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'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
'BigBirdPegasusForCausalLM',
'BigBirdPegasusForConditionalGeneration',
'BigBirdPegasusForQuestionAnswering',
'BigBirdPegasusForSequenceClassification',
'BigBirdPegasusModel',
'BigBirdPegasusPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
import baseaa
def lowerCamelCase ( lowerCamelCase : str):
return baseaa.aaaencode(string.encode("""utf-8"""))
def lowerCamelCase ( lowerCamelCase : bytes):
return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
__magic_name__ = '#'
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : str ):
'''simple docstring'''
A_ : dict = {}
def _a ( self : List[str] ,_a : str ):
'''simple docstring'''
A_ : Optional[Any] = self._trie
for char in text:
if char not in trie:
A_ : Dict = {}
A_ : List[Any] = trie[char]
A_ : Optional[Any] = True
def _a ( self : Optional[int] ,_a : str ):
'''simple docstring'''
A_ : Union[str, Any] = self._trie
for char in prefix:
if char in trie:
A_ : Optional[Any] = trie[char]
else:
return []
return self._elements(_a )
def _a ( self : Dict ,_a : dict ):
'''simple docstring'''
A_ : Optional[Any] = []
for c, v in d.items():
A_ : List[str] = [""" """] if c == END else [(c + s) for s in self._elements(_a )]
result.extend(_a )
return tuple(_a )
__magic_name__ = Trie()
__magic_name__ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def lowerCamelCase ( lowerCamelCase : str):
A_ : str = trie.find_word(lowerCamelCase)
return tuple(string + word for word in suffixes)
def lowerCamelCase ( ):
print(autocomplete_using_trie("""de"""))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def lowerCamelCase ( lowerCamelCase : str):
# word like '180' or '身高' or '神'
for char in word:
A_ : Optional[Any] = ord(lowerCamelCase)
if not _is_chinese_char(lowerCamelCase):
return 0
return 1
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Any = set()
for token in tokens:
A_ : str = len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
if chinese_word:
word_set.add(lowerCamelCase)
A_ : Any = list(lowerCamelCase)
return word_list
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : set()):
if not chinese_word_set:
return bert_tokens
A_ : Any = max([len(lowerCamelCase) for w in chinese_word_set])
A_ : str = bert_tokens
A_ , A_ : Any = 0, len(lowerCamelCase)
while start < end:
A_ : Tuple = True
if is_chinese(bert_word[start]):
A_ : List[str] = min(end - start , lowerCamelCase)
for i in range(lowerCamelCase , 1 , -1):
A_ : Tuple = """""".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
A_ : Dict = """##""" + bert_word[j]
A_ : str = start + i
A_ : Dict = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : LTP , lowerCamelCase : BertTokenizer):
A_ : Union[str, Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : List[Any] = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""]).cws
A_ : int = [get_chinese_word(lowerCamelCase) for r in res]
ltp_res.extend(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : List[Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : Dict = bert_tokenizer(lines[i : i + 100] , add_special_tokens=lowerCamelCase , truncation=lowerCamelCase , max_length=512)
bert_res.extend(res["""input_ids"""])
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Union[str, Any] = []
for input_ids, chinese_word in zip(lowerCamelCase , lowerCamelCase):
A_ : List[Any] = []
for id in input_ids:
A_ : List[Any] = bert_tokenizer._convert_id_to_token(lowerCamelCase)
input_tokens.append(lowerCamelCase)
A_ : int = add_sub_symbol(lowerCamelCase , lowerCamelCase)
A_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCamelCase):
if token[:2] == "##":
A_ : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
ref_id.append(lowerCamelCase)
ref_ids.append(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
return ref_ids
def lowerCamelCase ( lowerCamelCase : Tuple):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""") as f:
A_ : Optional[int] = f.readlines()
A_ : Union[str, Any] = [line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ : Optional[Any] = LTP(args.ltp) # faster in GPU device
A_ : Dict = BertTokenizer.from_pretrained(args.bert)
A_ : str = prepare_ref(lowerCamelCase , lowerCamelCase , lowerCamelCase)
with open(args.save_path , """w""" , encoding="""utf-8""") as f:
A_ : Optional[Any] = [json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
f.writelines(lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = 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',
)
__magic_name__ = parser.parse_args()
main(args)
| 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['MLukeTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """ViltImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] ,_a : Optional[Any]=None ,_a : List[str]=None ,**_a : Any ):
'''simple docstring'''
A_ : 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.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[Any] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Any ,_a : Tuple ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
# add pixel_values + pixel_mask
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Any ,**_a : Any ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : int ,*_a : int ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : int ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__magic_name__ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Tuple ,_a : Optional[Any] ,_a : List[Any]=16 ,_a : int=13 ,_a : Dict=7 ,_a : Union[str, Any]=14 ,_a : Dict=10 ,_a : Union[str, Any]=19 ,_a : Dict=5 ,_a : Any=4 ,_a : Tuple=True ,_a : List[Any]=16 ,_a : Optional[int]=2 ,_a : Tuple=4 ,_a : Dict=4 ,_a : List[Any]="gelu" ,_a : Union[str, Any]=0.1 ,_a : str=0.1 ,_a : Any=[1, 2, 3, 4, 5] ,_a : Union[str, Any]=25 ,_a : List[Any]=5 ,):
'''simple docstring'''
A_ : Any = d_model
A_ : Optional[Any] = parent
A_ : Union[str, Any] = batch_size
A_ : int = prediction_length
A_ : str = context_length
A_ : Dict = cardinality
A_ : Optional[Any] = num_time_features
A_ : Optional[int] = lags_sequence
A_ : int = embedding_dimension
A_ : int = is_training
A_ : List[str] = hidden_size
A_ : Dict = num_hidden_layers
A_ : str = num_attention_heads
A_ : Dict = intermediate_size
A_ : int = hidden_act
A_ : str = hidden_dropout_prob
A_ : int = attention_probs_dropout_prob
A_ : List[Any] = context_length
A_ : Union[str, Any] = prediction_length + label_length
A_ : int = label_length
A_ : Dict = moving_average
A_ : int = autocorrelation_factor
def _a ( self : List[Any] ):
'''simple docstring'''
return AutoformerConfig(
d_model=self.d_model ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,prediction_length=self.prediction_length ,context_length=self.context_length ,label_length=self.label_length ,lags_sequence=self.lags_sequence ,num_time_features=self.num_time_features ,num_static_categorical_features=1 ,cardinality=[self.cardinality] ,embedding_dimension=[self.embedding_dimension] ,moving_average=self.moving_average ,)
def _a ( self : int ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = config.context_length + max(config.lags_sequence )
A_ : Optional[Any] = ids_tensor([self.batch_size, 1] ,config.cardinality[0] )
A_ : List[Any] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
A_ : Any = floats_tensor([self.batch_size, _past_length] )
A_ : Optional[int] = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
A_ : Tuple = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
A_ : Dict = floats_tensor([self.batch_size, config.prediction_length] )
A_ : Any = {
"""past_values""": past_values,
"""static_categorical_features""": static_categorical_features,
"""past_time_features""": past_time_features,
"""past_observed_mask""": past_observed_mask,
"""future_time_features""": future_time_features,
"""future_values""": future_values,
}
return inputs_dict
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[Any] = self.get_config()
A_ : int = self.prepare_autoformer_inputs_dict(_a )
return config, inputs_dict
def _a ( self : Dict ):
'''simple docstring'''
A_ , A_ : List[str] = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self : Dict ,_a : Dict ,_a : Dict ):
'''simple docstring'''
A_ : Optional[Any] = AutoformerModel(config=_a ).to(_a ).eval()
A_ : Optional[Any] = model(**_a )
A_ : Tuple = outputs.encoder_last_hidden_state
A_ : Any = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
A_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(_a )
A_ : Tuple = AutoformerEncoder.from_pretrained(_a ).to(_a )
A_ , A_ , A_ , A_ , A_ : List[Any] = model.create_network_inputs(**_a )
A_ , A_ : Any = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
A_ : str = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) ,dim=-1 ,)
A_ : int = encoder(inputs_embeds=_a )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
A_ : List[str] = (
torch.mean(transformer_inputs[:, : config.context_length, ...] ,dim=1 )
.unsqueeze(1 )
.repeat(1 ,config.prediction_length ,1 )
)
A_ : Optional[int] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] ,device=enc_input.device ,)
A_ : int = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) ,dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) ,dim=-1 ,)
A_ : List[str] = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) ,dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) ,dim=-1 ,)
with tempfile.TemporaryDirectory() as tmpdirname:
A_ : Tuple = model.get_decoder()
decoder.save_pretrained(_a )
A_ : Any = AutoformerDecoder.from_pretrained(_a ).to(_a )
A_ : Any = decoder(
trend=_a ,inputs_embeds=_a ,encoder_hidden_states=_a ,)[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a_ = (AutoformerForPrediction,) if is_torch_available() else ()
a_ = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
def _a ( self : str ):
'''simple docstring'''
A_ : List[str] = AutoformerModelTester(self )
A_ : Tuple = ConfigTester(self ,config_class=_a ,has_text_modality=_a )
def _a ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Dict ):
'''simple docstring'''
A_ , A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
A_ : List[str] = model_class(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_a )
A_ , A_ : Any = model_class.from_pretrained(_a ,output_loading_info=_a )
self.assertEqual(info["""missing_keys"""] ,[] )
def _a ( self : Any ):
'''simple docstring'''
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_a )
@unittest.skip(reason="""Model has no tokens embeddings""" )
def _a ( self : List[str] ):
'''simple docstring'''
pass
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : int = inspect.signature(getattr(_a ,"""forward""" ) )
# The main input is the name of the argument after `self`
A_ : str = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name ,_a )
def _a ( self : str ):
'''simple docstring'''
A_ , A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_a )
A_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Union[str, Any] = [*signature.parameters.keys()]
A_ : str = [
"""past_values""",
"""past_time_features""",
"""past_observed_mask""",
"""static_categorical_features""",
"""static_real_features""",
"""future_values""",
"""future_time_features""",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("""future_observed_mask""" )
expected_arg_names.extend(
[
"""decoder_attention_mask""",
"""head_mask""",
"""decoder_head_mask""",
"""cross_attn_head_mask""",
"""encoder_outputs""",
"""past_key_values""",
"""output_hidden_states""",
"""output_attentions""",
"""use_cache""",
"""return_dict""",
] )
self.assertListEqual(arg_names[: len(_a )] ,_a )
def _a ( self : Dict ):
'''simple docstring'''
A_ , A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
A_ : int = True
A_ : str = getattr(self.model_tester ,"""seq_length""" ,_a )
A_ : Union[str, Any] = getattr(self.model_tester ,"""decoder_seq_length""" ,_a )
A_ : int = getattr(self.model_tester ,"""encoder_seq_length""" ,_a )
A_ : Union[str, Any] = getattr(self.model_tester ,"""d_model""" ,_a )
A_ : List[str] = getattr(self.model_tester ,"""num_attention_heads""" ,_a )
A_ : Tuple = d_model // num_attention_heads
for model_class in self.all_model_classes:
A_ : Dict = True
A_ : int = False
A_ : Optional[int] = True
A_ : Any = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
A_ : Optional[Any] = model(**self._prepare_for_class(_a ,_a ) )
A_ : int = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A_ : Tuple = True
A_ : List[Any] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
A_ : Dict = model(**self._prepare_for_class(_a ,_a ) )
A_ : int = outputs.encoder_attentions
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,)
A_ : List[Any] = len(_a )
A_ : Dict = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(_a ,_a )
# decoder attentions
A_ : Any = outputs.decoder_attentions
self.assertIsInstance(_a ,(list, tuple) )
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,)
# cross attentions
A_ : Any = outputs.cross_attentions
self.assertIsInstance(_a ,(list, tuple) )
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, decoder_seq_length, dim] ,)
# Check attention is always last and order is fine
A_ : int = True
A_ : Dict = True
A_ : Dict = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
A_ : Any = model(**self._prepare_for_class(_a ,_a ) )
self.assertEqual(out_len + 2 ,len(_a ) )
A_ : List[str] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_a ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads, encoder_seq_length, dim] ,)
@is_flaky()
def _a ( self : str ):
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def lowerCamelCase ( lowerCamelCase : Tuple="train-batch.pt"):
A_ : Optional[int] = hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch""" , filename=lowerCamelCase , repo_type="""dataset""")
A_ : Optional[Any] = torch.load(lowerCamelCase , map_location=lowerCamelCase)
return batch
@require_torch
@slow
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self : Any ):
'''simple docstring'''
A_ : Optional[int] = AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(_a )
A_ : Optional[int] = prepare_batch()
with torch.no_grad():
A_ : Optional[Any] = model(
past_values=batch["""past_values"""] ,past_time_features=batch["""past_time_features"""] ,past_observed_mask=batch["""past_observed_mask"""] ,static_categorical_features=batch["""static_categorical_features"""] ,future_values=batch["""future_values"""] ,future_time_features=batch["""future_time_features"""] ,)[0]
