_ : Union[str, Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__UpperCamelCase )
print(max(__UpperCamelCase ) )
# Adjacency list of Graph
__lowerCAmelCase : str = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 21 | 0 |
"""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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
snake_case_ : int = tf.convert_to_tensor(
[[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
snake_case_ : str = model(_lowercase )["""last_hidden_state"""]
snake_case_ : int = tf.TensorShape((1, 1_0, 7_6_8) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
snake_case_ : str = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , 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 ) )
| 706 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(__UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return binomial_coefficient(2 * node_count , __UpperCamelCase ) // (node_count + 1)
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
snake_case_ : Optional[int] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return catalan_number(__UpperCamelCase ) * factorial(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 21 | 0 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : List[str] = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaises(_lowercase ):
snake_case_ : Dict = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
with self.assertRaises(_lowercase ):
snake_case_ : str = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""bool""" ) , type=Value("""int64""" ) ) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Dict = pa.array(TypedSequence([1, 2, 3] , type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
snake_case_ : Tuple = pa.array(TypedSequence(["""foo""", """bar"""] , type=Value("""int64""" ) ) )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=Value("""int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : List[Any] = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
snake_case_ : List[str] = pa.array(TypedSequence(["""foo""", """bar"""] , type=ArrayaD((1, 3) , """int64""" ) ) )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : str = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
import PIL.Image
snake_case_ : int = PIL.Image.fromarray(np.arange(1_0 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
"""datasets.arrow_writer.cast_to_python_objects""" , side_effect=_lowercase ) as mock_cast_to_python_objects:
snake_case_ : Any = pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image] , type=Image() ) )
snake_case_ : List[Any] = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn("""optimize_list_casting""" , _lowercase )
self.assertFalse(kwargs["""optimize_list_casting"""] )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[str] = pa.BufferReader(__UpperCamelCase ) if isinstance(__UpperCamelCase , pa.Buffer ) else pa.memory_map(__UpperCamelCase )
snake_case_ : Optional[Any] = pa.ipc.open_stream(__UpperCamelCase )
snake_case_ : pa.Table = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = pa.BufferOutputStream()
snake_case_ : List[Any] = pa.schema(__UpperCamelCase ) if fields else None
with ArrowWriter(stream=__UpperCamelCase , schema=__UpperCamelCase , writer_batch_size=__UpperCamelCase ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
snake_case_ : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case_ : Tuple = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(__UpperCamelCase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = pa.BufferOutputStream()
snake_case_ : Optional[Any] = Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
with ArrowWriter(stream=__UpperCamelCase , features=__UpperCamelCase ) as writer:
writer.write({"""labels""": 0} )
writer.write({"""labels""": 1} )
snake_case_ : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
snake_case_ : int = pa.BufferReader(output.getvalue() )
snake_case_ : Optional[int] = pa.ipc.open_stream(__UpperCamelCase )
snake_case_ : pa.Table = f.read_all()
snake_case_ : Optional[int] = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(__UpperCamelCase )
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 1_0] )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : int = pa.BufferOutputStream()
with ArrowWriter(
stream=__UpperCamelCase , writer_batch_size=__UpperCamelCase , hash_salt="""split_name""" , check_duplicates=__UpperCamelCase , ) as writer:
with pytest.raises(__UpperCamelCase ):
writer.write({"""col_1""": """foo""", """col_2""": 1} , key=[1, 2] )
snake_case_ : List[Any] = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" , [None, 2, 1_0] )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[str] = pa.BufferOutputStream()
with ArrowWriter(
stream=__UpperCamelCase , writer_batch_size=__UpperCamelCase , hash_salt="""split_name""" , check_duplicates=__UpperCamelCase , ) as writer:
with pytest.raises(__UpperCamelCase ):
writer.write({"""col_1""": """foo""", """col_2""": 1} , key=1_0 )
writer.write({"""col_1""": """bar""", """col_2""": 2} , key=1_0 )
snake_case_ : Dict = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" , [None, 2, 1_0] )
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : List[Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=__UpperCamelCase , writer_batch_size=__UpperCamelCase , hash_salt="""split_name""" , check_duplicates=__UpperCamelCase , ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} , key=1 )
writer.write({"""col_1""": """bar""", """col_2""": 2} , key=2 )
snake_case_ : List[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = pa.BufferOutputStream()
snake_case_ : int = pa.schema(__UpperCamelCase ) if fields else None
with ArrowWriter(stream=__UpperCamelCase , schema=__UpperCamelCase , writer_batch_size=__UpperCamelCase ) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
writer.write_batch({"""col_1""": [], """col_2""": []} )
snake_case_ : List[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case_ : Tuple = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(__UpperCamelCase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = pa.BufferOutputStream()
snake_case_ : str = pa.schema(__UpperCamelCase ) if fields else None
with ArrowWriter(stream=__UpperCamelCase , schema=__UpperCamelCase , writer_batch_size=__UpperCamelCase ) as writer:
writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} ) )
snake_case_ : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case_ : List[str] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(__UpperCamelCase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = pa.BufferOutputStream()
snake_case_ : Optional[int] = pa.schema(__UpperCamelCase ) if fields else None
with ArrowWriter(stream=__UpperCamelCase , schema=__UpperCamelCase , writer_batch_size=__UpperCamelCase ) as writer:
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]} ) )
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]} ) )
snake_case_ : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
snake_case_ : List[Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(__UpperCamelCase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def __lowerCAmelCase ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case_ : str = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
snake_case_ : Any = os.path.join(__UpperCamelCase , """test.arrow""" )
with ArrowWriter(path=__UpperCamelCase , schema=pa.schema(__UpperCamelCase ) ) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
snake_case_ : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(__UpperCamelCase , metadata=writer._schema.metadata )
_check_output(__UpperCamelCase , 1 )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if pa.types.is_list(__UpperCamelCase ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Dict ):
'''simple docstring'''
if isinstance(lst[0] , __UpperCamelCase ):
change_first_primitive_element_in_list(lst[0] , __UpperCamelCase )
else:
snake_case_ : Tuple = value
@pytest.mark.parametrize("""optimized_int_type, expected_dtype""" , [(None, pa.intaa()), (Value("""int32""" ), pa.intaa())] )
@pytest.mark.parametrize("""sequence""" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : List[str] , __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : str = pa.array(TypedSequence(__UpperCamelCase , optimized_int_type=__UpperCamelCase ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
"""col, expected_dtype""" , [
("""attention_mask""", pa.inta()),
("""special_tokens_mask""", pa.inta()),
("""token_type_ids""", pa.inta()),
("""input_ids""", pa.intaa()),
("""other""", pa.intaa()),
] , )
@pytest.mark.parametrize("""sequence""" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : List[str] = pa.array(OptimizedTypedSequence(__UpperCamelCase , col=__UpperCamelCase ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
snake_case_ : Any = copy.deepcopy(__UpperCamelCase )
snake_case_ : List[str] = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(__UpperCamelCase , __UpperCamelCase )
snake_case_ : str = pa.array(OptimizedTypedSequence(__UpperCamelCase , col=__UpperCamelCase ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize("""raise_exception""" , [False, True] )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : List[str] = str(tmp_path / """dataset-train.arrow""" )
try:
with ArrowWriter(path=__UpperCamelCase ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : List[Any] = """mock://dataset-train.arrow"""
with ArrowWriter(path=__UpperCamelCase , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(__UpperCamelCase ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
snake_case_ : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(__UpperCamelCase )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = pa.BufferOutputStream()
with ParquetWriter(stream=__UpperCamelCase ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
snake_case_ : str = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
snake_case_ : Optional[int] = pa.BufferReader(output.getvalue() )
snake_case_ : pa.Table = pq.read_table(__UpperCamelCase )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("""embed_local_files""" , [False, True] )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
import PIL.Image
snake_case_ : int = str(tmp_path / """test_image_rgb.jpg""" )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(__UpperCamelCase , format="""png""" )
snake_case_ : List[str] = pa.BufferOutputStream()
with ParquetWriter(
stream=__UpperCamelCase , features=Features({"""image""": Image()} ) , embed_local_files=__UpperCamelCase ) as writer:
writer.write({"""image""": image_path} )
writer.finalize()
snake_case_ : Tuple = pa.BufferReader(output.getvalue() )
snake_case_ : pa.Table = pq.read_table(__UpperCamelCase )
snake_case_ : List[Any] = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out["""image"""][0]["""path"""] , __UpperCamelCase )
with open(__UpperCamelCase , """rb""" ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Tuple = pa.schema([pa.field("""col_1""" , pa.string() , nullable=__UpperCamelCase )] )
snake_case_ : Optional[Any] = pa.BufferOutputStream()
with ArrowWriter(stream=__UpperCamelCase ) as writer:
writer._build_writer(inferred_schema=__UpperCamelCase )
assert writer._schema == pa.schema([pa.field("""col_1""" , pa.string() )] )
| 707 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''swin'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=2_2_4 , _lowercase=4 , _lowercase=3 , _lowercase=9_6 , _lowercase=[2, 2, 6, 2] , _lowercase=[3, 6, 1_2, 2_4] , _lowercase=7 , _lowercase=4.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=3_2 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : str = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = embed_dim
snake_case_ : Optional[int] = depths
snake_case_ : Optional[int] = len(_lowercase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : Optional[Any] = window_size
snake_case_ : Optional[Any] = mlp_ratio
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = drop_path_rate
snake_case_ : List[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Any = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Tuple = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Tuple = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 21 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowerCAmelCase ( __UpperCamelCase : int = 8 ):
'''simple docstring'''
snake_case_ : int = ascii_letters + digits + punctuation
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
i -= len(__UpperCamelCase )
snake_case_ : Union[str, Any] = i // 3
snake_case_ : Optional[int] = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
snake_case_ : str = (
chars_incl
+ random(__UpperCamelCase , quotient + remainder )
+ random(__UpperCamelCase , __UpperCamelCase )
+ random(__UpperCamelCase , __UpperCamelCase )
)
snake_case_ : str = list(__UpperCamelCase )
shuffle(__UpperCamelCase )
return "".join(__UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
return "".join(secrets.choice(__UpperCamelCase ) for _ in range(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : Tuple ):
'''simple docstring'''
pass # Put your code here...
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : str ):
'''simple docstring'''
pass # Put your code here...
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Any ):
'''simple docstring'''
pass # Put your code here...
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int = 8 ):
'''simple docstring'''
if len(__UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
snake_case_ : Tuple = any(char in ascii_uppercase for char in password )
snake_case_ : str = any(char in ascii_lowercase for char in password )
snake_case_ : int = any(char in digits for char in password )
snake_case_ : Union[str, Any] = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = int(input("""Please indicate the max length of your password: """ ).strip() )
snake_case_ : Tuple = input(
"""Please indicate the characters that must be in your password: """ ).strip()
print("""Password generated:""" , password_generator(__UpperCamelCase ) )
print(
"""Alternative Password generated:""" , alternative_password_generator(__UpperCamelCase , __UpperCamelCase ) , )
print("""[If you are thinking of using this passsword, You better save it.]""" )
if __name__ == "__main__":
main()
| 708 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
snake_case_ : List[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
snake_case_ : Dict = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
snake_case_ : List[str] = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
snake_case_ : Optional[Any] = shift_tokens_right(_lowercase , model.config.pad_token_id , model.config.decoder_start_token_id )
snake_case_ : Tuple = model(_lowercase , decoder_input_ids=_lowercase ).logits
snake_case_ : Tuple = optax.softmax_cross_entropy(_lowercase , onehot(_lowercase , logits.shape[-1] ) ).mean()
snake_case_ : List[str] = -(labels.shape[-1] * loss.item())
snake_case_ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowerCAmelCase : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase ( datasets.BuilderConfig ):
"""simple docstring"""
_lowerCamelCase = 10_000
_lowerCamelCase = None
_lowerCamelCase = None
class _lowerCAmelCase ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
_lowerCamelCase = ParquetConfig
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def UpperCAmelCase__ ( self , _lowercase ) -> int:
'''simple docstring'''
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
snake_case_ : str = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_lowercase , (str, list, tuple) ):
snake_case_ : Union[str, Any] = data_files
if isinstance(_lowercase , _lowercase ):
snake_case_ : Any = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
snake_case_ : str = [dl_manager.iter_files(_lowercase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
snake_case_ : Union[str, Any] = []
for split_name, files in data_files.items():
if isinstance(_lowercase , _lowercase ):
snake_case_ : Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
snake_case_ : List[str] = [dl_manager.iter_files(_lowercase ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(_lowercase ):
with open(_lowercase , """rb""" ) as f:
snake_case_ : List[str] = datasets.Features.from_arrow_schema(pq.read_schema(_lowercase ) )
break
splits.append(datasets.SplitGenerator(name=_lowercase , gen_kwargs={"""files""": files} ) )
return splits
def UpperCAmelCase__ ( self , _lowercase ) -> pa.Table:
'''simple docstring'''
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
snake_case_ : Dict = table_cast(_lowercase , self.info.features.arrow_schema )
return pa_table
def UpperCAmelCase__ ( self , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f'Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'' )
for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase ) ):
with open(_lowercase , """rb""" ) as f:
snake_case_ : int = pq.ParquetFile(_lowercase )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
snake_case_ : Tuple = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'{file_idx}_{batch_idx}', self._cast_table(_lowercase )
except ValueError as e:
logger.error(f'Failed to read file \'{file}\' with error {type(_lowercase )}: {e}' )
raise
| 709 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 21 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase__ ( _lowercase ) -> List[str]:
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
raise NotImplementedError()
| 710 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCAmelCase : List[str] = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
'''
__lowerCAmelCase : Optional[Any] = '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''
__lowerCAmelCase : str = '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
predictions: list of predictions to score. Depending on the SuperGlUE subset:
- for \'record\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'prediction_text\': the predicted answer text
- for \'multirc\': list of question-answer dictionaries with the following keys:
- \'idx\': index of the question-answer pair as specified by the dataset
- \'prediction\': the predicted answer label
- otherwise: list of predicted labels
references: list of reference labels. Depending on the SuperGLUE subset:
- for \'record\': list of question-answers dictionaries with the following keys:
- \'idx\': index of the question as specified by the dataset
- \'answers\': list of possible answers
- otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
- for \'record\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1\': F1 score
- for \'multirc\':
- \'exact_match\': Exact match between answer and gold answer
- \'f1_m\': Per-question macro-F1 score
- \'f1_a\': Average F1 score over all answers
- for \'axb\':
\'matthews_correlation\': Matthew Correlation
- for \'cb\':
- \'accuracy\': Accuracy
- \'f1\': F1 score
- for all others:
- \'accuracy\': Accuracy
Examples:
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
>>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
>>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
>>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
>>> references = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}
>>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = super_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Any ):
'''simple docstring'''
return float((preds == labels).mean() )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : str="binary" ):
'''simple docstring'''
snake_case_ : Optional[Any] = simple_accuracy(__UpperCamelCase , __UpperCamelCase )
snake_case_ : Dict = float(fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average=__UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : List[Any] = {}
for id_pred, label in zip(__UpperCamelCase , __UpperCamelCase ):
snake_case_ : Optional[int] = F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
snake_case_ : Union[str, Any] = id_pred["""prediction"""]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
snake_case_ : str = [(pred, label)]
snake_case_ , snake_case_ : List[str] = [], []
for question, preds_labels in question_map.items():
snake_case_ , snake_case_ : Optional[Any] = zip(*__UpperCamelCase )
snake_case_ : int = fa_score(y_true=__UpperCamelCase , y_pred=__UpperCamelCase , average="""macro""" )
fas.append(__UpperCamelCase )
snake_case_ : Dict = int(sum(pred == label for pred, label in preds_labels ) == len(__UpperCamelCase ) )
ems.append(__UpperCamelCase )
snake_case_ : Optional[int] = float(sum(__UpperCamelCase ) / len(__UpperCamelCase ) )
snake_case_ : Any = sum(__UpperCamelCase ) / len(__UpperCamelCase )
snake_case_ : int = float(fa_score(y_true=__UpperCamelCase , y_pred=[id_pred["""prediction"""] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None , )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"prediction_text": datasets.Value("""string""" ),
},
"references": {
"idx": {
"passage": datasets.Value("""int64""" ),
"query": datasets.Value("""int64""" ),
},
"answers": datasets.Sequence(datasets.Value("""string""" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("""int64""" ),
"paragraph": datasets.Value("""int64""" ),
"question": datasets.Value("""int64""" ),
},
"prediction": datasets.Value("""int64""" ),
},
"references": datasets.Value("""int64""" ),
}
else:
return {
"predictions": datasets.Value("""int64""" ),
"references": datasets.Value("""int64""" ),
}
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(_lowercase , _lowercase )}
elif self.config_name == "cb":
return acc_and_fa(_lowercase , _lowercase , fa_avg="""macro""" )
elif self.config_name == "record":
snake_case_ : Optional[Any] = [
{
"""qas""": [
{"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
for ref in references
]
}
]
snake_case_ : Dict = {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
return evaluate_record(_lowercase , _lowercase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(_lowercase , _lowercase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(_lowercase , _lowercase )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]""" )
| 21 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''swin'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=2_2_4 , _lowercase=4 , _lowercase=3 , _lowercase=9_6 , _lowercase=[2, 2, 6, 2] , _lowercase=[3, 6, 1_2, 2_4] , _lowercase=7 , _lowercase=4.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=3_2 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : str = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = embed_dim
snake_case_ : Optional[int] = depths
snake_case_ : Optional[int] = len(_lowercase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : Optional[Any] = window_size
snake_case_ : Optional[Any] = mlp_ratio
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = drop_path_rate
snake_case_ : List[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Any = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Tuple = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Tuple = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 711 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Dict = batch_size
snake_case_ : Any = seq_length
snake_case_ : Tuple = is_training
snake_case_ : Dict = use_attention_mask
snake_case_ : int = use_token_type_ids
snake_case_ : List[Any] = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : str = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : int = max_position_embeddings
snake_case_ : Dict = type_vocab_size
snake_case_ : Dict = type_sequence_label_size
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Tuple = num_choices
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : List[str] = None
if self.use_token_type_ids:
snake_case_ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Tuple = RobertaPreLayerNormConfig(
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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : int = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : Any = config_and_inputs
snake_case_ : Union[str, Any] = True
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : List[str] = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : str = model(_lowercase )[0]
snake_case_ : int = [1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ) , _lowercase )
# compare the actual values for a slice.
snake_case_ : Tuple = np.array(
[[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
@slow
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : Any = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_lowercase )
snake_case_ : Dict = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] , dtype=jnp.intaa )
snake_case_ : Any = model(_lowercase )[0]
# compare the actual values for a slice.
snake_case_ : Optional[Any] = np.array(
[[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Optional[Any] = [False] * len(__UpperCamelCase )
snake_case_ : str = [s]
snake_case_ : Tuple = True
while queue:
snake_case_ : Dict = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__UpperCamelCase )
snake_case_ : Dict = True
snake_case_ : Tuple = u
return visited[t]
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[int] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = [-1] * (len(__UpperCamelCase ))
snake_case_ : Any = 0
snake_case_ : Optional[Any] = []
snake_case_ : List[str] = [i[:] for i in graph] # Record original cut, copy.