A_ : int = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape ,_a )
A_ : Optional[Any] = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] ,device=_a )
self.assertTrue(torch.allclose(output[0, :3, :3] ,_a ,atol=_a ) )
def _a ( self : List[str] ):
'''simple docstring'''
A_ : str = AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(_a )
A_ : Optional[Any] = prepare_batch("""val-batch.pt""" )
with torch.no_grad():
A_ : Dict = model(
past_values=batch["""past_values"""] ,past_time_features=batch["""past_time_features"""] ,past_observed_mask=batch["""past_observed_mask"""] ,static_categorical_features=batch["""static_categorical_features"""] ,).encoder_last_hidden_state
A_ : List[str] = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape ,_a )
A_ : Union[str, Any] = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] ,device=_a )
self.assertTrue(torch.allclose(output[0, :3, :3] ,_a ,atol=_a ) )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(_a )
A_ : str = prepare_batch("""val-batch.pt""" )
with torch.no_grad():
A_ : Union[str, Any] = model.generate(
static_categorical_features=batch["""static_categorical_features"""] ,past_time_features=batch["""past_time_features"""] ,past_values=batch["""past_values"""] ,future_time_features=batch["""future_time_features"""] ,past_observed_mask=batch["""past_observed_mask"""] ,)
A_ : int = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape ,_a )
A_ : int = torch.tensor([3130.6763, 4056.5293, 7053.0786] ,device=_a )
A_ : Tuple = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] ,_a ,rtol=1e-1 ) )
| 27 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""torch""", """torchsde"""]
def __init__( self : Any ,*_a : Union[str, Any] ,**_a : Optional[int] ):
'''simple docstring'''
requires_backends(self ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : Optional[int] ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : List[Any] ,*_a : Tuple ,**_a : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
| 27 | 1 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = 'T5Config'
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """mt5"""
a_ = MTaConfig
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """mt5"""
a_ = MTaConfig
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """mt5"""
a_ = MTaConfig
| 27 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]="pt"):
A_ : Optional[int] = {"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase) and not line.startswith(""" """) else {}
A_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , ):
A_ : Dict = input_ids.ne(lowerCamelCase).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Dict ,_a : Tuple ,_a : Tuple="train" ,_a : Optional[int]=None ,_a : Any=None ,_a : int=None ,_a : Union[str, Any]="" ,):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = Path(_a ).joinpath(type_path + """.source""" )
A_ : Any = Path(_a ).joinpath(type_path + """.target""" )
A_ : Dict = self.get_char_lens(self.src_file )
A_ : Optional[int] = max_source_length
A_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A_ : List[Any] = tokenizer
A_ : Optional[Any] = prefix
if n_obs is not None:
A_ : Any = self.src_lens[:n_obs]
A_ : Optional[int] = src_lang
A_ : Tuple = tgt_lang
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : List[str] ,_a : Tuple ):
'''simple docstring'''
A_ : int = index + 1 # linecache starts at 1
A_ : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,_a ).rstrip("""\n""" )
A_ : Dict = linecache.getline(str(self.tgt_file ) ,_a ).rstrip("""\n""" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ : List[str] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_a ) else self.tokenizer
)
A_ : Any = self.tokenizer.generator if isinstance(self.tokenizer ,_a ) else self.tokenizer
A_ : Optional[int] = encode_line(_a ,_a ,self.max_source_length ,"""right""" )
A_ : Optional[int] = encode_line(_a ,_a ,self.max_target_length ,"""right""" )
A_ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
A_ : Dict = target_inputs["""input_ids"""].squeeze()
A_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( _a : int ):
'''simple docstring'''
return [len(_a ) for x in Path(_a ).open().readlines()]
def _a ( self : Optional[int] ,_a : Dict ):
'''simple docstring'''
A_ : str = torch.stack([x["""input_ids"""] for x in batch] )
A_ : Optional[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
A_ : str = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : List[str] = trim_batch(_a ,_a )
A_ , A_ : Union[str, Any] = trim_batch(_a ,_a ,attention_mask=_a )
A_ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__magic_name__ = getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[List]):
return list(itertools.chain.from_iterable(lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json"""))
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : List[str]=4 , **lowerCamelCase : List[str]):
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase) as f:
return json.load(lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[str] = git.Repo(search_parent_directories=lowerCamelCase)
A_ : Union[str, Any] = {
"""repo_id""": str(lowerCamelCase),
"""repo_sha""": str(repo.head.object.hexsha),
"""repo_branch""": str(repo.active_branch),
"""hostname""": str(socket.gethostname()),
}
return repo_infos
def lowerCamelCase ( lowerCamelCase : Callable , lowerCamelCase : Iterable):
return list(map(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
with open(lowerCamelCase , """wb""") as f:
return pickle.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str]):
def remove_articles(lowerCamelCase : Any):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , lowerCamelCase)
def white_space_fix(lowerCamelCase : List[Any]):
return " ".join(text.split())
def remove_punc(lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(lowerCamelCase : List[str]):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = normalize_answer(lowerCamelCase).split()
A_ : Dict = normalize_answer(lowerCamelCase).split()
A_ : int = Counter(lowerCamelCase) & Counter(lowerCamelCase)
A_ : Any = sum(common.values())
if num_same == 0:
return 0
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Any):
return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[str]):
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Any = 0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase):
em += exact_match_score(lowerCamelCase , lowerCamelCase)
if len(lowerCamelCase) > 0:
em /= len(lowerCamelCase)
return {"em": em}
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return model_prefix.startswith("""rag""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
A_ : Optional[Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ : Tuple = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase):
if not hasattr(lowerCamelCase , lowerCamelCase) and not hasattr(lowerCamelCase , equivalent_param[p]):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
continue
A_ : Tuple = p if hasattr(lowerCamelCase , lowerCamelCase) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
return hparams, config
| 27 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Dict ,_a : Union[str, Any] ,_a : Optional[Any]=13 ,_a : List[str]=30 ,_a : List[str]=2 ,_a : Dict=3 ,_a : Tuple=True ,_a : Optional[int]=True ,_a : Any=32 ,_a : List[str]=5 ,_a : Optional[int]=4 ,_a : List[Any]=37 ,_a : int="gelu" ,_a : int=0.1 ,_a : Optional[int]=0.1 ,_a : Union[str, Any]=10 ,_a : Tuple=0.02 ,):
'''simple docstring'''
A_ : Union[str, Any] = parent
A_ : Optional[Any] = batch_size
A_ : Optional[int] = image_size
A_ : List[Any] = patch_size
A_ : Optional[int] = num_channels
A_ : List[str] = is_training
A_ : Union[str, Any] = use_labels
A_ : int = hidden_size
A_ : Optional[int] = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Union[str, Any] = intermediate_size
A_ : Optional[int] = hidden_act
A_ : Tuple = hidden_dropout_prob
A_ : Optional[Any] = attention_probs_dropout_prob
A_ : int = type_sequence_label_size
A_ : List[str] = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A_ : Any = (image_size // patch_size) ** 2
A_ : Optional[Any] = num_patches + 1
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : str = ViTConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,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 ,is_decoder=_a ,initializer_range=self.initializer_range ,)
return config, pixel_values
def _a ( self : Optional[int] ,_a : Tuple ,_a : Tuple ):
'''simple docstring'''
A_ : Any = FlaxViTModel(config=_a )
A_ : str = model(_a )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
A_ : str = (self.image_size, self.image_size)
A_ : List[str] = (self.patch_size, self.patch_size)
A_ : Optional[int] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, num_patches + 1, self.hidden_size) )
def _a ( self : List[Any] ,_a : Any ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self.type_sequence_label_size
A_ : Tuple = FlaxViTForImageClassification(config=_a )
A_ : int = model(_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : List[str] = 1
A_ : str = FlaxViTForImageClassification(_a )
A_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : Any = model(_a )
def _a ( self : int ):
'''simple docstring'''
A_ : List[str] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) ,
) : int = config_and_inputs
A_ : str = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[str] = FlaxViTModelTester(self )
A_ : str = ConfigTester(self ,config_class=_a ,has_text_modality=_a ,hidden_size=37 )
def _a ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ , A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_a )
A_ : Optional[int] = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : List[str] = [*signature.parameters.keys()]
A_ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,_a )
def _a ( self : List[str] ):
'''simple docstring'''
A_ , A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
A_ : List[str] = self._prepare_for_class(_a ,_a )
A_ : Tuple = model_class(_a )
@jax.jit
def model_jitted(_a : Dict ,**_a : Dict ):
return model(pixel_values=_a ,**_a )
with self.subTest("""JIT Enabled""" ):
A_ : List[str] = model_jitted(**_a ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
A_ : Union[str, Any] = model_jitted(**_a ).to_tuple()
self.assertEqual(len(_a ) ,len(_a ) )
for jitted_output, output in zip(_a ,_a ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def _a ( self : List[str] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
A_ : int = model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
A_ : str = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(_a )
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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'''
a_ = KandinskyVaaControlnetPipeline
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a_ = False
@property
def _a ( self : Any ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return self.time_input_dim
@property
def _a ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 100
@property
def _a ( self : List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[Any] = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
A_ : Tuple = UNetaDConditionModel(**_a )
return model
@property
def _a ( self : List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Optional[Any] = self.dummy_unet
A_ : int = self.dummy_movq
A_ : Tuple = DDIMScheduler(
num_train_timesteps=1000 ,beta_schedule="""linear""" ,beta_start=0.00085 ,beta_end=0.012 ,clip_sample=_a ,set_alpha_to_one=_a ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=_a ,)
A_ : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _a ( self : Dict ,_a : str ,_a : Union[str, Any]=0 ):
'''simple docstring'''
A_ : Dict = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(_a ) ).to(_a )
A_ : int = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
_a )
# create hint
A_ : List[Any] = floats_tensor((1, 3, 64, 64) ,rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_a )
else:
A_ : str = torch.Generator(device=_a ).manual_seed(_a )
A_ : List[Any] = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[Any] = """cpu"""
A_ : List[str] = self.get_dummy_components()
A_ : Tuple = self.pipeline_class(**_a )
A_ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = pipe(**self.get_dummy_inputs(_a ) )
A_ : Tuple = output.images
A_ : Optional[Any] = pipe(
**self.get_dummy_inputs(_a ) ,return_dict=_a ,)[0]
A_ : Tuple = image[0, -3:, -3:, -1]
A_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : List[Any] = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
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 _a ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Any ):
'''simple docstring'''
A_ : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
A_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
A_ : Optional[int] = torch.from_numpy(np.array(_a ) ).float() / 255.0
A_ : List[Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
A_ : List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(_a )
A_ : Union[str, Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
A_ : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
A_ : Optional[Any] = """A robot, 4k photo"""
A_ : Any = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ , A_ : List[str] = pipe_prior(
_a ,generator=_a ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
A_ : int = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ : List[Any] = pipeline(
image_embeds=_a ,negative_image_embeds=_a ,hint=_a ,generator=_a ,num_inference_steps=100 ,output_type="""np""" ,)
A_ : Dict = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_a ,_a )
| 27 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """Pix2StructImageProcessor"""
a_ = ("""T5Tokenizer""", """T5TokenizerFast""")
def __init__( self : Union[str, Any] ,_a : List[str] ,_a : int ):
'''simple docstring'''
A_ : Tuple = False
super().__init__(_a ,_a )
def __call__( self : Tuple ,_a : Tuple=None ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : Optional[int] = 2048 ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Dict ,):
'''simple docstring'''
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None and not self.image_processor.is_vqa:
A_ : str = self.tokenizer
A_ : List[Any] = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_token_type_ids=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
A_ : Optional[int] = self.image_processor(
_a ,return_tensors=_a ,max_patches=_a ,**_a )
else:
# add pixel_values and bbox
A_ : List[str] = self.image_processor(
_a ,return_tensors=_a ,max_patches=_a ,header_text=_a ,**_a )
if text is not None and not self.image_processor.is_vqa:
A_ : Optional[Any] = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_token_type_ids=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
if "attention_mask" in text_encoding:
A_ : Union[str, Any] = text_encoding.pop("""attention_mask""" )
if "input_ids" in text_encoding:
A_ : Tuple = text_encoding.pop("""input_ids""" )
else:
A_ : Tuple = None
if text_encoding is not None:
encoding_image_processor.update(_a )
return encoding_image_processor
def _a ( self : Optional[int] ,*_a : List[str] ,**_a : str ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : Tuple ,*_a : Optional[int] ,**_a : Union[str, Any] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : str ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 27 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """deberta-v2"""
def __init__( self : Optional[Any] ,_a : Union[str, Any]=128100 ,_a : Optional[int]=1536 ,_a : Dict=24 ,_a : int=24 ,_a : Tuple=6144 ,_a : Union[str, Any]="gelu" ,_a : List[Any]=0.1 ,_a : Dict=0.1 ,_a : int=512 ,_a : int=0 ,_a : int=0.02 ,_a : int=1e-7 ,_a : List[str]=False ,_a : Union[str, Any]=-1 ,_a : List[Any]=0 ,_a : Optional[Any]=True ,_a : Tuple=None ,_a : Any=0 ,_a : int="gelu" ,**_a : Any ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : List[Any] = initializer_range
A_ : int = relative_attention
A_ : Tuple = max_relative_positions
A_ : int = pad_token_id
A_ : Tuple = position_biased_input
# Backwards compatibility
if type(_a ) == str:
A_ : str = [x.strip() for x in pos_att_type.lower().split("""|""" )]
A_ : Any = pos_att_type
A_ : Optional[int] = vocab_size
A_ : Tuple = layer_norm_eps
A_ : Any = kwargs.get("""pooler_hidden_size""" ,_a )
A_ : Union[str, Any] = pooler_dropout
A_ : List[Any] = pooler_hidden_act
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : Any = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
return 12
def _a ( self : int ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 3 ,_a : int = 40 ,_a : int = 40 ,_a : "PreTrainedTokenizerBase" = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(preprocessor=_a ,framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig',
'BlipTextConfig',
'BlipVisionConfig',
],
'processing_blip': ['BlipProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['BlipImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlipModel',
'BlipPreTrainedModel',
'BlipForConditionalGeneration',
'BlipForQuestionAnswering',
'BlipVisionModel',
'BlipTextModel',
'BlipForImageTextRetrieval',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFBlipModel',
'TFBlipPreTrainedModel',
'TFBlipForConditionalGeneration',
'TFBlipForQuestionAnswering',
'TFBlipVisionModel',
'TFBlipTextModel',
'TFBlipForImageTextRetrieval',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__magic_name__ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__magic_name__ = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
__magic_name__ = BeautifulSoup(res.text, 'html.parser')
__magic_name__ = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f"""https://google.com{link.get('href')}""")
| 27 | 1 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
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 (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Tuple ,_a : Optional[int] ,_a : Optional[int]=13 ,_a : Dict=7 ,_a : Any=True ,_a : List[Any]=True ,_a : Dict=False ,_a : List[str]=True ,_a : List[str]=99 ,_a : List[str]=32 ,_a : Optional[int]=5 ,_a : str=4 ,_a : Dict=64 ,_a : Optional[Any]="gelu" ,_a : Tuple=0.1 ,_a : Union[str, Any]=0.1 ,_a : Dict=512 ,_a : Tuple=16 ,_a : str=2 ,_a : str=0.02 ,_a : Optional[int]=3 ,_a : Union[str, Any]=4 ,_a : List[Any]=None ,_a : int=2 ,_a : List[str]=2 ,_a : Tuple=2 ,_a : Dict=2 ,_a : str=4 ,_a : Optional[Any]=1 ,):
'''simple docstring'''
A_ : Optional[int] = parent
A_ : int = batch_size
A_ : str = seq_length
A_ : Any = is_training
A_ : Dict = use_input_mask
A_ : int = use_token_type_ids
A_ : Optional[Any] = use_labels
A_ : str = vocab_size
A_ : Optional[Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : Tuple = intermediate_size
A_ : Any = hidden_act
A_ : Tuple = hidden_dropout_prob
A_ : str = attention_probs_dropout_prob
A_ : Optional[int] = max_position_embeddings
A_ : List[Any] = type_vocab_size
A_ : Tuple = type_sequence_label_size
A_ : Optional[int] = initializer_range
A_ : Any = num_labels
A_ : Dict = num_choices
A_ : List[Any] = scope
A_ : List[str] = q_groups
A_ : Optional[Any] = k_groups
A_ : int = v_groups
A_ : Optional[int] = post_attention_groups
A_ : Any = intermediate_groups
A_ : Optional[Any] = output_groups
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : Optional[int] = None
if self.use_input_mask:
A_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
A_ : Dict = None
A_ : Union[str, Any] = None
A_ : Any = None
if self.use_labels:
A_ : str = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Tuple = ids_tensor([self.batch_size] ,self.num_choices )
A_ : Union[str, Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ):
'''simple docstring'''
return SqueezeBertConfig(
embedding_size=self.hidden_size ,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 ,attention_probs_dropout_prob=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,q_groups=self.q_groups ,k_groups=self.k_groups ,v_groups=self.v_groups ,post_attention_groups=self.post_attention_groups ,intermediate_groups=self.intermediate_groups ,output_groups=self.output_groups ,)
def _a ( self : List[Any] ,_a : List[Any] ,_a : List[str] ,_a : Union[str, Any] ,_a : str ,_a : str ,_a : Tuple ):
'''simple docstring'''
A_ : List[Any] = SqueezeBertModel(config=_a )
model.to(_a )
model.eval()
A_ : Tuple = model(_a ,_a )
A_ : Any = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] ,_a : List[Any] ,_a : int ,_a : Dict ,_a : Tuple ,_a : Dict ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : Tuple = SqueezeBertForMaskedLM(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,attention_mask=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Any ,_a : int ,_a : str ,_a : str ,_a : List[str] ,_a : List[Any] ,_a : Tuple ):
'''simple docstring'''
A_ : Optional[int] = SqueezeBertForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
A_ : Dict = model(
_a ,attention_mask=_a ,start_positions=_a ,end_positions=_a )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _a ( self : Tuple ,_a : Any ,_a : Union[str, Any] ,_a : int ,_a : List[Any] ,_a : int ,_a : int ):
'''simple docstring'''
A_ : Any = self.num_labels
A_ : Union[str, Any] = SqueezeBertForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : List[Any] = model(_a ,attention_mask=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _a ( self : str ,_a : str ,_a : Tuple ,_a : Union[str, Any] ,_a : Dict ,_a : str ,_a : Optional[int] ):
'''simple docstring'''
A_ : str = self.num_labels
A_ : Dict = SqueezeBertForTokenClassification(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,attention_mask=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : Optional[Any] ,_a : Dict ,_a : Union[str, Any] ,_a : Dict ,_a : List[str] ,_a : int ,_a : Any ):
'''simple docstring'''
A_ : int = self.num_choices
A_ : Optional[Any] = SqueezeBertForMultipleChoice(config=_a )
model.to(_a )
model.eval()
A_ : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : List[str] = model(
_a ,attention_mask=_a ,labels=_a ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Tuple = self.prepare_config_and_inputs()
((A_) , (A_) , (A_) , (A_) , (A_) , (A_)) : List[str] = config_and_inputs
A_ : List[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'''
a_ = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
a_ = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
a_ = False
a_ = True
a_ = False
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = SqueezeBertModelTester(self )
A_ : int = ConfigTester(self ,config_class=_a ,dim=37 )
def _a ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*_a )
def _a ( self : Dict ):
'''simple docstring'''
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*_a )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_a )
def _a ( self : Dict ):
'''simple docstring'''
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_a )
@slow
def _a ( self : str ):
'''simple docstring'''
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Dict = SqueezeBertModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_sentencepiece
@require_tokenizers
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
A_ : List[str] = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
A_ : Union[str, Any] = model(_a )[0]
A_ : int = torch.Size((1, 3) )
self.assertEqual(output.shape ,_a )
A_ : int = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(_a ,_a ,atol=1e-4 ) )
| 27 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = """nezha"""
def __init__( self : int ,_a : Union[str, Any]=21128 ,_a : int=768 ,_a : Any=12 ,_a : List[str]=12 ,_a : str=3072 ,_a : int="gelu" ,_a : int=0.1 ,_a : str=0.1 ,_a : Tuple=512 ,_a : List[Any]=64 ,_a : Dict=2 ,_a : List[Any]=0.02 ,_a : Optional[Any]=1e-12 ,_a : List[Any]=0.1 ,_a : Union[str, Any]=0 ,_a : Any=2 ,_a : Union[str, Any]=3 ,_a : int=True ,**_a : int ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Dict = max_position_embeddings
A_ : Optional[Any] = max_relative_position
A_ : List[Any] = type_vocab_size
A_ : int = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Dict = classifier_dropout
A_ : int = use_cache
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list[int]):
if not numbers:
return 0
if not isinstance(lowerCamelCase , (list, tuple)) or not all(
isinstance(lowerCamelCase , lowerCamelCase) for number in numbers):
raise ValueError("""numbers must be an iterable of integers""")
A_ : str = numbers[0]
for i in range(1 , len(lowerCamelCase)):
# update the maximum and minimum subarray products
A_ : Any = numbers[i]
if number < 0:
A_ , A_ : Optional[int] = min_till_now, max_till_now
A_ : int = max(lowerCamelCase , max_till_now * number)
A_ : Optional[Any] = min(lowerCamelCase , min_till_now * number)
# update the maximum product found till now
A_ : int = max(lowerCamelCase , lowerCamelCase)
return max_prod
| 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] = stack.pop()
explored.add(lowerCamelCase)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(lowerCamelCase)
return explored
__magic_name__ = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 27 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : int , lowerCamelCase : Dict , lowerCamelCase : Union[str, Any] , lowerCamelCase : Any):
# load base model
A_ : Union[str, Any] = StableDiffusionPipeline.from_pretrained(lowerCamelCase , torch_dtype=torch.floataa)
# load LoRA weight from .safetensors
A_ : int = load_file(lowerCamelCase)
A_ : str = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
A_ : int = key.split(""".""")[0].split(LORA_PREFIX_TEXT_ENCODER + """_""")[-1].split("""_""")
A_ : Optional[Any] = pipeline.text_encoder
else:
A_ : Optional[Any] = key.split(""".""")[0].split(LORA_PREFIX_UNET + """_""")[-1].split("""_""")
A_ : Dict = pipeline.unet
# find the target layer
A_ : List[str] = layer_infos.pop(0)
while len(lowerCamelCase) > -1:
try:
A_ : int = curr_layer.__getattr__(lowerCamelCase)
if len(lowerCamelCase) > 0:
A_ : int = layer_infos.pop(0)
elif len(lowerCamelCase) == 0:
break
except Exception:
if len(lowerCamelCase) > 0:
temp_name += "_" + layer_infos.pop(0)
else:
A_ : List[Any] = layer_infos.pop(0)
A_ : Optional[Any] = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up"""))
pair_keys.append(lowerCamelCase)
else:
pair_keys.append(lowerCamelCase)
pair_keys.append(key.replace("""lora_up""" , """lora_down"""))
# update weight
if len(state_dict[pair_keys[0]].shape) == 4:
A_ : Union[str, Any] = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.floataa)
A_ : str = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.floataa)
curr_layer.weight.data += alpha * torch.mm(lowerCamelCase , lowerCamelCase).unsqueeze(2).unsqueeze(3)
else:
A_ : int = state_dict[pair_keys[0]].to(torch.floataa)
A_ : str = state_dict[pair_keys[1]].to(torch.floataa)
curr_layer.weight.data += alpha * torch.mm(lowerCamelCase , lowerCamelCase)
# update visited list
for item in pair_keys:
visited.append(lowerCamelCase)
return pipeline
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument(
'--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
)
parser.add_argument(
'--lora_prefix_text_encoder',
default='lora_te',
type=str,
help='The prefix of text encoder weight in safetensors',
)
parser.add_argument('--alpha', default=0.7_5, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
parser.add_argument(
'--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
)
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
__magic_name__ = parser.parse_args()
__magic_name__ = args.base_model_path
__magic_name__ = args.checkpoint_path
__magic_name__ = args.dump_path
__magic_name__ = args.lora_prefix_unet
__magic_name__ = args.lora_prefix_text_encoder
__magic_name__ = args.alpha
__magic_name__ = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
__magic_name__ = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : List[str] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
A_ : Union[str, Any] = [144, 192, 240]
A_ : int = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
A_ : List[str] = [96, 120, 144]
A_ : Any = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
A_ : Any = [64, 80, 96]
A_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
A_ : Any = 0.05
A_ : List[Any] = 2.0
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : int = 512
A_ : Optional[int] = 16
A_ : List[Any] = 21
A_ : List[str] = """pascal-voc-id2label.json"""
else:
A_ : str = 1000
A_ : Any = """imagenet-1k-id2label.json"""
A_ : Any = """huggingface/label-files"""
A_ : List[str] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""") , """r"""))
A_ : str = {int(lowerCamelCase): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int=False):
for i in range(1 , 6):
if F'layer_{i}.' in name:
A_ : Tuple = name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.')