while bfs(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
snake_case_ : Dict = float("""Inf""" )
snake_case_ : List[Any] = sink
while s != source:
# Find the minimum value in select path
snake_case_ : Optional[int] = min(__UpperCamelCase , graph[parent[s]][s] )
snake_case_ : Dict = parent[s]
max_flow += path_flow
snake_case_ : Tuple = sink
while v != source:
snake_case_ : int = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
snake_case_ : Optional[int] = parent[v]
for i in range(len(__UpperCamelCase ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 712 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__lowerCAmelCase : Optional[Any] = parser.parse_args()
__lowerCAmelCase : Dict = '''cpu'''
__lowerCAmelCase : Optional[Any] = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__lowerCAmelCase : Tuple = '''path-to-your-trained-model'''
__lowerCAmelCase : List[Any] = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__lowerCAmelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__lowerCAmelCase : List[Any] = pipe.to(device)
# to channels last
__lowerCAmelCase : Optional[Any] = pipe.unet.to(memory_format=torch.channels_last)
__lowerCAmelCase : List[str] = pipe.vae.to(memory_format=torch.channels_last)
__lowerCAmelCase : Optional[Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__lowerCAmelCase : Dict = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__lowerCAmelCase : Tuple = torch.randn(2, 4, 64, 64)
__lowerCAmelCase : Any = torch.rand(1) * 999
__lowerCAmelCase : List[str] = torch.randn(2, 77, 768)
__lowerCAmelCase : Optional[int] = (sample, timestep, encoder_hidden_status)
try:
__lowerCAmelCase : List[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__lowerCAmelCase : Optional[Any] = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : Any = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__lowerCAmelCase : int = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__lowerCAmelCase : Union[str, Any] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__lowerCAmelCase : List[str] = 666
__lowerCAmelCase : Optional[int] = torch.Generator(device).manual_seed(seed)
__lowerCAmelCase : List[Any] = {'''generator''': generator}
if args.steps is not None:
__lowerCAmelCase : Any = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__lowerCAmelCase : str = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 21 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Optional[int] = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = SpeechTaTokenizer
_lowerCamelCase = False
_lowerCamelCase = True
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : Tuple = SpeechTaTokenizer(_lowercase )
snake_case_ : List[str] = AddedToken("""<mask>""" , lstrip=_lowercase , rstrip=_lowercase )
snake_case_ : int = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[int] = """this is a test"""
snake_case_ : Dict = """this is a test"""
return input_text, output_text
def UpperCAmelCase__ ( self , _lowercase , _lowercase=False , _lowercase=2_0 , _lowercase=5 ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.get_input_output_texts(_lowercase )
snake_case_ : List[Any] = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
snake_case_ : Optional[Any] = tokenizer.decode(_lowercase , clean_up_tokenization_spaces=_lowercase )
return text, ids
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = """<pad>"""
snake_case_ : Dict = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-4] , """œ""" )
self.assertEqual(vocab_keys[-2] , """<mask>""" )
self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" )
self.assertEqual(len(_lowercase ) , 8_1 )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 7_9 )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ : Optional[Any] = tokenizer.vocab_size
snake_case_ : str = len(_lowercase )
self.assertNotEqual(_lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
snake_case_ : Dict = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
snake_case_ : Optional[Any] = tokenizer.add_tokens(_lowercase )
snake_case_ : int = tokenizer.vocab_size
snake_case_ : int = len(_lowercase )
self.assertNotEqual(_lowercase , 0 )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , len(_lowercase ) )
self.assertEqual(_lowercase , all_size + len(_lowercase ) )
snake_case_ : Optional[int] = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=_lowercase )
self.assertGreaterEqual(len(_lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
snake_case_ : Optional[Any] = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
snake_case_ : str = tokenizer.add_special_tokens(_lowercase )
snake_case_ : List[Any] = tokenizer.vocab_size
snake_case_ : Tuple = len(_lowercase )
self.assertNotEqual(_lowercase , 0 )
self.assertEqual(_lowercase , _lowercase )
self.assertEqual(_lowercase , len(_lowercase ) )
self.assertEqual(_lowercase , all_size_a + len(_lowercase ) )
snake_case_ : List[str] = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=_lowercase )
self.assertGreaterEqual(len(_lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Any = self.get_tokenizer()
snake_case_ : Any = tokenizer.tokenize("""This is a test""" )
# fmt: off
self.assertListEqual(_lowercase , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6] , )
snake_case_ : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowercase , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
snake_case_ : List[str] = tokenizer.convert_tokens_to_ids(_lowercase )
# fmt: off
self.assertListEqual(_lowercase , [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6] )
# fmt: on
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertListEqual(
_lowercase , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
snake_case_ : int = {
"""input_ids""": [
[4, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
[4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 2, 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, 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, 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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 2, 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, 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, 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, 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, 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, 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, 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, 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],
],
"""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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[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, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowercase , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=_lowercase , )
| 713 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = RoFormerTokenizer
_lowerCamelCase = RoFormerTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().setUp()
def UpperCAmelCase__ ( self , **_lowercase ) -> str:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" , **_lowercase )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Tuple = """永和服装饰品有限公司,今天天气非常好"""
snake_case_ : int = """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
snake_case_ , snake_case_ : Optional[Any] = self.get_chinese_input_output_texts()
snake_case_ : List[str] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : str = tokens + [tokenizer.unk_token]
snake_case_ : Tuple = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : int = self.get_rust_tokenizer()
snake_case_ , snake_case_ : List[Any] = self.get_chinese_input_output_texts()
snake_case_ : Union[str, Any] = tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , output_text.split() )
snake_case_ : Optional[int] = tokens + [tokenizer.unk_token]
snake_case_ : Union[str, Any] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , _lowercase )
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = 1
@register_to_config
def __init__( self , _lowercase=2_0_0_0 , _lowercase=0.1 , _lowercase=2_0 , _lowercase=1E-3 ) -> str:
'''simple docstring'''
snake_case_ : str = None
snake_case_ : Union[str, Any] = None
snake_case_ : Optional[int] = None
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = torch.linspace(1 , self.config.sampling_eps , _lowercase , device=_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase=None ) -> int:
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
snake_case_ : Optional[Any] = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
snake_case_ : Dict = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
snake_case_ : str = std.flatten()
while len(std.shape ) < len(score.shape ):
snake_case_ : List[str] = std.unsqueeze(-1 )
snake_case_ : str = -score / std
# compute
snake_case_ : Any = -1.0 / len(self.timesteps )
snake_case_ : List[str] = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
snake_case_ : Tuple = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
snake_case_ : Union[str, Any] = beta_t.unsqueeze(-1 )
snake_case_ : List[str] = -0.5 * beta_t * x
snake_case_ : Dict = torch.sqrt(_lowercase )
snake_case_ : Any = drift - diffusion**2 * score
snake_case_ : str = x + drift * dt
# add noise
snake_case_ : Dict = randn_tensor(x.shape , layout=x.layout , generator=_lowercase , device=x.device , dtype=x.dtype )
snake_case_ : str = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ) -> int:
'''simple docstring'''
return self.config.num_train_timesteps
| 714 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
snake_case_ : List[Any] = [
2_0_4_7,
1_3_7_3_6_5_3,
2_5_3_2_6_0_0_1,
3_2_1_5_0_3_1_7_5_1,
2_1_5_2_3_0_2_8_9_8_7_4_7,
3_4_7_4_7_4_9_6_6_0_3_8_3,
3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
1,
3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
1,
1,
3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
]
snake_case_ : Dict = [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
for idx, _p in enumerate(__UpperCamelCase , 1 ):
if n < _p:
# then we have our last prime to check
snake_case_ : Optional[int] = primes[:idx]
break
snake_case_ , snake_case_ : Tuple = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
snake_case_ : List[str] = False
for r in range(__UpperCamelCase ):
snake_case_ : int = pow(__UpperCamelCase , d * 2**r , __UpperCamelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
snake_case_ : Optional[Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def __lowerCAmelCase ( ):
'''simple docstring'''
assert not miller_rabin(5_6_1 )
assert miller_rabin(5_6_3 )
# 2047
assert not miller_rabin(8_3_8_2_0_1 )
assert miller_rabin(8_3_8_2_0_7 )
# 1_373_653
assert not miller_rabin(1_7_3_1_6_0_0_1 )
assert miller_rabin(1_7_3_1_6_0_1_7 )
# 25_326_001
assert not miller_rabin(3_0_7_8_3_8_6_6_4_1 )
assert miller_rabin(3_0_7_8_3_8_6_6_5_3 )
# 3_215_031_751
assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1 )
assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9 )
# 2_152_302_898_747
assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7 )
assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7 )
# 3_474_749_660_383
assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1 )
assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7 )
# 341_550_071_728_321
assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1 )
assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7 )
assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3 )
assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 21 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase , **_lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = feature_size
snake_case_ : Optional[int] = sampling_rate
snake_case_ : Dict = padding_value
snake_case_ : Any = kwargs.pop("""padding_side""" , """right""" )
snake_case_ : Dict = kwargs.pop("""return_attention_mask""" , _lowercase )
super().__init__(**_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase = True , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ) -> BatchFeature:
'''simple docstring'''
if isinstance(_lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
snake_case_ : Optional[Any] = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f' to this method that includes {self.model_input_names[0]}, but you provided'
f' {list(processed_features.keys() )}' )
snake_case_ : Union[str, Any] = processed_features[self.model_input_names[0]]
snake_case_ : Tuple = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(_lowercase ) == 0:
if return_attention_mask:
snake_case_ : Dict = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
snake_case_ : Union[str, Any] = required_input[0]
if isinstance(_lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
snake_case_ : Any = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(_lowercase ):
snake_case_ : Any = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(_lowercase ):
snake_case_ : Optional[Any] = """tf"""
elif is_torch_tensor(_lowercase ):
snake_case_ : Union[str, Any] = """pt"""
elif isinstance(_lowercase , (int, float, list, tuple, np.ndarray) ):
snake_case_ : int = """np"""
else:
raise ValueError(
f'type of {first_element} unknown: {type(_lowercase )}. '
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
snake_case_ : Dict = to_numpy(_lowercase )
else:
snake_case_ : Optional[int] = [to_numpy(_lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
snake_case_ : List[str] = self._get_padding_strategies(padding=_lowercase , max_length=_lowercase )
snake_case_ : List[Any] = processed_features[self.model_input_names[0]]
snake_case_ : str = len(_lowercase )
if not all(len(_lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
snake_case_ : Any = []
for i in range(_lowercase ):
snake_case_ : List[str] = {k: v[i] for k, v in processed_features.items()}
# truncation
snake_case_ : Any = self._truncate(
_lowercase , max_length=_lowercase , pad_to_multiple_of=_lowercase , truncation=_lowercase , )
truncated_inputs.append(_lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
snake_case_ : Optional[int] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
snake_case_ : List[str] = PaddingStrategy.MAX_LENGTH
snake_case_ : List[Any] = {}
for i in range(_lowercase ):