if "conv_1." in name:
A_ : Union[str, Any] = name.replace("""conv_1.""" , """conv_stem.""")
if ".block." in name:
A_ : Optional[Any] = name.replace(""".block.""" , """.""")
if "exp_1x1" in name:
A_ : Union[str, Any] = name.replace("""exp_1x1""" , """expand_1x1""")
if "red_1x1" in name:
A_ : int = name.replace("""red_1x1""" , """reduce_1x1""")
if ".local_rep.conv_3x3." in name:
A_ : List[str] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""")
if ".local_rep.conv_1x1." in name:
A_ : Optional[int] = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""")
if ".norm." in name:
A_ : Tuple = name.replace(""".norm.""" , """.normalization.""")
if ".conv." in name:
A_ : List[Any] = name.replace(""".conv.""" , """.convolution.""")
if ".conv_proj." in name:
A_ : str = name.replace(""".conv_proj.""" , """.conv_projection.""")
for i in range(0 , 2):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Tuple = name.replace(F'.{i}.{j}.' , F'.{i}.layer.{j}.')
for i in range(2 , 6):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Dict = name.replace(F'.{i}.{j}.' , F'.{i}.')
if "expand_1x1" in name:
A_ : Union[str, Any] = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""")
if "conv_3x3" in name:
A_ : str = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""")
if "reduce_1x1" in name:
A_ : Union[str, Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""")
for i in range(2 , 5):
if F'.global_rep.{i}.weight' in name:
A_ : List[Any] = name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight""")
if F'.global_rep.{i}.bias' in name:
A_ : Optional[int] = name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias""")
if ".global_rep." in name:
A_ : Optional[Any] = name.replace(""".global_rep.""" , """.transformer.""")
if ".pre_norm_mha.0." in name:
A_ : int = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""")
if ".pre_norm_mha.1.out_proj." in name:
A_ : Dict = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""")
if ".pre_norm_ffn.0." in name:
A_ : Dict = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""")
if ".pre_norm_ffn.1." in name:
A_ : Any = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""")
if ".pre_norm_ffn.4." in name:
A_ : Union[str, Any] = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""")
if ".transformer." in name:
A_ : Any = name.replace(""".transformer.""" , """.transformer.layer.""")
if ".aspp_layer." in name:
A_ : int = name.replace(""".aspp_layer.""" , """.""")
if ".aspp_pool." in name:
A_ : Tuple = name.replace(""".aspp_pool.""" , """.""")
if "seg_head." in name:
A_ : Optional[int] = name.replace("""seg_head.""" , """segmentation_head.""")
if "segmentation_head.classifier.classifier." in name:
A_ : List[str] = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""")
if "classifier.fc." in name:
A_ : str = name.replace("""classifier.fc.""" , """classifier.""")
elif (not base_model) and ("segmentation_head." not in name):
A_ : str = """mobilevit.""" + name
return name
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False):
if base_model:
A_ : Dict = """"""
else:
A_ : Any = """mobilevit."""
for key in orig_state_dict.copy().keys():
A_ : List[Any] = orig_state_dict.pop(lowerCamelCase)
if key[:8] == "encoder.":
A_ : int = key[8:]
if "qkv" in key:
A_ : Any = key.split(""".""")
A_ : str = int(key_split[0][6:]) - 1
A_ : int = int(key_split[3])
A_ : Optional[Any] = model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}')
A_ : Tuple = layer.transformer.layer[transformer_num].attention.attention.all_head_size
A_ : Optional[Any] = (
F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
)
if "weight" in key:
A_ : Dict = val[:dim, :]
A_ : Optional[int] = val[dim : dim * 2, :]
A_ : List[Any] = val[-dim:, :]
else:
A_ : Optional[Any] = val[:dim]
A_ : List[Any] = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : List[str] = val
return orig_state_dict
def lowerCamelCase ( ):
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase).raw)
return im
@torch.no_grad()
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : int=False):
A_ : Optional[Any] = get_mobilevit_config(lowerCamelCase)
# load original state_dict
A_ : List[Any] = torch.load(lowerCamelCase , map_location="""cpu""")
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : List[str] = MobileViTForSemanticSegmentation(lowerCamelCase).eval()
else:
A_ : str = MobileViTForImageClassification(lowerCamelCase).eval()
A_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase)
model.load_state_dict(lowerCamelCase)
# Check outputs on an image, prepared by MobileViTImageProcessor
A_ : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32)
A_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
A_ : List[Any] = model(**lowerCamelCase)
A_ : Dict = outputs.logits
if mobilevit_name.startswith("""deeplabv3_"""):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
A_ : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xs":
A_ : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
A_ : Tuple = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4)
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
A_ : Tuple = torch.tensor([-0.9866, 0.2392, -1.1241])
elif mobilevit_name == "mobilevit_xs":
A_ : Any = torch.tensor([-2.4761, -0.9399, -1.9587])
elif mobilevit_name == "mobilevit_xxs":
A_ : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4)
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowerCamelCase)
print(F'Saving image processor to {pytorch_dump_folder_path}')
image_processor.save_pretrained(lowerCamelCase)
if push_to_hub:
A_ : str = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""")
A_ : Union[str, Any] = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowerCamelCase , organization="""apple""")
model.push_to_hub(lowerCamelCase , organization="""apple""")
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
__magic_name__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 100):
A_ : Any = n * (n + 1) * (2 * n + 1) / 6
A_ : str = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__magic_name__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : Optional[Any] ,_a : bool = True ,_a : Dict[str, int] = None ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : bool = True ,_a : Dict[str, int] = None ,_a : bool = True ,_a : Union[int, float] = 1 / 255 ,_a : bool = True ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = True ,**_a : Dict ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Tuple = size if size is not None else {"""shortest_edge""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
A_ : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a ,param_name="""crop_size""" )
A_ : Any = do_resize
A_ : List[str] = size
A_ : Union[str, Any] = resample
A_ : Dict = do_center_crop
A_ : List[str] = crop_size
A_ : Any = do_rescale
A_ : Union[str, Any] = rescale_factor
A_ : Any = do_normalize
A_ : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A_ : List[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
A_ : Tuple = do_convert_rgb
def _a ( self : Optional[int] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[Any] ,):
'''simple docstring'''
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A_ : Tuple = get_resize_output_image_size(_a ,size=size["""shortest_edge"""] ,default_to_square=_a )
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def _a ( self : List[Any] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[int] ,):
'''simple docstring'''
A_ : Optional[int] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_a ,size=(size["""height"""], size["""width"""]) ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[int, float] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Any ,):
'''simple docstring'''
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[float, List[float]] ,_a : Union[float, List[float]] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[str] ,):
'''simple docstring'''
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def _a ( self : Optional[Any] ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : int = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[ChannelDimension] = ChannelDimension.FIRST ,**_a : int ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : Tuple = size if size is not None else self.size
A_ : Optional[int] = get_size_dict(_a ,param_name="""size""" ,default_to_square=_a )
A_ : List[str] = resample if resample is not None else self.resample
A_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : Any = crop_size if crop_size is not None else self.crop_size
A_ : int = get_size_dict(_a ,param_name="""crop_size""" ,default_to_square=_a )
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : Any = do_normalize if do_normalize is not None else self.do_normalize
A_ : int = image_mean if image_mean is not None else self.image_mean
A_ : int = image_std if image_std is not None else self.image_std
A_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A_ : int = make_list_of_images(_a )
if not valid_images(_a ):
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.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A_ : Optional[int] = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
A_ : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
A_ : int = [self.resize(image=_a ,size=_a ,resample=_a ) for image in images]
if do_center_crop:
A_ : Tuple = [self.center_crop(image=_a ,size=_a ) for image in images]
if do_rescale:
A_ : List[str] = [self.rescale(image=_a ,scale=_a ) for image in images]
if do_normalize:
A_ : Any = [self.normalize(image=_a ,mean=_a ,std=_a ) for image in images]
A_ : List[str] = [to_channel_dimension_format(_a ,_a ) for image in images]
A_ : List[str] = {"""pixel_values""": images}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
__magic_name__ = trt.Logger(trt.Logger.WARNING)
__magic_name__ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
__magic_name__ = logging.getLogger(__name__)
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=384,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=128,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
__magic_name__ = parser.parse_args()
if args.tokenizer_name:
__magic_name__ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
__magic_name__ = args.per_device_eval_batch_size
__magic_name__ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
__magic_name__ = True
__magic_name__ = 'temp_engine/bert-fp32.engine'
if args.fpaa:
__magic_name__ = 'temp_engine/bert-fp16.engine'
if args.inta:
__magic_name__ = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
__magic_name__ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
__magic_name__ = [network.get_input(i) for i in range(network.num_inputs)]
__magic_name__ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
__magic_name__ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
__magic_name__ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
__magic_name__ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[str]):
A_ : str = np.asarray(inputs["""input_ids"""] , dtype=np.intaa)
A_ : int = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa)
A_ : Optional[int] = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa)
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCamelCase)
# start time
A_ : List[Any] = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCamelCase) for d_inp in d_inputs] + [int(lowerCamelCase), int(lowerCamelCase)] , stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Synchronize the stream and take time
stream.synchronize()
# end time
A_ : str = time.time()
A_ : Tuple = end_time - start_time
A_ : Any = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__magic_name__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__magic_name__ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
__magic_name__ = raw_datasets['validation'].column_names
__magic_name__ = 'question' if 'question' in column_names else column_names[0]
__magic_name__ = 'context' if 'context' in column_names else column_names[1]
__magic_name__ = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
__magic_name__ = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
)
__magic_name__ = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCamelCase ( lowerCamelCase : Dict):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
A_ : List[Any] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
A_ : Optional[int] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=lowerCamelCase , stride=args.doc_stride , return_overflowing_tokens=lowerCamelCase , return_offsets_mapping=lowerCamelCase , padding="""max_length""" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
A_ : List[str] = tokenized_examples.pop("""overflow_to_sample_mapping""")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
A_ : Union[str, Any] = []
for i in range(len(tokenized_examples["""input_ids"""])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
A_ : Any = tokenized_examples.sequence_ids(lowerCamelCase)
A_ : Tuple = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
A_ : Union[str, Any] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["""id"""][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
A_ : Dict = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
]
return tokenized_examples
__magic_name__ = raw_datasets['validation']
# Validation Feature Creation
__magic_name__ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
__magic_name__ = default_data_collator
__magic_name__ = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__magic_name__ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any]="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
A_ : Tuple = postprocess_qa_predictions(
examples=lowerCamelCase , features=lowerCamelCase , predictions=lowerCamelCase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCamelCase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
A_ : Dict = [
{"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
]
else:
A_ : Union[str, Any] = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]
A_ : Any = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCamelCase , label_ids=lowerCamelCase)
__magic_name__ = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize
# Allocate device memory for inputs and outputs.
__magic_name__ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
__magic_name__ = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(f""" Num examples = {len(eval_dataset)}""")
logger.info(f""" Batch size = {args.per_device_eval_batch_size}""")
__magic_name__ = 0.0
__magic_name__ = 0
__magic_name__ = timeit.default_timer()
__magic_name__ = None
for step, batch in enumerate(eval_dataloader):
__magic_name__ , __magic_name__ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
__magic_name__ , __magic_name__ = outputs
__magic_name__ = torch.tensor(start_logits)
__magic_name__ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
__magic_name__ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
__magic_name__ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
__magic_name__ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
__magic_name__ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
__magic_name__ = nested_truncate(all_preds, len(eval_dataset))
__magic_name__ = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_000))
logger.info('Total Number of Inference = %d', niter)
__magic_name__ = post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")
| 27 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,*_a : Optional[Any] ,**_a : Optional[int] ):
'''simple docstring'''
warnings.warn(
"""The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use OwlViTImageProcessor instead.""" ,_a ,)
super().__init__(*_a ,**_a )
| 27 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
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'''
a_ = 1
@register_to_config
def __init__( self : str ,_a : Dict=2000 ,_a : Optional[Any]=0.1 ,_a : List[str]=20 ,_a : Tuple=1e-3 ):
'''simple docstring'''
A_ : List[str] = None
A_ : Tuple = None
A_ : Any = None
def _a ( self : int ,_a : Optional[int] ,_a : Union[str, torch.device] = None ):
'''simple docstring'''
A_ : int = torch.linspace(1 ,self.config.sampling_eps ,_a ,device=_a )
def _a ( self : int ,_a : str ,_a : Optional[int] ,_a : str ,_a : List[Any]=None ):
'''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
A_ : str = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
A_ : Union[str, Any] = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
A_ : Any = std.flatten()
while len(std.shape ) < len(score.shape ):
A_ : List[str] = std.unsqueeze(-1 )
A_ : Dict = -score / std
# compute
A_ : Dict = -1.0 / len(self.timesteps )
A_ : Optional[int] = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
A_ : Optional[int] = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
A_ : str = beta_t.unsqueeze(-1 )
A_ : int = -0.5 * beta_t * x
A_ : List[str] = torch.sqrt(_a )
A_ : Any = drift - diffusion**2 * score
A_ : Optional[int] = x + drift * dt
# add noise
A_ : Optional[Any] = randn_tensor(x.shape ,layout=x.layout ,generator=_a ,device=x.device ,dtype=x.dtype )
A_ : Dict = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self : List[Any] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 27 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : complex , lowerCamelCase : str = "x" , lowerCamelCase : float = 10**-10 , lowerCamelCase : int = 1 , ):
A_ : int = symbols(lowerCamelCase)
A_ : List[Any] = lambdify(lowerCamelCase , lowerCamelCase)
A_ : List[str] = lambdify(lowerCamelCase , diff(lowerCamelCase , lowerCamelCase))
A_ : str = starting_point
while True:
if diff_function(lowerCamelCase) != 0:
A_ : int = prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
lowerCamelCase)
else:
raise ZeroDivisionError("""Could not find root""") from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess) < precision:
return next_guess
A_ : Union[str, Any] = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}""",
)
# Find root of cos(x)
print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
| 27 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = 42
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@register_to_config
def __init__( self : str ,_a : int = 3 ,_a : int = 3 ,_a : Tuple[str] = ("DownEncoderBlock2D",) ,_a : Tuple[str] = ("UpDecoderBlock2D",) ,_a : Tuple[int] = (64,) ,_a : int = 1 ,_a : str = "silu" ,_a : int = 3 ,_a : int = 32 ,_a : int = 256 ,_a : int = 32 ,_a : Optional[int] = None ,_a : float = 0.18215 ,_a : str = "group" ,):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
A_ : Union[str, Any] = Encoder(
in_channels=_a ,out_channels=_a ,down_block_types=_a ,block_out_channels=_a ,layers_per_block=_a ,act_fn=_a ,norm_num_groups=_a ,double_z=_a ,)
A_ : Optional[Any] = vq_embed_dim if vq_embed_dim is not None else latent_channels
A_ : Optional[Any] = nn.Convad(_a ,_a ,1 )
A_ : int = VectorQuantizer(_a ,_a ,beta=0.25 ,remap=_a ,sane_index_shape=_a )
A_ : Dict = nn.Convad(_a ,_a ,1 )
# pass init params to Decoder
A_ : Union[str, Any] = Decoder(
in_channels=_a ,out_channels=_a ,up_block_types=_a ,block_out_channels=_a ,layers_per_block=_a ,act_fn=_a ,norm_num_groups=_a ,norm_type=_a ,)
@apply_forward_hook
def _a ( self : str ,_a : torch.FloatTensor ,_a : bool = True ):
'''simple docstring'''
A_ : Union[str, Any] = self.encoder(_a )
A_ : Optional[Any] = self.quant_conv(_a )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=_a )
@apply_forward_hook
def _a ( self : Any ,_a : torch.FloatTensor ,_a : bool = False ,_a : bool = True ):
'''simple docstring'''
if not force_not_quantize:
A_ , A_ , A_ : str = self.quantize(_a )
else:
A_ : int = h
A_ : int = self.post_quant_conv(_a )
A_ : List[str] = self.decoder(_a ,quant if self.config.norm_type == """spatial""" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_a )
def _a ( self : Dict ,_a : torch.FloatTensor ,_a : bool = True ):
'''simple docstring'''
A_ : Tuple = sample
A_ : Dict = self.encode(_a ).latents
A_ : List[Any] = self.decode(_a ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_a )
| 27 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__magic_name__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_a : Dict ):
'''simple docstring'''
super().__init__()
A_ : List[str] = torchvision.models.resnetaaa(pretrained=_a )
A_ : int = list(model.children() )[:-2]
A_ : int = nn.Sequential(*_a )
A_ : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def _a ( self : str ,_a : Optional[int] ):
'''simple docstring'''
A_ : Tuple = self.pool(self.model(_a ) )
A_ : Any = torch.flatten(_a ,start_dim=2 )
A_ : str = out.transpose(1 ,2 ).contiguous()
return out # BxNx2048
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Dict ,_a : Dict ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Dict = [json.loads(_a ) for l in open(_a )]
A_ : Optional[int] = os.path.dirname(_a )
A_ : Optional[Any] = tokenizer
A_ : Optional[Any] = labels
A_ : List[Any] = len(_a )
A_ : str = max_seq_length
A_ : str = transforms
def __len__( self : str ):
'''simple docstring'''
return len(self.data )
def __getitem__( self : Tuple ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,add_special_tokens=_a ) )
A_ , A_ , A_ : Dict = sentence[0], sentence[1:-1], sentence[-1]
A_ : Optional[int] = sentence[: self.max_seq_length]
A_ : Any = torch.zeros(self.n_classes )
A_ : Tuple = 1
A_ : Optional[Any] = Image.open(os.path.join(self.data_dir ,self.data[index]["""img"""] ) ).convert("""RGB""" )
A_ : Union[str, Any] = self.transforms(_a )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def lowerCamelCase ( lowerCamelCase : str):
A_ : List[Any] = [len(row["""sentence"""]) for row in batch]
A_ , A_ : Dict = len(lowerCamelCase), max(lowerCamelCase)
A_ : Optional[int] = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
A_ : Tuple = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase , lowerCamelCase)):
A_ : str = input_row["""sentence"""]
A_ : Tuple = 1
A_ : int = torch.stack([row["""image"""] for row in batch])
A_ : str = torch.stack([row["""label"""] for row in batch])
A_ : List[Any] = torch.stack([row["""image_start_token"""] for row in batch])
A_ : Tuple = torch.stack([row["""image_end_token"""] for row in batch])
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase ( ):
return transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),
])
| 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( lowerCamelCase : int):
if num <= 0:
A_ : List[Any] = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase)
A_ : str = [True] * (num + 1)
A_ : Tuple = []
A_ : str = 2
A_ : Any = int(math.sqrt(lowerCamelCase))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCamelCase)
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCamelCase):
if sieve[i] is True:
A_ : Union[str, Any] = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(lowerCamelCase)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 27 | 1 |
'''simple docstring'''
import numpy as np
def lowerCamelCase ( lowerCamelCase : np.ndarray , lowerCamelCase : np.ndarray , lowerCamelCase : float = 1E-12 , lowerCamelCase : int = 100 , ):
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[1]
# Ensure proper dimensionality.
assert np.shape(lowerCamelCase)[0] == np.shape(lowerCamelCase)[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowerCamelCase) == np.iscomplexobj(lowerCamelCase)
A_ : Tuple = np.iscomplexobj(lowerCamelCase)
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowerCamelCase , input_matrix.conj().T)
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
A_ : int = False
A_ : int = 0
A_ : Tuple = 0
A_ : Dict = 1E12
while not convergence:
# Multiple matrix by the vector.
A_ : List[str] = np.dot(lowerCamelCase , lowerCamelCase)
# Normalize the resulting output vector.
A_ : str = w / np.linalg.norm(lowerCamelCase)
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
A_ : Optional[int] = vector.conj().T if is_complex else vector.T
A_ : List[Any] = np.dot(lowerCamelCase , np.dot(lowerCamelCase , lowerCamelCase))
# Check convergence.
A_ : Any = np.abs(lambda_ - lambda_previous) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
A_ : str = True
A_ : str = lambda_
if is_complex:
A_ : List[Any] = np.real(lambda_)
return lambda_, vector
def lowerCamelCase ( ):
A_ : List[Any] = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]])
A_ : int = np.array([41, 4, 20])
A_ : Optional[int] = real_input_matrix.astype(np.complexaaa)
A_ : int = np.triu(1j * complex_input_matrix , 1)
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
A_ : List[str] = np.array([41, 4, 20]).astype(np.complexaaa)
for problem_type in ["real", "complex"]:
if problem_type == "real":
A_ : int = real_input_matrix
A_ : str = real_vector
elif problem_type == "complex":
A_ : List[str] = complex_input_matrix
A_ : List[str] = complex_vector
# Our implementation.
A_ , A_ : Optional[int] = power_iteration(lowerCamelCase , lowerCamelCase)
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
A_ , A_ : Union[str, Any] = np.linalg.eigh(lowerCamelCase)