# padding
snake_case_ : str = self._pad(
truncated_inputs[i] , max_length=_lowercase , padding_strategy=_lowercase , pad_to_multiple_of=_lowercase , return_attention_mask=_lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
snake_case_ : Optional[Any] = []
if value.dtype is np.dtype(np.floataa ):
snake_case_ : List[Any] = value.astype(np.floataa )
batch_outputs[key].append(_lowercase )
return BatchFeature(_lowercase , tensor_type=_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = PaddingStrategy.DO_NOT_PAD , _lowercase = None , _lowercase = None , ) -> dict:
'''simple docstring'''
snake_case_ : List[Any] = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
snake_case_ : Dict = len(_lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case_ : str = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case_ : str = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
snake_case_ : Any = np.ones(len(_lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
snake_case_ : Any = max_length - len(_lowercase )
if self.padding_side == "right":
if return_attention_mask:
snake_case_ : Any = np.pad(
processed_features["""attention_mask"""] , (0, difference) )
snake_case_ : List[Any] = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
snake_case_ : Dict = np.pad(
_lowercase , _lowercase , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
snake_case_ : Optional[Any] = np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
snake_case_ : Optional[Any] = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
snake_case_ : Optional[Any] = np.pad(
_lowercase , _lowercase , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , ) -> str:
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
snake_case_ : Union[str, Any] = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
snake_case_ : Union[str, Any] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
snake_case_ : List[str] = len(_lowercase ) > max_length
if needs_to_be_truncated:
snake_case_ : Tuple = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
snake_case_ : Any = processed_features["""attention_mask"""][:max_length]
return processed_features
def UpperCAmelCase__ ( self , _lowercase=False , _lowercase=None ) -> Tuple:
'''simple docstring'''
if padding is not False:
if padding is True:
snake_case_ : Any = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(_lowercase , _lowercase ):
snake_case_ : Union[str, Any] = PaddingStrategy(_lowercase )
elif isinstance(_lowercase , _lowercase ):
snake_case_ : Dict = padding
else:
snake_case_ : List[Any] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 715 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def is_in_circle(__UpperCamelCase : float , __UpperCamelCase : float ) -> bool:
snake_case_ : Dict = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
snake_case_ : Tuple = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
snake_case_ : Union[str, Any] = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Callable[[float], float] , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 , ):
'''simple docstring'''
return mean(
function_to_integrate(uniform(__UpperCamelCase , __UpperCamelCase ) ) for _ in range(__UpperCamelCase ) ) * (max_value - min_value)
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ):
'''simple docstring'''
def identity_function(__UpperCamelCase : float ) -> float:
return x
snake_case_ : int = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : str = (max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("""******************""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def function_to_integrate(__UpperCamelCase : float ) -> float:
return sqrt(4.0 - x * x )
snake_case_ : List[Any] = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
def decorator(__UpperCamelCase : str ):
snake_case_ : Dict = getattr(__UpperCamelCase , """handle_key""" , [] )
handle += [key]
setattr(__UpperCamelCase , """handle_key""" , __UpperCamelCase )
return func
return decorator
def __lowerCAmelCase ( *__UpperCamelCase : List[str] ):
'''simple docstring'''
def decorator(__UpperCamelCase : Optional[int] ):
snake_case_ : Optional[int] = getattr(__UpperCamelCase , """handle_key""" , [] )
handle += keys
setattr(__UpperCamelCase , """handle_key""" , __UpperCamelCase )
return func
return decorator
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __new__( cls , _lowercase , _lowercase , _lowercase ) -> List[str]:
'''simple docstring'''
snake_case_ : Any = super().__new__(cls , _lowercase , _lowercase , _lowercase )
if not hasattr(_lowercase , """key_handler""" ):
setattr(_lowercase , """key_handler""" , {} )
setattr(_lowercase , """handle_input""" , KeyHandler.handle_input )
for value in attrs.values():
snake_case_ : List[str] = getattr(_lowercase , """handle_key""" , [] )
for key in handled_keys:
snake_case_ : List[str] = value
return new_cls
@staticmethod
def UpperCAmelCase__ ( cls ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = get_character()
if char != KEYMAP["undefined"]:
snake_case_ : int = ord(_lowercase )
snake_case_ : Tuple = cls.key_handler.get(_lowercase )
if handler:
snake_case_ : Union[str, Any] = char
return handler(cls )
else:
return None
def __lowerCAmelCase ( cls : Optional[int] ):
'''simple docstring'''
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 716 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = nn.functional.normalize(__UpperCamelCase )
snake_case_ : Tuple = nn.functional.normalize(__UpperCamelCase )
return torch.mm(__UpperCamelCase , normalized_text_embeds.t() )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = CLIPConfig
_lowerCamelCase = ['''CLIPEncoderLayer''']
def __init__( self , _lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(_lowercase )
snake_case_ : Tuple = CLIPVisionModel(config.vision_config )
snake_case_ : int = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=_lowercase )
snake_case_ : Optional[Any] = nn.Parameter(torch.ones(1_7 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Dict = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=_lowercase )
snake_case_ : Any = nn.Parameter(torch.ones(1_7 ) , requires_grad=_lowercase )
snake_case_ : List[str] = nn.Parameter(torch.ones(3 ) , requires_grad=_lowercase )
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : int = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : str = self.visual_projection(_lowercase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
snake_case_ : Dict = cosine_distance(_lowercase , self.special_care_embeds ).cpu().float().numpy()
snake_case_ : List[str] = cosine_distance(_lowercase , self.concept_embeds ).cpu().float().numpy()
snake_case_ : Any = []
snake_case_ : Any = image_embeds.shape[0]
for i in range(_lowercase ):
snake_case_ : List[Any] = {"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : int = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
snake_case_ : List[str] = special_cos_dist[i][concept_idx]
snake_case_ : Union[str, Any] = self.special_care_embeds_weights[concept_idx].item()
snake_case_ : Tuple = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]} )
snake_case_ : Dict = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
snake_case_ : int = cos_dist[i][concept_idx]
snake_case_ : List[Any] = self.concept_embeds_weights[concept_idx].item()
snake_case_ : List[str] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(_lowercase )
result.append(_lowercase )
snake_case_ : Union[str, Any] = [len(res["""bad_concepts"""] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase__ ( self , _lowercase , _lowercase ) -> Tuple:
'''simple docstring'''
snake_case_ : Optional[Any] = self.vision_model(_lowercase )[1] # pooled_output
snake_case_ : List[str] = self.visual_projection(_lowercase )
snake_case_ : str = cosine_distance(_lowercase , self.special_care_embeds )
snake_case_ : Optional[int] = cosine_distance(_lowercase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
snake_case_ : Tuple = 0.0
snake_case_ : List[Any] = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
snake_case_ : str = torch.any(special_scores > 0 , dim=1 )
snake_case_ : List[str] = special_care * 0.01
snake_case_ : Optional[int] = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
snake_case_ : Optional[Any] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
snake_case_ : str = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 21 | 0 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
__lowerCAmelCase : Optional[Any] = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''
__lowerCAmelCase : Dict = '''
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results[\'matthews_correlation\'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results[\'matthews_correlation\'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results[\'matthews_correlation\'], 2))
-0.25
'''
__lowerCAmelCase : int = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase=None ) -> str:
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(_lowercase , _lowercase , sample_weight=_lowercase ) ),
}
| 717 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : List[str] = []
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
F'stage{idx}.patch_embed.proj.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
F'stage{idx}.patch_embed.proj.bias',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
F'stage{idx}.patch_embed.norm.weight',
) )
embed.append(
(
F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
F'stage{idx}.patch_embed.norm.bias',
) )
return embed
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : str = []
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
F'stage{idx}.blocks.{cnt}.attn.proj.weight',
) )
attention_weights.append(
(
F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
F'stage{idx}.blocks.{cnt}.attn.proj.bias',
) )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight') )
attention_weights.append(
(F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias') )
return attention_weights
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : int = []
token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""") )
return token
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Union[str, Any] = []
head.append(("""layernorm.weight""", """norm.weight""") )
head.append(("""layernorm.bias""", """norm.bias""") )
head.append(("""classifier.weight""", """head.weight""") )
head.append(("""classifier.bias""", """head.bias""") )
return head
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = """imagenet-1k-id2label.json"""
snake_case_ : Optional[Any] = 1_0_0_0
snake_case_ : Any = """huggingface/label-files"""
snake_case_ : Tuple = num_labels
snake_case_ : Dict = json.load(open(cached_download(hf_hub_url(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
snake_case_ : str = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : List[str] = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
snake_case_ : Dict = CvtConfig(num_labels=__UpperCamelCase , idalabel=__UpperCamelCase , labelaid=__UpperCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "13":
snake_case_ : Any = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("""/""" , 1 )[-1][4:6] == "21":
snake_case_ : Any = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
snake_case_ : Optional[int] = [2, 2, 2_0]
snake_case_ : str = [3, 1_2, 1_6]
snake_case_ : Any = [1_9_2, 7_6_8, 1_0_2_4]
snake_case_ : Union[str, Any] = CvtForImageClassification(__UpperCamelCase )
snake_case_ : str = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
snake_case_ : List[Any] = image_size
snake_case_ : str = torch.load(__UpperCamelCase , map_location=torch.device("""cpu""" ) )
snake_case_ : Any = OrderedDict()
snake_case_ : Tuple = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
snake_case_ : Optional[Any] = list_of_state_dict + cls_token(__UpperCamelCase )
snake_case_ : str = list_of_state_dict + embeddings(__UpperCamelCase )
for cnt in range(config.depth[idx] ):
snake_case_ : List[str] = list_of_state_dict + attention(__UpperCamelCase , __UpperCamelCase )
snake_case_ : str = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__UpperCamelCase )
for i in range(len(__UpperCamelCase ) ):
snake_case_ : Union[str, Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
image_processor.save_pretrained(__UpperCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--cvt_model''',
default='''cvt-w24''',
type=str,
help='''Name of the cvt model you\'d like to convert.''',
)
parser.add_argument(
'''--image_size''',
default=384,
type=int,
help='''Input Image Size''',
)
parser.add_argument(
'''--cvt_file_name''',
default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
type=str,
help='''Input Image Size''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__lowerCAmelCase : Dict = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 21 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
__lowerCAmelCase : str = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
'''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
'''junnyu/roformer_chinese_char_small''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
),
'''junnyu/roformer_chinese_char_base''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
),
'''junnyu/roformer_small_discriminator''': (
'''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
),
'''junnyu/roformer_small_generator''': (
'''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''roformer'''
def __init__( self , _lowercase=5_0_0_0_0 , _lowercase=None , _lowercase=7_6_8 , _lowercase=1_2 , _lowercase=1_2 , _lowercase=3_0_7_2 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1_5_3_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=1E-12 , _lowercase=0 , _lowercase=False , _lowercase=True , **_lowercase , ) -> Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=_lowercase , **_lowercase )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size if embedding_size is None else embedding_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : Any = num_attention_heads
snake_case_ : str = hidden_act
snake_case_ : Any = intermediate_size
snake_case_ : Tuple = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : Any = type_vocab_size
snake_case_ : Any = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = rotary_value
snake_case_ : List[Any] = use_cache
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case_ : List[str] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case_ : Any = {0: """batch""", 1: """sequence"""}
snake_case_ : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 718 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MgpstrTokenizer
_lowerCamelCase = False
_lowerCamelCase = {}
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# fmt: off
snake_case_ : Optional[Any] = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
snake_case_ : str = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowercase ) + """\n""" )
def UpperCAmelCase__ ( self , **_lowercase ) -> Union[str, Any]:
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = """tester"""
snake_case_ : Tuple = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ : Optional[Any] = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
snake_case_ : Union[str, Any] = tokenizer.encode([special_token] , add_special_tokens=_lowercase )
self.assertEqual(len(_lowercase ) , 1 )
snake_case_ : List[Any] = tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
snake_case_ , snake_case_ : Union[str, Any] = self.get_input_output_texts(_lowercase )
snake_case_ : List[Any] = tokenizer.tokenize(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.convert_tokens_to_ids(_lowercase )
snake_case_ : Union[str, Any] = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
snake_case_ : List[str] = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertNotEqual(len(_lowercase ) , 0 )
snake_case_ : str = tokenizer.decode(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(text_a.replace(""" """ , """""" ) , _lowercase )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
pass
| 21 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = SwinConfig(
embed_dim=1_9_2 , depths=(2, 2, 1_8, 2) , num_heads=(6, 1_2, 2_4, 4_8) , window_size=1_2 , out_features=["""stage2""", """stage3""", """stage4"""] , )
snake_case_ : Optional[Any] = DetaConfig(
backbone_config=__UpperCamelCase , num_queries=9_0_0 , encoder_ffn_dim=2_0_4_8 , decoder_ffn_dim=2_0_4_8 , num_feature_levels=5 , assign_first_stage=__UpperCamelCase , with_box_refine=__UpperCamelCase , two_stage=__UpperCamelCase , )
# set labels
snake_case_ : str = """huggingface/label-files"""
if "o365" in model_name:
snake_case_ : List[str] = 3_6_6
snake_case_ : Dict = """object365-id2label.json"""
else:
snake_case_ : Optional[Any] = 9_1
snake_case_ : int = """coco-detection-id2label.json"""
snake_case_ : Optional[Any] = num_labels
snake_case_ : int = json.load(open(cached_download(hf_hub_url(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
snake_case_ : Optional[int] = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
snake_case_ : Dict = idalabel
snake_case_ : Any = {v: k for k, v in idalabel.items()}
return config
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Tuple = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""") )
rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""") )
rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""") )
rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""") )
rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""") )
rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""") )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias') )
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias') )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias') )
# fmt: on
return rename_keys
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Optional[int] = dct.pop(__UpperCamelCase )
snake_case_ : Tuple = val
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Optional[Any] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
snake_case_ : Union[str, Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
snake_case_ : Optional[int] = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight' )
snake_case_ : Optional[int] = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Union[str, Any] = in_proj_weight[:dim, :]
snake_case_ : List[str] = in_proj_bias[: dim]
snake_case_ : str = in_proj_weight[
dim : dim * 2, :
]
snake_case_ : Any = in_proj_bias[
dim : dim * 2
]
snake_case_ : Any = in_proj_weight[
-dim :, :
]
snake_case_ : str = in_proj_bias[-dim :]
# fmt: on
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
snake_case_ : List[str] = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
snake_case_ : str = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
snake_case_ : Optional[Any] = in_proj_weight[:hidden_size, :]
snake_case_ : Union[str, Any] = in_proj_bias[:hidden_size]
snake_case_ : Tuple = in_proj_weight[
hidden_size : hidden_size * 2, :
]
snake_case_ : Union[str, Any] = in_proj_bias[hidden_size : hidden_size * 2]
snake_case_ : Union[str, Any] = in_proj_weight[-hidden_size:, :]
snake_case_ : List[Any] = in_proj_bias[-hidden_size:]
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : Optional[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case_ : Union[str, Any] = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = get_deta_config(__UpperCamelCase )
# load original state dict
if model_name == "deta-swin-large":
snake_case_ : Optional[Any] = hf_hub_download(repo_id="""nielsr/deta-checkpoints""" , filename="""adet_swin_ft.pth""" )
elif model_name == "deta-swin-large-o365":
snake_case_ : Any = hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""" , filename="""deta_swin_pt_o365.pth""" )
else:
raise ValueError(F'Model name {model_name} not supported' )
snake_case_ : int = torch.load(__UpperCamelCase , map_location="""cpu""" )["""model"""]
# original state dict
for name, param in state_dict.items():
print(__UpperCamelCase , param.shape )
# rename keys
snake_case_ : Union[str, Any] = create_rename_keys(__UpperCamelCase )
for src, dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
read_in_swin_q_k_v(__UpperCamelCase , config.backbone_config )
read_in_decoder_q_k_v(__UpperCamelCase , __UpperCamelCase )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
snake_case_ : Optional[int] = state_dict.pop(__UpperCamelCase )
snake_case_ : Tuple = val
if "input_proj" in key:
snake_case_ : Dict = state_dict.pop(__UpperCamelCase )
snake_case_ : List[Any] = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
snake_case_ : Optional[Any] = state_dict.pop(__UpperCamelCase )
snake_case_ : Union[str, Any] = val
# finally, create HuggingFace model and load state dict
snake_case_ : List[str] = DetaForObjectDetection(__UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
model.eval()
snake_case_ : int = """cuda""" if torch.cuda.is_available() else """cpu"""
model.to(__UpperCamelCase )
# load image processor
snake_case_ : Any = DetaImageProcessor(format="""coco_detection""" )
# verify our conversion on image
snake_case_ : Dict = prepare_img()
snake_case_ : str = processor(images=__UpperCamelCase , return_tensors="""pt""" )
snake_case_ : Optional[Any] = encoding["""pixel_values"""]
snake_case_ : int = model(pixel_values.to(__UpperCamelCase ) )
# verify logits
print("""Logits:""" , outputs.logits[0, :3, :3] )
print("""Boxes:""" , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
snake_case_ : Any = torch.tensor(
[[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]] )
snake_case_ : Optional[int] = torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]] )
elif model_name == "deta-swin-large-o365":
snake_case_ : Dict = torch.tensor(
[[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]] )
snake_case_ : Optional[Any] = torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(__UpperCamelCase ) , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(__UpperCamelCase ) , atol=1E-4 )
print("""Everything ok!""" )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
# Push to hub
if push_to_hub:
print("""Pushing model and processor to hub...""" )
model.push_to_hub(F'jozhang97/{model_name}' )
processor.push_to_hub(F'jozhang97/{model_name}' )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
type=str,
default='''deta-swin-large''',
choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
help='''Path to the folder to output PyTorch model.''',
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 719 |
"""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 _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=5 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : List[str] = batch_size
snake_case_ : int = seq_length
snake_case_ : List[Any] = is_training
snake_case_ : Optional[int] = use_attention_mask
snake_case_ : Optional[Any] = use_token_type_ids
snake_case_ : Union[str, Any] = use_labels
snake_case_ : str = vocab_size
snake_case_ : List[str] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Dict = intermediate_size
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : List[str] = max_position_embeddings
snake_case_ : Union[str, Any] = type_vocab_size
snake_case_ : str = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : str = num_choices
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Tuple = None
if self.use_attention_mask:
snake_case_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[int] = None
if self.use_token_type_ids:
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = 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=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ , snake_case_ : List[Any] = config_and_inputs
snake_case_ : Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = True
_lowerCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerModelTester(self )
@slow
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
snake_case_ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_lowercase )
snake_case_ : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
snake_case_ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
snake_case_ : Dict = model(_lowercase )[0]
snake_case_ : Optional[int] = 5_0_0_0_0
snake_case_ : Union[str, Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _lowercase )
snake_case_ : Dict = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _lowercase , atol=1E-4 ) )
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int = 1_0_0_0 ):
'''simple docstring'''
snake_case_ : Optional[int] = 2**power
snake_case_ : Dict = 0
while n:
snake_case_ : Optional[Any] = r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 720 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : int = 1_0
snake_case_ : Any = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
snake_case_ : Tuple = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(__UpperCamelCase ) ),
} , features=__UpperCamelCase , )
return dataset
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return filename
# FILE_CONTENT + files
__lowerCAmelCase : List[Any] = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
snake_case_ : Optional[Any] = FILE_CONTENT
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
import bza
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
snake_case_ : Any = bytes(__UpperCamelCase , """utf-8""" )
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
import gzip
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
snake_case_ : List[Any] = bytes(__UpperCamelCase , """utf-8""" )
with gzip.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
snake_case_ : Optional[Any] = bytes(__UpperCamelCase , """utf-8""" )
with lza.frame.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(__UpperCamelCase , """w""" ) as archive:
archive.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple ):
'''simple docstring'''
import tarfile
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
import lzma
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
snake_case_ : str = bytes(__UpperCamelCase , """utf-8""" )
with lzma.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
import zipfile
snake_case_ : Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
snake_case_ : Tuple = bytes(__UpperCamelCase , """utf-8""" )
with zstd.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
snake_case_ : List[str] = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase )
return filename
__lowerCAmelCase : List[str] = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__lowerCAmelCase : Tuple = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__lowerCAmelCase : int = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
__lowerCAmelCase : int = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
__lowerCAmelCase : Any = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[int] = datasets.Dataset.from_dict(__UpperCamelCase )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(__UpperCamelCase ) ) as con:
snake_case_ : Tuple = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : Optional[Any] = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(__UpperCamelCase , """w""" , newline="""""" ) as f:
snake_case_ : str = csv.DictWriter(__UpperCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : int ):
'''simple docstring'''
import bza
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(__UpperCamelCase , """rb""" ) as f:
snake_case_ : int = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__UpperCamelCase , """wb""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : int , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(__UpperCamelCase , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
snake_case_ : Any = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(__UpperCamelCase , """wb""" ) as f:
snake_case_ : Optional[int] = pq.ParquetWriter(__UpperCamelCase , schema=__UpperCamelCase )
snake_case_ : Optional[int] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__UpperCamelCase ) )] for k in DATA[0]} , schema=__UpperCamelCase )
writer.write_table(__UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : Any = {"""data""": DATA}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
snake_case_ : List[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(__UpperCamelCase ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
import gzip
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[str] ):
'''simple docstring'''
import gzip