# Last eigenvalue is the maximum one.
A_ : Tuple = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
A_ : Any = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(lowerCamelCase) - np.abs(lowerCamelCase)) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 27 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
__magic_name__ = trt.Logger(trt.Logger.WARNING)
__magic_name__ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
__magic_name__ = logging.getLogger(__name__)
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=384,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=128,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
__magic_name__ = parser.parse_args()
if args.tokenizer_name:
__magic_name__ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
__magic_name__ = args.per_device_eval_batch_size
__magic_name__ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
__magic_name__ = True
__magic_name__ = 'temp_engine/bert-fp32.engine'
if args.fpaa:
__magic_name__ = 'temp_engine/bert-fp16.engine'
if args.inta:
__magic_name__ = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
__magic_name__ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
__magic_name__ = [network.get_input(i) for i in range(network.num_inputs)]
__magic_name__ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
__magic_name__ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
__magic_name__ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
__magic_name__ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[str]):
A_ : str = np.asarray(inputs["""input_ids"""] , dtype=np.intaa)
A_ : int = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa)
A_ : Optional[int] = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa)
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCamelCase)
# start time
A_ : List[Any] = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCamelCase) for d_inp in d_inputs] + [int(lowerCamelCase), int(lowerCamelCase)] , stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Synchronize the stream and take time
stream.synchronize()
# end time
A_ : str = time.time()
A_ : Tuple = end_time - start_time
A_ : Any = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__magic_name__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__magic_name__ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
__magic_name__ = raw_datasets['validation'].column_names
__magic_name__ = 'question' if 'question' in column_names else column_names[0]
__magic_name__ = 'context' if 'context' in column_names else column_names[1]
__magic_name__ = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
__magic_name__ = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
)
__magic_name__ = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCamelCase ( lowerCamelCase : Dict):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
A_ : List[Any] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
A_ : Optional[int] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=lowerCamelCase , stride=args.doc_stride , return_overflowing_tokens=lowerCamelCase , return_offsets_mapping=lowerCamelCase , padding="""max_length""" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
A_ : List[str] = tokenized_examples.pop("""overflow_to_sample_mapping""")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
A_ : Union[str, Any] = []
for i in range(len(tokenized_examples["""input_ids"""])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
A_ : Any = tokenized_examples.sequence_ids(lowerCamelCase)
A_ : Tuple = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
A_ : Union[str, Any] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["""id"""][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
A_ : Dict = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
]
return tokenized_examples
__magic_name__ = raw_datasets['validation']
# Validation Feature Creation
__magic_name__ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
__magic_name__ = default_data_collator
__magic_name__ = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__magic_name__ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any]="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
A_ : Tuple = postprocess_qa_predictions(
examples=lowerCamelCase , features=lowerCamelCase , predictions=lowerCamelCase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCamelCase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
A_ : Dict = [
{"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
]
else:
A_ : Union[str, Any] = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]
A_ : Any = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCamelCase , label_ids=lowerCamelCase)
__magic_name__ = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize
# Allocate device memory for inputs and outputs.
__magic_name__ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
__magic_name__ = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(f""" Num examples = {len(eval_dataset)}""")
logger.info(f""" Batch size = {args.per_device_eval_batch_size}""")
__magic_name__ = 0.0
__magic_name__ = 0
__magic_name__ = timeit.default_timer()
__magic_name__ = None
for step, batch in enumerate(eval_dataloader):
__magic_name__ , __magic_name__ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
__magic_name__ , __magic_name__ = outputs
__magic_name__ = torch.tensor(start_logits)
__magic_name__ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
__magic_name__ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
__magic_name__ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
__magic_name__ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
__magic_name__ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
__magic_name__ = nested_truncate(all_preds, len(eval_dataset))
__magic_name__ = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_000))
logger.info('Total Number of Inference = %d', niter)
__magic_name__ = post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")
| 27 | 1 |
'''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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__magic_name__ = {
'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'
},
}
__magic_name__ = {'facebook/blenderbot-3B': 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCamelCase ( ):
A_ : int = (
list(range(ord("""!""") , ord("""~""") + 1)) + list(range(ord("""¡""") , ord("""¬""") + 1)) + list(range(ord("""®""") , ord("""ÿ""") + 1))
)
A_ : Dict = bs[:]
A_ : Tuple = 0
for b in range(2**8):
if b not in bs:
bs.append(lowerCamelCase)
cs.append(2**8 + n)
n += 1
A_ : Optional[Any] = [chr(lowerCamelCase) for n in cs]
return dict(zip(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Any = set()
A_ : Union[str, Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
A_ : Tuple = char
return pairs
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Union[str, Any] ,_a : Tuple ,_a : Dict ,_a : List[str]="replace" ,_a : List[str]="<s>" ,_a : int="</s>" ,_a : Tuple="</s>" ,_a : List[Any]="<s>" ,_a : Union[str, Any]="<unk>" ,_a : List[str]="<pad>" ,_a : Optional[int]="<mask>" ,_a : Optional[Any]=False ,**_a : List[str] ,):
'''simple docstring'''
A_ : Any = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else bos_token
A_ : Dict = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else eos_token
A_ : List[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else sep_token
A_ : Optional[int] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else cls_token
A_ : List[Any] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else unk_token
A_ : List[str] = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
A_ : Dict = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else mask_token
super().__init__(
errors=_a ,bos_token=_a ,eos_token=_a ,unk_token=_a ,sep_token=_a ,cls_token=_a ,pad_token=_a ,mask_token=_a ,add_prefix_space=_a ,**_a ,)
with open(_a ,encoding="""utf-8""" ) as vocab_handle:
A_ : Any = json.load(_a )
A_ : str = {v: k for k, v in self.encoder.items()}
A_ : Union[str, Any] = errors # how to handle errors in decoding
A_ : Any = bytes_to_unicode()
A_ : int = {v: k for k, v in self.byte_encoder.items()}
with open(_a ,encoding="""utf-8""" ) as merges_handle:
A_ : int = merges_handle.read().split("""\n""" )[1:-1]
A_ : Optional[int] = [tuple(merge.split() ) for merge in bpe_merges]
A_ : Union[str, Any] = dict(zip(_a ,range(len(_a ) ) ) )
A_ : Any = {}
A_ : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
A_ : int = 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 _a ( self : Tuple ):
'''simple docstring'''
return len(self.encoder )
def _a ( self : Dict ):
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def _a ( self : Optional[Any] ,_a : Any ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
A_ : Optional[int] = tuple(_a )
A_ : int = get_pairs(_a )
if not pairs:
return token
while True:
A_ : List[str] = min(_a ,key=lambda _a : self.bpe_ranks.get(_a ,float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
A_ , A_ : Dict = bigram
A_ : Optional[Any] = []
A_ : Dict = 0
while i < len(_a ):
try:
A_ : int = word.index(_a ,_a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
A_ : Any = j
if word[i] == first and i < len(_a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
A_ : Union[str, Any] = tuple(_a )
A_ : Optional[Any] = new_word
if len(_a ) == 1:
break
else:
A_ : str = get_pairs(_a )
A_ : List[Any] = """ """.join(_a )
A_ : str = word
return word
def _a ( self : Optional[Any] ,_a : List[str] ):
'''simple docstring'''
A_ : Union[str, Any] = []
for token in re.findall(self.pat ,_a ):
A_ : str = """""".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(_a ).split(""" """ ) )
return bpe_tokens
def _a ( self : str ,_a : List[str] ):
'''simple docstring'''
return self.encoder.get(_a ,self.encoder.get(self.unk_token ) )
def _a ( self : Any ,_a : List[Any] ):
'''simple docstring'''
return self.decoder.get(_a )
def _a ( self : Union[str, Any] ,_a : Dict ):
'''simple docstring'''
A_ : int = """""".join(_a )
A_ : Tuple = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" ,errors=self.errors )
return text
def _a ( self : Tuple ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : Union[str, Any] = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
A_ : Dict = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_a ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=_a ,ensure_ascii=_a ) + """\n""" )
A_ : List[Any] = 0
with open(_a ,"""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 _a : 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!""" )
A_ : Tuple = token_index
writer.write(""" """.join(_a ) + """\n""" )
index += 1
return vocab_file, merge_file
def _a ( self : int ,_a : List[int] ,_a : Optional[List[int]] = None ,_a : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a ,token_ids_a=_a ,already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def _a ( self : int ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Optional[int] = [self.sep_token_id]
A_ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self : Optional[int] ,_a : Dict ,_a : Optional[Any]=False ,**_a : Any ):
'''simple docstring'''
A_ : Optional[Any] = kwargs.pop("""add_prefix_space""" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_a ) > 0 and not text[0].isspace()):
A_ : List[str] = """ """ + text
return (text, kwargs)
def _a ( self : Optional[Any] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def _a ( self : List[str] ,_a : "Conversation" ):
'''simple docstring'''
A_ : Tuple = []
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(_a )
A_ : List[Any] = """ """.join(_a )
A_ : List[str] = self.encode(_a )
if len(_a ) > self.model_max_length:
A_ : List[Any] = 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
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['ConvNextFeatureExtractor']
__magic_name__ = ['ConvNextImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvNextForImageClassification',
'ConvNextModel',
'ConvNextPreTrainedModel',
'ConvNextBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TFConvNextForImageClassification',
'TFConvNextModel',
'TFConvNextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 27 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = BioGptTokenizer
a_ = False
def _a ( self : Tuple ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A_ : int = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
A_ : Any = dict(zip(_a ,range(len(_a ) ) ) )
A_ : List[Any] = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
A_ : Dict = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
A_ : Dict = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ) as fp:
fp.write(json.dumps(_a ) )
with open(self.merges_file ,"""w""" ) as fp:
fp.write("""\n""".join(_a ) )
def _a ( self : Optional[int] ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : int = """lower newer"""
A_ : Any = """lower newer"""
return input_text, output_text
def _a ( self : int ):
'''simple docstring'''
A_ : Optional[Any] = BioGptTokenizer(self.vocab_file ,self.merges_file )
A_ : Optional[Any] = """lower"""
A_ : Union[str, Any] = ["""low""", """er</w>"""]
A_ : Union[str, Any] = tokenizer.tokenize(_a )
self.assertListEqual(_a ,_a )
A_ : List[str] = tokens + ["""<unk>"""]
A_ : Optional[int] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) ,_a )
@slow
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[str] = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
A_ : List[str] = tokenizer.encode("""sequence builders""" ,add_special_tokens=_a )
A_ : Tuple = tokenizer.encode("""multi-sequence build""" ,add_special_tokens=_a )
A_ : str = tokenizer.build_inputs_with_special_tokens(_a )
A_ : Tuple = tokenizer.build_inputs_with_special_tokens(_a ,_a )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 27 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_text_model"""
def __init__( self : Union[str, Any] ,_a : Any=49408 ,_a : Any=512 ,_a : Tuple=2048 ,_a : Dict=12 ,_a : Optional[int]=8 ,_a : Tuple=16 ,_a : Tuple="quick_gelu" ,_a : Optional[Any]=1e-5 ,_a : List[Any]=0.0 ,_a : Optional[int]=0.02 ,_a : Dict=1.0 ,_a : Dict=0 ,_a : Any=49406 ,_a : Tuple=49407 ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Optional[int] = intermediate_size
A_ : Optional[int] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : int = max_position_embeddings
A_ : str = hidden_act
A_ : Union[str, Any] = layer_norm_eps
A_ : Tuple = attention_dropout
A_ : Union[str, Any] = initializer_range
A_ : List[Any] = initializer_factor
@classmethod
def _a ( cls : List[str] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : int = cls.get_config_dict(_a ,**_a )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : Union[str, Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_vision_model"""
def __init__( self : List[Any] ,_a : Optional[Any]=768 ,_a : Tuple=3072 ,_a : Dict=12 ,_a : int=12 ,_a : Dict=3 ,_a : Tuple=768 ,_a : int=32 ,_a : int="quick_gelu" ,_a : List[Any]=1e-5 ,_a : Tuple=0.0 ,_a : List[Any]=0.02 ,_a : str=1.0 ,**_a : int ,):
'''simple docstring'''
super().__init__(**_a )
A_ : List[str] = hidden_size
A_ : Union[str, Any] = intermediate_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : int = num_channels
A_ : str = image_size
A_ : List[Any] = patch_size
A_ : int = hidden_act
A_ : List[Any] = layer_norm_eps
A_ : List[str] = attention_dropout
A_ : str = initializer_range
A_ : str = initializer_factor
@classmethod
def _a ( cls : List[Any] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : Optional[int] = cls.get_config_dict(_a ,**_a )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : List[str] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit"""
a_ = True
def __init__( self : Union[str, Any] ,_a : List[str]=None ,_a : List[str]=None ,_a : Dict=512 ,_a : List[Any]=2.6592 ,_a : Optional[Any]=True ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(**_a )
if text_config is None:
A_ : List[Any] = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
A_ : Tuple = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
A_ : Dict = OwlViTTextConfig(**_a )
A_ : Dict = OwlViTVisionConfig(**_a )
A_ : Any = projection_dim
A_ : Optional[int] = logit_scale_init_value
A_ : Optional[int] = return_dict
A_ : Dict = 1.0
@classmethod
def _a ( cls : Union[str, Any] ,_a : Union[str, os.PathLike] ,**_a : Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : List[Any] = cls.get_config_dict(_a ,**_a )
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
@classmethod
def _a ( cls : int ,_a : Dict ,_a : Dict ,**_a : List[str] ):
'''simple docstring'''
A_ : str = {}
A_ : int = text_config
A_ : Union[str, Any] = vision_config
return cls.from_dict(_a ,**_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = copy.deepcopy(self.__dict__ )
A_ : str = self.text_config.to_dict()
A_ : Optional[int] = self.vision_config.to_dict()
A_ : List[Any] = self.__class__.model_type
return output
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : int ):
'''simple docstring'''
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _a ( self : str ):
'''simple docstring'''
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
def _a ( self : int ,_a : "ProcessorMixin" ,_a : int = -1 ,_a : int = -1 ,_a : Optional["TensorType"] = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(
processor.tokenizer ,batch_size=_a ,seq_length=_a ,framework=_a )
A_ : Any = super().generate_dummy_inputs(
processor.image_processor ,batch_size=_a ,framework=_a )
return {**text_input_dict, **image_input_dict}
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 14
| 27 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__magic_name__ = 16
__magic_name__ = 32
def lowerCamelCase ( lowerCamelCase : Accelerator , lowerCamelCase : int = 16 , lowerCamelCase : str = "bert-base-cased"):
A_ : Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase)
A_ : Tuple = load_dataset("""glue""" , """mrpc""")
def tokenize_function(lowerCamelCase : Any):
# max_length=None => use the model max length (it's actually the default)
A_ : Union[str, Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase)
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A_ : str = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ : List[Any] = tokenized_datasets.rename_column("""label""" , """labels""")
def collate_fn(lowerCamelCase : str):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase , padding="""max_length""" , max_length=128 , return_tensors="""pt""")
return tokenizer.pad(lowerCamelCase , padding="""longest""" , return_tensors="""pt""")