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(__UpperCamelCase , """rb""" ) as orig_file:
with gzip.open(__UpperCamelCase , """wb""" ) as zipped_file:
zipped_file.writelines(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : Dict , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : List[str] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ):
'''simple docstring'''
snake_case_ : Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.add(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(__UpperCamelCase , """w""" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join("""nested""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : str = ["""0""", """1""", """2""", """3"""]
snake_case_ : Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : int = ["""0""", """1""", """2""", """3"""]
snake_case_ : int = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : List[Any] = ["""0""", """1""", """2""", """3"""]
snake_case_ : str = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(__UpperCamelCase , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict ):
'''simple docstring'''
snake_case_ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any , __UpperCamelCase : Optional[Any] ):
'''simple docstring'''
snake_case_ : Dict = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
f.write(__UpperCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(__UpperCamelCase ) ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(__UpperCamelCase , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : List[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
snake_case_ : List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(__UpperCamelCase )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( ):
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : Any = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(__UpperCamelCase , """w""" ) as f:
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ) )
f.write(__UpperCamelCase , arcname=os.path.basename(__UpperCamelCase ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 21 | 0 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
_lowerCamelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
_lowerCamelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
_lowerCamelCase = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ : Dict = self.task_name.lower()
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''train'''
_lowerCamelCase = '''dev'''
_lowerCamelCase = '''test'''
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = None , ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowercase , )
snake_case_ : List[Any] = args
snake_case_ : List[str] = glue_processors[args.task_name]()
snake_case_ : Union[str, Any] = glue_output_modes[args.task_name]
if isinstance(_lowercase , _lowercase ):
try:
snake_case_ : Optional[Any] = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
snake_case_ : Optional[int] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
snake_case_ : str = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
snake_case_ : List[Any] = label_list[2], label_list[1]
snake_case_ : Dict = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case_ : Optional[Any] = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
snake_case_ : str = time.time()
snake_case_ : Tuple = torch.load(_lowercase )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(f'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
snake_case_ : Any = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
snake_case_ : Optional[Any] = self.processor.get_test_examples(args.data_dir )
else:
snake_case_ : int = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
snake_case_ : str = examples[:limit_length]
snake_case_ : Tuple = glue_convert_examples_to_features(
_lowercase , _lowercase , max_length=args.max_seq_length , label_list=_lowercase , output_mode=self.output_mode , )
snake_case_ : List[str] = time.time()
torch.save(self.features , _lowercase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ) -> int:
'''simple docstring'''
return len(self.features )
def __getitem__( self , _lowercase ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return self.label_list
| 721 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ : List[str] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : Union[str, Any] = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
snake_case_ : int = max(len(__UpperCamelCase ) , len(__UpperCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase ) , b_binary.zfill(__UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : Optional[Any] = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''RobertaPreLayerNormConfig''',
'''RobertaPreLayerNormOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[int] = [
'''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:
__lowerCAmelCase : Optional[int] = [
'''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:
__lowerCAmelCase : str = [
'''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
__lowerCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 700 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int = 1_0_0_0 ):
'''simple docstring'''
snake_case_ : int = -1
snake_case_ : Tuple = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
snake_case_ : Optional[Any] = (n * n - 2 * a * n) // (2 * n - 2 * a)
snake_case_ : Dict = n - a - b
if c * c == (a * a + b * b):
snake_case_ : Tuple = a * b * c
if candidate >= product:
snake_case_ : Union[str, Any] = candidate
return product
if __name__ == "__main__":
print(F'''{solution() = }''')
| 701 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''nat'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=4 , _lowercase=3 , _lowercase=6_4 , _lowercase=[3, 4, 6, 5] , _lowercase=[2, 4, 8, 1_6] , _lowercase=7 , _lowercase=3.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=0.0 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : Any = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = embed_dim
snake_case_ : Tuple = depths
snake_case_ : int = len(_lowercase )
snake_case_ : Optional[int] = num_heads
snake_case_ : List[str] = kernel_size
snake_case_ : str = mlp_ratio
snake_case_ : str = qkv_bias
snake_case_ : str = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Tuple = drop_path_rate
snake_case_ : Dict = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Tuple = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Union[str, Any] = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Union[str, Any] = layer_scale_init_value
snake_case_ : Optional[Any] = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 21 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__lowerCAmelCase : str = logging.get_logger(__name__)
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = ['''pixel_values''']
def __init__( self , _lowercase = True , _lowercase = None , _lowercase = None , _lowercase = PILImageResampling.BILINEAR , _lowercase = True , _lowercase = 1 / 2_5_5 , _lowercase = True , _lowercase = None , _lowercase = None , **_lowercase , ) -> None:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : int = size if size is not None else {"""shortest_edge""": 3_8_4}
snake_case_ : Optional[Any] = get_size_dict(_lowercase , default_to_square=_lowercase )
snake_case_ : Any = do_resize
snake_case_ : int = size
# Default value set here for backwards compatibility where the value in config is None
snake_case_ : Any = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
snake_case_ : Optional[Any] = resample
snake_case_ : List[str] = do_rescale
snake_case_ : List[Any] = rescale_factor
snake_case_ : List[Any] = do_normalize
snake_case_ : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case_ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase = PILImageResampling.BICUBIC , _lowercase = None , **_lowercase , ) -> np.ndarray:
'''simple docstring'''
snake_case_ : Any = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
snake_case_ : List[Any] = size["""shortest_edge"""]
if shortest_edge < 3_8_4:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
snake_case_ : Any = int(shortest_edge / crop_pct )
snake_case_ : Optional[int] = get_resize_output_image_size(_lowercase , size=_lowercase , default_to_square=_lowercase )
snake_case_ : Union[str, Any] = resize(image=_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_lowercase , size=(shortest_edge, shortest_edge) , data_format=_lowercase , **_lowercase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_lowercase , size=(shortest_edge, shortest_edge) , resample=_lowercase , data_format=_lowercase , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase = None , **_lowercase , ) -> np.ndarray:
'''simple docstring'''
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = ChannelDimension.FIRST , **_lowercase , ) -> PIL.Image.Image:
'''simple docstring'''
snake_case_ : Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case_ : List[str] = crop_pct if crop_pct is not None else self.crop_pct
snake_case_ : str = resample if resample is not None else self.resample
snake_case_ : int = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
snake_case_ : Optional[int] = image_mean if image_mean is not None else self.image_mean
snake_case_ : Dict = image_std if image_std is not None else self.image_std
snake_case_ : int = size if size is not None else self.size
snake_case_ : int = get_size_dict(_lowercase , default_to_square=_lowercase )
snake_case_ : str = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
raise ValueError("""crop_pct must be specified if size < 384.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
snake_case_ : str = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
snake_case_ : Tuple = [self.resize(image=_lowercase , size=_lowercase , crop_pct=_lowercase , resample=_lowercase ) for image in images]
if do_rescale:
snake_case_ : int = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
snake_case_ : int = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
snake_case_ : List[str] = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
snake_case_ : int = {"""pixel_values""": images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 702 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__lowerCAmelCase : Optional[Any] = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return 1_2
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
return 3_2
@property
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : List[Any] = VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(_lowercase )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = 1_2
snake_case_ : Tuple = 1_2
snake_case_ : Tuple = {
"""attention_bias""": True,
"""cross_attention_dim""": 3_2,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 3_2,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
snake_case_ : Optional[Any] = TransformeraDModel(**_lowercase )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = """cpu"""
snake_case_ : List[str] = self.dummy_vqvae
snake_case_ : Any = self.dummy_text_encoder
snake_case_ : Tuple = self.dummy_tokenizer
snake_case_ : int = self.dummy_transformer
snake_case_ : int = VQDiffusionScheduler(self.num_embed )
snake_case_ : Dict = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase )
snake_case_ : Optional[Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : int = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : List[Any] = """teddy bear playing in the pool"""
snake_case_ : Dict = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : List[Any] = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Optional[int] = output.images
snake_case_ : List[Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Dict = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : List[Any] = image[0, -3:, -3:, -1]
snake_case_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : Dict = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
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 UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ : int = """cpu"""
snake_case_ : List[Any] = self.dummy_vqvae
snake_case_ : Optional[int] = self.dummy_text_encoder
snake_case_ : List[Any] = self.dummy_tokenizer
snake_case_ : Union[str, Any] = self.dummy_transformer
snake_case_ : str = VQDiffusionScheduler(self.num_embed )
snake_case_ : List[Any] = LearnedClassifierFreeSamplingEmbeddings(
learnable=_lowercase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ : Union[str, Any] = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
snake_case_ : Any = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case_ : Tuple = """teddy bear playing in the pool"""
snake_case_ : str = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Tuple = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="""np""" )
snake_case_ : Dict = output.images
snake_case_ : Union[str, Any] = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Any = pipe(
[prompt] , generator=_lowercase , output_type="""np""" , return_dict=_lowercase , num_inference_steps=2 )[0]
snake_case_ : Optional[Any] = image[0, -3:, -3:, -1]
snake_case_ : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
snake_case_ : int = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""" )
snake_case_ : str = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""" )
snake_case_ : Optional[Any] = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ : Any = torch.Generator(device=_lowercase ).manual_seed(0 )
snake_case_ : Optional[int] = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=_lowercase , output_type="""np""" , )
snake_case_ : Union[str, Any] = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 21 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Union[str, Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[str] = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 703 |
"""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 __lowerCAmelCase ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple , __UpperCamelCase : Any=0 ):
'''simple docstring'''
if name is None:
snake_case_ : Dict = None
else:
snake_case_ : Dict = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(5_0 - spaces ) + """s}"""
snake_case_ : Any = fmt.format(__UpperCamelCase )
# Print and recurse (if needed).
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if msg is not None:
print(__UpperCamelCase )
for k in val.keys():
recursive_print(__UpperCamelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCamelCase , torch.Tensor ):
print(__UpperCamelCase , """:""" , val.size() )
else:
print(__UpperCamelCase , """:""" , __UpperCamelCase )
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : str , __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : Any = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case_ : List[str] = (num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case_ : Tuple = param.view(*__UpperCamelCase )
snake_case_ : Tuple = param.transpose(0 , 2 )
snake_case_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case_ : Optional[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case_ : str = param.view(*__UpperCamelCase )
snake_case_ : Dict = param.transpose(0 , 1 ).contiguous()
snake_case_ : int = param.view(*__UpperCamelCase )
return param
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : Dict = {}
# old versions did not store training args
snake_case_ : List[str] = input_state_dict.get("""args""" , __UpperCamelCase )
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))
snake_case_ : Tuple = ds_args.padded_vocab_size
snake_case_ : Optional[int] = ds_args.max_position_embeddings
snake_case_ : Union[str, Any] = ds_args.hidden_size
snake_case_ : Union[str, Any] = ds_args.num_layers
snake_case_ : str = ds_args.num_attention_heads
snake_case_ : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case_ : Union[str, Any] = config.n_head
# The hidden_size per head.
snake_case_ : Optional[Any] = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case_ : Optional[Any] = input_state_dict["""checkpoint_version"""]
else:
snake_case_ : int = 0.0
# The model.
snake_case_ : List[str] = input_state_dict["""model"""]
# The language model.
snake_case_ : str = model["""language_model"""]
# The embeddings.
snake_case_ : Tuple = lm["""embedding"""]
# The word embeddings.
snake_case_ : List[str] = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
snake_case_ : Optional[int] = word_embeddings[: config.vocab_size, :]
snake_case_ : Optional[int] = word_embeddings
# The position embeddings.
snake_case_ : List[Any] = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
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.
snake_case_ : Union[str, Any] = pos_embeddings
# The transformer.
snake_case_ : Optional[Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
snake_case_ : Optional[Any] = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
snake_case_ : List[str] = {
"""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.
snake_case_ : int = layer_re.match(__UpperCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case_ : Tuple = int(m.group(1 ) )
# The name of the operation.
snake_case_ : Any = m.group(2 )
# Is it a weight or a bias?
snake_case_ : Union[str, Any] = m.group(3 )
# The name of the layer.
snake_case_ : str = F'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
snake_case_ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
snake_case_ : Optional[int] = 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.
snake_case_ : Optional[Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCamelCase , __UpperCamelCase )
snake_case_ : List[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case_ : str = torch.tensor(-1E4 , dtype=torch.floataa )
snake_case_ : List[Any] = masked_bias
snake_case_ : Optional[int] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case_ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case_ : Tuple = 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":
snake_case_ : Optional[Any] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Store. No change of shape.
snake_case_ : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case_ : Any = megatron_to_transformers[op_name]
snake_case_ : str = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case_ : List[str] = megatron_to_transformers[op_name]
snake_case_ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case_ : Dict = transformer["""final_layernorm.weight"""]
snake_case_ : Dict = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case_ : Optional[int] = word_embeddings
# It should be done!
return output_state_dict
def __lowerCAmelCase ( ):
'''simple docstring'''
snake_case_ : List[str] = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__UpperCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__UpperCamelCase , help="""An optional config json file describing the pre-trained model.""" , )
snake_case_ : str = parser.parse_args()
# Extract the basename.
snake_case_ : Optional[Any] = 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:
snake_case_ : Optional[int] = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
snake_case_ : List[Any] = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
snake_case_ : Any = input_state_dict.get("""args""" , __UpperCamelCase )
# 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:
snake_case_ : Any = """gelu_fast"""
elif ds_args.openai_gelu:
snake_case_ : Tuple = """gelu_new"""
else:
snake_case_ : List[str] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
snake_case_ : Dict = """gelu_new"""
# Spell out all parameters in case the defaults change.
snake_case_ : List[str] = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=__UpperCamelCase , 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=__UpperCamelCase , summary_activation=__UpperCamelCase , summary_proj_to_labels=__UpperCamelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCamelCase , use_cache=__UpperCamelCase , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
snake_case_ : List[Any] = GPTaConfig.from_json_file(args.config_file )
snake_case_ : int = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
snake_case_ : Tuple = convert_megatron_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCamelCase , __UpperCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case_ : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case_ : Optional[Any] = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
snake_case_ : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
else:
snake_case_ : List[str] = """gpt2"""
snake_case_ : List[Any] = AutoTokenizer.from_pretrained(__UpperCamelCase )
snake_case_ : List[str] = type(__UpperCamelCase ).__name__
snake_case_ : Optional[int] = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__UpperCamelCase )
# Save tokenizer based on args
print(F'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(__UpperCamelCase )