# Instantiate dataloaders.
A_ : List[str] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase)
A_ : Dict = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase)
return train_dataloader, eval_dataloader
def lowerCamelCase ( lowerCamelCase : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : int , lowerCamelCase : Tuple):
model.eval()
A_ : Union[str, Any] = 0
for step, batch in enumerate(lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
with torch.no_grad():
A_ : Union[str, Any] = model(**lowerCamelCase)
A_ : Optional[int] = outputs.logits.argmax(dim=-1)
# It is slightly faster to call this once, than multiple times
A_ , A_ : Tuple = accelerator.gather(
(predictions, batch["""labels"""])) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCamelCase) - 1:
A_ : int = predictions[: len(eval_dataloader.dataset) - samples_seen]
A_ : List[str] = references[: len(eval_dataloader.dataset) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCamelCase , references=lowerCamelCase , )
A_ : str = metric.compute()
return eval_metric["accuracy"]
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
# Initialize accelerator
A_ : Optional[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ : List[str] = config["""lr"""]
A_ : Optional[int] = int(config["""num_epochs"""])
A_ : Optional[Any] = int(config["""seed"""])
A_ : str = int(config["""batch_size"""])
A_ : Any = args.model_name_or_path
set_seed(lowerCamelCase)
A_ , A_ : Optional[Any] = get_dataloaders(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ : str = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase , return_dict=lowerCamelCase)
# Instantiate optimizer
A_ : Union[str, Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
A_ : Any = optimizer_cls(params=model.parameters() , lr=lowerCamelCase)
if accelerator.state.deepspeed_plugin is not None:
A_ : List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
A_ : Dict = 1
A_ : Optional[int] = (len(lowerCamelCase) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
A_ : Any = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=0 , num_training_steps=lowerCamelCase , )
else:
A_ : Optional[Any] = DummyScheduler(lowerCamelCase , total_num_steps=lowerCamelCase , warmup_num_steps=0)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ , A_ , A_ , A_ , A_ : Optional[Any] = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
# We need to keep track of how many total steps we have iterated over
A_ : List[str] = 0
# We also need to keep track of the stating epoch so files are named properly
A_ : Dict = 0
A_ : Optional[Any] = evaluate.load("""glue""" , """mrpc""")
A_ : str = num_epochs
if args.partial_train_epoch is not None:
A_ : Optional[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint)
A_ : List[str] = args.resume_from_checkpoint.split("""epoch_""")[1]
A_ : Optional[Any] = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
A_ : Optional[Any] = int(lowerCamelCase) + 1
A_ : Dict = evaluation_loop(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
accelerator.print("""resumed checkpoint performance:""" , lowerCamelCase)
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0])
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""])
with open(os.path.join(args.output_dir , F'state_{starting_epoch-1}.json') , """r""") as f:
A_ : Union[str, Any] = json.load(lowerCamelCase)
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
A_ : Tuple = {}
for epoch in range(lowerCamelCase , lowerCamelCase):
model.train()
for step, batch in enumerate(lowerCamelCase):
A_ : Dict = model(**lowerCamelCase)
A_ : str = outputs.loss
A_ : List[str] = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
A_ : List[str] = F'epoch_{epoch}'
A_ : Optional[Any] = os.path.join(args.output_dir , lowerCamelCase)
accelerator.save_state(lowerCamelCase)
A_ : List[Any] = evaluation_loop(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
A_ : str = accuracy
A_ : List[Any] = lr_scheduler.get_lr()[0]
A_ : int = optimizer.param_groups[0]["""lr"""]
A_ : Any = epoch
A_ : Optional[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , lowerCamelCase)
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , F'state_{epoch}.json') , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[Any] = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowerCamelCase , default=lowerCamelCase , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowerCamelCase , default=lowerCamelCase , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase , default=2 , help="""Number of train epochs.""" , )
A_ : Tuple = parser.parse_args()
A_ : Union[str, Any] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase)
if __name__ == "__main__":
main()
| 27 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""input_features""", """is_longer"""]
def __init__( self : Dict ,_a : Optional[int]=64 ,_a : List[Any]=48000 ,_a : str=480 ,_a : Optional[Any]=10 ,_a : Optional[int]=1024 ,_a : Tuple=0.0 ,_a : str=False ,_a : float = 0 ,_a : float = 14000 ,_a : int = None ,_a : str = "fusion" ,_a : str = "repeatpad" ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(
feature_size=_a ,sampling_rate=_a ,padding_value=_a ,return_attention_mask=_a ,**_a ,)
A_ : Tuple = top_db
A_ : Tuple = truncation
A_ : Optional[Any] = padding
A_ : Optional[int] = fft_window_size
A_ : Dict = (fft_window_size >> 1) + 1
A_ : Any = hop_length
A_ : List[Any] = max_length_s
A_ : Tuple = max_length_s * sampling_rate
A_ : Tuple = sampling_rate
A_ : Optional[int] = frequency_min
A_ : Tuple = frequency_max
A_ : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm=_a ,mel_scale="""htk""" ,)
A_ : Dict = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def _a ( self : int ):
'''simple docstring'''
A_ : int = copy.deepcopy(self.__dict__ )
A_ : Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _a ( self : Dict ,_a : np.array ,_a : Optional[np.array] = None ):
'''simple docstring'''
A_ : List[str] = spectrogram(
_a ,window_function(self.fft_window_size ,"""hann""" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=_a ,log_mel="""dB""" ,)
return log_mel_spectrogram.T
def _a ( self : Optional[int] ,_a : Dict ,_a : Optional[Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Dict = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : int = [0]
# randomly choose index for each part
A_ : List[str] = np.random.choice(ranges[0] )
A_ : int = np.random.choice(ranges[1] )
A_ : Optional[int] = np.random.choice(ranges[2] )
A_ : Tuple = mel[idx_front : idx_front + chunk_frames, :]
A_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
A_ : Dict = mel[idx_back : idx_back + chunk_frames, :]
A_ : Optional[int] = torch.tensor(mel[None, None, :] )
A_ : Dict = torch.nn.functional.interpolate(
_a ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=_a )
A_ : str = mel_shrink[0][0].numpy()
A_ : Tuple = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def _a ( self : Dict ,_a : np.array ,_a : Optional[Any] ,_a : int ,_a : Dict ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
A_ : Dict = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
A_ : Tuple = len(_a ) - max_length
A_ : Optional[int] = np.random.randint(0 ,overflow + 1 )
A_ : List[Any] = waveform[idx : idx + max_length]
A_ : Optional[Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
A_ : Dict = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Tuple = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
A_ : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
A_ : Optional[int] = np.stack([mel, mel, mel, mel] ,axis=0 )
A_ : str = False
else:
A_ : str = self._random_mel_fusion(_a ,_a ,_a )
A_ : Optional[Any] = True
else:
raise NotImplementedError(f'data_truncating {truncation} not implemented' )
else:
A_ : Optional[int] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
A_ : int = int(max_length / len(_a ) )
A_ : Any = np.stack(np.tile(_a ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
A_ : List[str] = int(max_length / len(_a ) )
A_ : Optional[Any] = np.stack(np.tile(_a ,_a ) )
A_ : Any = np.pad(_a ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
A_ : List[Any] = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
A_ : Union[str, Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[Any] ,_a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_a : str = None ,_a : Optional[str] = None ,_a : Optional[int] = None ,_a : Optional[int] = None ,_a : Optional[Union[str, TensorType]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : List[str] = truncation if truncation is not None else self.truncation
A_ : List[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
A_ : Any = isinstance(_a ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A_ : int = is_batched_numpy or (
isinstance(_a ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
A_ : Optional[int] = [np.asarray(_a ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_a ,np.ndarray ):
A_ : str = np.asarray(_a ,dtype=np.floataa )
elif isinstance(_a ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A_ : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ : Any = [np.asarray(_a )]