# Store the state_dict to file.
snake_case_ : List[Any] = os.path.join(__UpperCamelCase , """pytorch_model.bin""" )
print(F'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(__UpperCamelCase , __UpperCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 21 | 0 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : int = {
'''nvidia/segformer-b0-finetuned-ade-512-512''': (
'''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''segformer'''
def __init__( self , _lowercase=3 , _lowercase=4 , _lowercase=[2, 2, 2, 2] , _lowercase=[8, 4, 2, 1] , _lowercase=[3_2, 6_4, 1_6_0, 2_5_6] , _lowercase=[7, 3, 3, 3] , _lowercase=[4, 2, 2, 2] , _lowercase=[1, 2, 5, 8] , _lowercase=[4, 4, 4, 4] , _lowercase="gelu" , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase=0.02 , _lowercase=0.1 , _lowercase=1E-6 , _lowercase=2_5_6 , _lowercase=2_5_5 , **_lowercase , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_lowercase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , _lowercase , )
snake_case_ : Union[str, Any] = num_channels
snake_case_ : Optional[int] = num_encoder_blocks
snake_case_ : Union[str, Any] = depths
snake_case_ : Optional[int] = sr_ratios
snake_case_ : Dict = hidden_sizes
snake_case_ : str = patch_sizes
snake_case_ : Optional[int] = strides
snake_case_ : Union[str, Any] = mlp_ratios
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Dict = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = classifier_dropout_prob
snake_case_ : str = initializer_range
snake_case_ : Optional[int] = drop_path_rate
snake_case_ : Tuple = layer_norm_eps
snake_case_ : List[str] = decoder_hidden_size
snake_case_ : Optional[Any] = kwargs.get("""reshape_last_stage""" , _lowercase )
snake_case_ : List[str] = semantic_loss_ignore_index
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return 1_2
| 704 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : List[Any] = tf.cast(math.pi , x.dtype )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Tuple = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -1_0 , 1_0 )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=-1 ):
'''simple docstring'''
snake_case_ , snake_case_ : List[Any] = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
__lowerCAmelCase : int = tf.keras.activations.gelu
__lowerCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
__lowerCAmelCase : List[Any] = _gelu
__lowerCAmelCase : Any = _gelu_new
__lowerCAmelCase : Dict = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 21 | 0 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def is_in_circle(__UpperCamelCase : float , __UpperCamelCase : float ) -> bool:
snake_case_ : Dict = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
snake_case_ : Tuple = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
snake_case_ : Union[str, Any] = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : Callable[[float], float] , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 , ):
'''simple docstring'''
return mean(
function_to_integrate(uniform(__UpperCamelCase , __UpperCamelCase ) ) for _ in range(__UpperCamelCase ) ) * (max_value - min_value)
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ):
'''simple docstring'''
def identity_function(__UpperCamelCase : float ) -> float:
return x
snake_case_ : int = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
snake_case_ : str = (max_value * max_value - min_value * min_value) / 2
print("""******************""" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("""******************""" )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
def function_to_integrate(__UpperCamelCase : float ) -> float:
return sqrt(4.0 - x * x )
snake_case_ : List[Any] = area_under_curve_estimator(
__UpperCamelCase , __UpperCamelCase , 0.0 , 2.0 )
print("""******************""" )
print("""Estimating pi using area_under_curve_estimator""" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("""******************""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_case_ : int = [0] * len(__UpperCamelCase )
snake_case_ : List[str] = []
snake_case_ : Any = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__UpperCamelCase ) ):
if indegree[i] == 0:
queue.append(__UpperCamelCase )
while queue:
snake_case_ : Optional[int] = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
snake_case_ : Union[str, Any] = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__UpperCamelCase )
print(max(__UpperCamelCase ) )
# Adjacency list of Graph
__lowerCAmelCase : str = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 21 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''ibert'''
def __init__( self , _lowercase=3_0_5_2_2 , _lowercase=7_6_8 , _lowercase=1_2 , _lowercase=1_2 , _lowercase=3_0_7_2 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=2 , _lowercase=0.02 , _lowercase=1E-12 , _lowercase=1 , _lowercase=0 , _lowercase=2 , _lowercase="absolute" , _lowercase=False , _lowercase="none" , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
snake_case_ : Dict = vocab_size
snake_case_ : Tuple = hidden_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Tuple = hidden_act
snake_case_ : Tuple = intermediate_size
snake_case_ : int = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : Tuple = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : str = initializer_range
snake_case_ : Optional[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Union[str, Any] = quant_mode
snake_case_ : List[str] = force_dequant
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case_ : List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case_ : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 706 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n,k)
for i in range(__UpperCamelCase ):
result *= n - i
result //= i + 1
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return binomial_coefficient(2 * node_count , __UpperCamelCase ) // (node_count + 1)
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n < 0:
raise ValueError("""factorial() not defined for negative values""" )
snake_case_ : Optional[int] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return catalan_number(__UpperCamelCase ) * factorial(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
F'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 21 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase=1_3 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=9_9 , _lowercase=3_2 , _lowercase=2 , _lowercase=4 , _lowercase=3_7 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=5_1_2 , _lowercase=1_6 , _lowercase=2 , _lowercase=0.02 , _lowercase=False , _lowercase=True , _lowercase="None" , _lowercase=3 , _lowercase=4 , _lowercase=None , ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = parent
snake_case_ : Tuple = batch_size
snake_case_ : Tuple = seq_length
snake_case_ : List[str] = is_training
snake_case_ : Tuple = use_input_mask
snake_case_ : Dict = use_token_type_ids
snake_case_ : str = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Tuple = hidden_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Tuple = intermediate_size
snake_case_ : Tuple = hidden_act
snake_case_ : List[Any] = hidden_dropout_prob
snake_case_ : Optional[Any] = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : Any = type_vocab_size
snake_case_ : Any = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : str = num_labels
snake_case_ : Tuple = num_choices
snake_case_ : Dict = relative_attention
snake_case_ : List[Any] = position_biased_input
snake_case_ : Tuple = pos_att_type
snake_case_ : Dict = scope
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : int = None
if self.use_input_mask:
snake_case_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Optional[Any] = None
if self.use_token_type_ids:
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Optional[int] = None
snake_case_ : int = None
snake_case_ : Optional[Any] = None
if self.use_labels:
snake_case_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : Optional[int] = DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=_lowercase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : int = TFDebertaVaModel(config=_lowercase )
snake_case_ : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
snake_case_ : Any = [input_ids, input_mask]
snake_case_ : str = model(_lowercase )
snake_case_ : str = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Dict:
'''simple docstring'''
snake_case_ : int = TFDebertaVaForMaskedLM(config=_lowercase )
snake_case_ : Dict = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case_ : Tuple = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : Any = self.num_labels
snake_case_ : Any = TFDebertaVaForSequenceClassification(config=_lowercase )
snake_case_ : Tuple = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case_ : int = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
'''simple docstring'''
snake_case_ : int = self.num_labels
snake_case_ : Union[str, Any] = TFDebertaVaForTokenClassification(config=_lowercase )
snake_case_ : Optional[Any] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case_ : Any = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = TFDebertaVaForQuestionAnswering(config=_lowercase )
snake_case_ : Optional[int] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
snake_case_ : Optional[Any] = model(_lowercase )
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 UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Tuple = self.prepare_config_and_inputs()
(
snake_case_
) : str = config_and_inputs
snake_case_ : List[str] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowerCamelCase = (
{
'''feature-extraction''': TFDebertaVaModel,
'''fill-mask''': TFDebertaVaForMaskedLM,
'''question-answering''': TFDebertaVaForQuestionAnswering,
'''text-classification''': TFDebertaVaForSequenceClassification,
'''token-classification''': TFDebertaVaForTokenClassification,
'''zero-shot''': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
snake_case_ : int = TFDebertaVaModelTester(self )
snake_case_ : int = ConfigTester(self , config_class=_lowercase , hidden_size=3_7 )
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowercase )
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowercase )
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowercase )
@slow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" )
self.assertIsNotNone(_lowercase )
@require_tf
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip(reason="""Model not available yet""" )
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ : int = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge""" )
snake_case_ : Any = tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] )
snake_case_ : List[str] = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case_ : Optional[int] = model(_lowercase , attention_mask=_lowercase )[0]
snake_case_ : Any = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , _lowercase , atol=1E-4 )
| 707 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = '''swin'''
_lowerCamelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , _lowercase=2_2_4 , _lowercase=4 , _lowercase=3 , _lowercase=9_6 , _lowercase=[2, 2, 6, 2] , _lowercase=[3, 6, 1_2, 2_4] , _lowercase=7 , _lowercase=4.0 , _lowercase=True , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase="gelu" , _lowercase=False , _lowercase=0.02 , _lowercase=1E-5 , _lowercase=3_2 , _lowercase=None , _lowercase=None , **_lowercase , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**_lowercase )
snake_case_ : str = image_size
snake_case_ : int = patch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = embed_dim
snake_case_ : Optional[int] = depths
snake_case_ : Optional[int] = len(_lowercase )
snake_case_ : Optional[Any] = num_heads
snake_case_ : Optional[Any] = window_size
snake_case_ : Optional[Any] = mlp_ratio
snake_case_ : Optional[Any] = qkv_bias
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = drop_path_rate
snake_case_ : List[Any] = hidden_act
snake_case_ : str = use_absolute_embeddings
snake_case_ : str = layer_norm_eps
snake_case_ : Optional[Any] = initializer_range
snake_case_ : Any = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case_ : Tuple = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
snake_case_ : Tuple = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
snake_case_ , snake_case_ : Any = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1E-4
| 21 | 0 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : int = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : List[Any] = tf.cast(math.pi , x.dtype )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__UpperCamelCase , 3 )) ))
return x * cdf
def __lowerCAmelCase ( __UpperCamelCase : str ):
'''simple docstring'''
snake_case_ : Optional[Any] = tf.convert_to_tensor(__UpperCamelCase )
return x * tf.tanh(tf.math.softplus(__UpperCamelCase ) )
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
snake_case_ : str = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : int = tf.cast(0.044_715 , x.dtype )
snake_case_ : Optional[int] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __lowerCAmelCase ( __UpperCamelCase : List[str] ):
'''simple docstring'''
snake_case_ : Tuple = tf.convert_to_tensor(__UpperCamelCase )
snake_case_ : str = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
return tf.clip_by_value(_gelu(__UpperCamelCase ) , -1_0 , 1_0 )
def __lowerCAmelCase ( __UpperCamelCase : Optional[int] , __UpperCamelCase : List[str]=-1 ):
'''simple docstring'''
snake_case_ : List[Any] = tf.split(__UpperCamelCase , 2 , axis=__UpperCamelCase )
return a * tf.math.sigmoid(__UpperCamelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def __lowerCAmelCase ( __UpperCamelCase : List[Any] ):
'''simple docstring'''
return tf.keras.activations.gelu(__UpperCamelCase , approximate=__UpperCamelCase )
__lowerCAmelCase : int = tf.keras.activations.gelu
__lowerCAmelCase : Optional[Any] = approximate_gelu_wrap
else:
__lowerCAmelCase : List[Any] = _gelu
__lowerCAmelCase : Any = _gelu_new
__lowerCAmelCase : Dict = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def __lowerCAmelCase ( __UpperCamelCase : Any ):
'''simple docstring'''
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}' )
| 708 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
snake_case_ : List[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
snake_case_ : Dict = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
snake_case_ : List[str] = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
snake_case_ : Optional[Any] = shift_tokens_right(_lowercase , model.config.pad_token_id , model.config.decoder_start_token_id )
snake_case_ : Tuple = model(_lowercase , decoder_input_ids=_lowercase ).logits
snake_case_ : Tuple = optax.softmax_cross_entropy(_lowercase , onehot(_lowercase , logits.shape[-1] ) ).mean()
snake_case_ : List[str] = -(labels.shape[-1] * loss.item())
snake_case_ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 21 | 0 |
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