# convert to mel spectrogram, truncate and pad if needed.
A_ : str = [
self._get_input_mel(_a ,max_length if max_length else self.nb_max_samples ,_a ,_a )
for waveform in raw_speech
]
A_ : int = []
A_ : Any = []
for mel, longer in padded_inputs:
input_mel.append(_a )
is_longer.append(_a )
if truncation == "fusion" and sum(_a ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
A_ : List[Any] = np.random.randint(0 ,len(_a ) )
A_ : List[str] = True
if isinstance(input_mel[0] ,_a ):
A_ : Tuple = [np.asarray(_a ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
A_ : List[str] = [[longer] for longer in is_longer]
A_ : Optional[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
A_ : int = BatchFeature(_a )
if return_tensors is not None:
A_ : int = input_features.convert_to_tensors(_a )
return input_features
| 27 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = IFInpaintingPipeline
a_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
a_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
a_ = PipelineTesterMixin.required_optional_params - {"""latents"""}
def _a ( self : int ):
'''simple docstring'''
return self._get_dummy_components()
def _a ( self : List[str] ,_a : Tuple ,_a : Any=0 ):
'''simple docstring'''
if str(_a ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_a )
else:
A_ : Tuple = torch.Generator(device=_a ).manual_seed(_a )
A_ : Dict = floats_tensor((1, 3, 32, 32) ,rng=random.Random(_a ) ).to(_a )
A_ : Optional[int] = floats_tensor((1, 3, 32, 32) ,rng=random.Random(_a ) ).to(_a )
A_ : str = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def _a ( self : List[str] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : Optional[Any] ):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" ,reason="""float16 requires CUDA""" )
def _a ( self : List[str] ):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Dict ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : Any ):
'''simple docstring'''
self._test_save_load_local()
def _a ( self : Dict ):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 ,)
| 27 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : List[Any] ,_a : Dict=13 ,_a : List[Any]=7 ,_a : Optional[Any]=True ,_a : Any=True ,_a : Optional[int]=True ,_a : Union[str, Any]=99 ,_a : Union[str, Any]=32 ,_a : List[str]=5 ,_a : List[str]=4 ,_a : Dict=37 ,_a : List[Any]="gelu" ,_a : int=0.1 ,_a : Optional[int]=0.1 ,_a : Tuple=512 ,_a : Union[str, Any]=16 ,_a : Optional[Any]=2 ,_a : Optional[Any]=0.02 ,_a : Optional[int]=3 ,_a : str=4 ,_a : Optional[Any]=None ,):
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : str = batch_size
A_ : int = seq_length
A_ : Union[str, Any] = is_training
A_ : Optional[Any] = use_token_type_ids
A_ : int = use_labels
A_ : Dict = vocab_size
A_ : List[Any] = hidden_size
A_ : Tuple = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : int = intermediate_size
A_ : Tuple = hidden_act
A_ : int = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : Any = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Tuple = type_sequence_label_size
A_ : int = initializer_range
A_ : Optional[Any] = num_labels
A_ : str = num_choices
A_ : Optional[Any] = scope
A_ : List[Any] = self.vocab_size - 1
def _a ( self : Any ):
'''simple docstring'''
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : List[Any] = None
if self.use_token_type_ids:
A_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : int = None
A_ : str = None
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Any = ids_tensor([self.batch_size] ,self.num_choices )
A_ : List[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,pad_token_id=self.pad_token_id ,)
A_ : Tuple = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _a ( self : Optional[int] ,_a : List[str] ,_a : str ,_a : int ,_a : int ,*_a : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[Any] = OpenAIGPTModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,head_mask=_a )
A_ : str = model(_a ,token_type_ids=_a )
A_ : Dict = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Dict ,_a : Optional[int] ,_a : Union[str, Any] ,_a : Dict ,_a : List[str] ,*_a : str ):
'''simple docstring'''
A_ : str = OpenAIGPTLMHeadModel(_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Any ,_a : Dict ,_a : List[Any] ,_a : Dict ,_a : Union[str, Any] ,*_a : str ):
'''simple docstring'''
A_ : Any = OpenAIGPTDoubleHeadsModel(_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : List[str] ,_a : str ,_a : Tuple ,_a : Dict ,_a : Tuple ,*_a : Dict ):
'''simple docstring'''
A_ : List[str] = self.num_labels
A_ : int = OpenAIGPTForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Optional[Any] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : str = config_and_inputs
A_ : int = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
a_ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
a_ = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _a ( self : Tuple ,_a : Optional[int] ,_a : str ,_a : List[str] ,_a : List[str] ,_a : Any ):
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _a ( self : Optional[int] ,_a : str ,_a : Dict ,_a : Optional[int]=False ):
'''simple docstring'''
A_ : Any = super()._prepare_for_class(_a ,_a ,return_labels=_a )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
A_ : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,dtype=torch.long ,device=_a ,)
A_ : Any = inputs_dict["""labels"""]
A_ : Any = inputs_dict["""labels"""]
A_ : Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) ,dtype=torch.long ,device=_a ,)
A_ : int = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=_a )
return inputs_dict
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Tuple = OpenAIGPTModelTester(self )
A_ : Optional[int] = ConfigTester(self ,config_class=_a ,n_embd=37 )
def _a ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_a )
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a )
@slow
def _a ( self : List[Any] ):
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = OpenAIGPTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Dict = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(_a )
A_ : Dict = torch.tensor([[481, 4735, 544]] ,dtype=torch.long ,device=_a ) # the president is
A_ : Dict = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
A_ : int = model.generate(_a ,do_sample=_a )
self.assertListEqual(output_ids[0].tolist() ,_a )
| 27 | 1 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = """nezha"""
def __init__( self : int ,_a : Union[str, Any]=21128 ,_a : int=768 ,_a : Any=12 ,_a : List[str]=12 ,_a : str=3072 ,_a : int="gelu" ,_a : int=0.1 ,_a : str=0.1 ,_a : Tuple=512 ,_a : List[Any]=64 ,_a : Dict=2 ,_a : List[Any]=0.02 ,_a : Optional[Any]=1e-12 ,_a : List[Any]=0.1 ,_a : Union[str, Any]=0 ,_a : Any=2 ,_a : Union[str, Any]=3 ,_a : int=True ,**_a : int ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Dict = max_position_embeddings
A_ : Optional[Any] = max_relative_position
A_ : List[Any] = type_vocab_size
A_ : int = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Dict = classifier_dropout
A_ : int = use_cache
| 27 |
'''simple docstring'''
import baseaa
def lowerCamelCase ( lowerCamelCase : str):
return baseaa.aaaencode(string.encode("""utf-8"""))
def lowerCamelCase ( lowerCamelCase : bytes):
return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : list[int]):
A_ : int = len(lowerCamelCase) // 2
# choose the middle 3 elements
A_ : Tuple = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m]) == 2:
m -= 1
return peak(lst[m:])
# decreasing
else:
if len(lst[:m]) == 2:
m += 1
return peak(lst[:m])
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def lowerCamelCase ( lowerCamelCase : str):
# word like '180' or '身高' or '神'
for char in word:
A_ : Optional[Any] = ord(lowerCamelCase)
if not _is_chinese_char(lowerCamelCase):
return 0
return 1
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Any = set()
for token in tokens:
A_ : str = len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
if chinese_word:
word_set.add(lowerCamelCase)
A_ : Any = list(lowerCamelCase)
return word_list
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : set()):
if not chinese_word_set:
return bert_tokens
A_ : Any = max([len(lowerCamelCase) for w in chinese_word_set])
A_ : str = bert_tokens
A_ , A_ : Any = 0, len(lowerCamelCase)
while start < end:
A_ : Tuple = True
if is_chinese(bert_word[start]):
A_ : List[str] = min(end - start , lowerCamelCase)
for i in range(lowerCamelCase , 1 , -1):
A_ : Tuple = """""".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
A_ : Dict = """##""" + bert_word[j]
A_ : str = start + i
A_ : Dict = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : LTP , lowerCamelCase : BertTokenizer):
A_ : Union[str, Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : List[Any] = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""]).cws
A_ : int = [get_chinese_word(lowerCamelCase) for r in res]
ltp_res.extend(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : List[Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : Dict = bert_tokenizer(lines[i : i + 100] , add_special_tokens=lowerCamelCase , truncation=lowerCamelCase , max_length=512)
bert_res.extend(res["""input_ids"""])
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Union[str, Any] = []
for input_ids, chinese_word in zip(lowerCamelCase , lowerCamelCase):
A_ : List[Any] = []
for id in input_ids:
A_ : List[Any] = bert_tokenizer._convert_id_to_token(lowerCamelCase)
input_tokens.append(lowerCamelCase)
A_ : int = add_sub_symbol(lowerCamelCase , lowerCamelCase)
A_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCamelCase):
if token[:2] == "##":
A_ : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
ref_id.append(lowerCamelCase)
ref_ids.append(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
return ref_ids
def lowerCamelCase ( lowerCamelCase : Tuple):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""") as f:
A_ : Optional[int] = f.readlines()
A_ : Union[str, Any] = [line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ : Optional[Any] = LTP(args.ltp) # faster in GPU device
A_ : Dict = BertTokenizer.from_pretrained(args.bert)
A_ : str = prepare_ref(lowerCamelCase , lowerCamelCase , lowerCamelCase)
with open(args.save_path , """w""" , encoding="""utf-8""") as f:
A_ : Optional[Any] = [json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
f.writelines(lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = 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',
)
__magic_name__ = parser.parse_args()
main(args)
| 27 | 1 |
'''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 ( ):
A_ : int = 10
A_ : Dict = 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"""),
})
A_ : List[Any] = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10,
"""id""": list(range(lowerCamelCase)),
} , features=lowerCamelCase , )
return dataset
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : Tuple):
A_ : List[Any] = str(tmp_path_factory.mktemp("""data""") / """file.arrow""")
dataset.map(cache_file_name=lowerCamelCase)
return filename
# FILE_CONTENT + files
__magic_name__ = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int):
A_ : Dict = tmp_path_factory.mktemp("""data""") / """file.txt"""
A_ : Any = FILE_CONTENT
with open(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase)
return filename
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int):
import bza
A_ : Union[str, Any] = tmp_path_factory.mktemp("""data""") / """file.txt.bz2"""
A_ : int = bytes(lowerCamelCase , """utf-8""")
with bza.open(lowerCamelCase , """wb""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
import gzip
A_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""") / """file.txt.gz""")
A_ : Optional[Any] = bytes(lowerCamelCase , """utf-8""")
with gzip.open(lowerCamelCase , """wb""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
A_ : Dict = tmp_path_factory.mktemp("""data""") / """file.txt.lz4"""
A_ : Optional[int] = bytes(lowerCamelCase , """utf-8""")
with lza.frame.open(lowerCamelCase , """wb""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : List[Any]):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
A_ : Union[str, Any] = tmp_path_factory.mktemp("""data""") / """file.txt.7z"""
with pyazr.SevenZipFile(lowerCamelCase , """w""") as archive:
archive.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : str):
import tarfile
A_ : List[str] = tmp_path_factory.mktemp("""data""") / """file.txt.tar"""
with tarfile.TarFile(lowerCamelCase , """w""") as f:
f.add(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int):
import lzma
A_ : List[Any] = tmp_path_factory.mktemp("""data""") / """file.txt.xz"""
A_ : Any = bytes(lowerCamelCase , """utf-8""")
with lzma.open(lowerCamelCase , """wb""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : List[str]):
import zipfile
A_ : str = tmp_path_factory.mktemp("""data""") / """file.txt.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int]):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
A_ : str = tmp_path_factory.mktemp("""data""") / """file.txt.zst"""
A_ : Tuple = bytes(lowerCamelCase , """utf-8""")
with zstd.open(lowerCamelCase , """wb""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int):
A_ : List[str] = tmp_path_factory.mktemp("""data""") / """file.xml"""
A_ : Optional[int] = 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(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase)
return filename
__magic_name__ = [
{'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},
]
__magic_name__ = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__magic_name__ = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__magic_name__ = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__magic_name__ = [
{'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 ( ):
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[Any]):
A_ : List[Any] = datasets.Dataset.from_dict(lowerCamelCase)
A_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""") / """dataset.arrow""")
dataset.map(cache_file_name=lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int]):
A_ : int = str(tmp_path_factory.mktemp("""data""") / """dataset.sqlite""")
with contextlib.closing(sqlitea.connect(lowerCamelCase)) as con:
A_ : Optional[int] = 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 ( lowerCamelCase : Optional[Any]):
A_ : Any = str(tmp_path_factory.mktemp("""data""") / """dataset.csv""")
with open(lowerCamelCase , """w""" , newline="""""") as f:
A_ : Any = csv.DictWriter(lowerCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""])
writer.writeheader()
for item in DATA:
writer.writerow(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
A_ : str = str(tmp_path_factory.mktemp("""data""") / """dataset2.csv""")
with open(lowerCamelCase , """w""" , newline="""""") as f:
A_ : str = csv.DictWriter(lowerCamelCase , fieldnames=["""col_1""", """col_2""", """col_3"""])
writer.writeheader()
for item in DATA:
writer.writerow(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : List[Any]):
import bza
A_ : Optional[int] = tmp_path_factory.mktemp("""data""") / """dataset.csv.bz2"""
with open(lowerCamelCase , """rb""") as f:
A_ : Optional[int] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowerCamelCase , """wb""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Dict , lowerCamelCase : Dict):
A_ : Optional[int] = tmp_path_factory.mktemp("""data""") / """dataset.csv.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Optional[Any] , lowerCamelCase : Dict):
A_ : Any = tmp_path_factory.mktemp("""data""") / """dataset.csv.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""")))
f.write(lowerCamelCase , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""")))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any]):
A_ : Any = tmp_path_factory.mktemp("""data""") / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(lowerCamelCase)))
f.write(lowerCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(lowerCamelCase)))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""") / """dataset.parquet""")
A_ : str = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
})
with open(lowerCamelCase , """wb""") as f:
A_ : List[Any] = pq.ParquetWriter(lowerCamelCase , schema=lowerCamelCase)
A_ : Optional[int] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowerCamelCase))] for k in DATA[0]} , schema=lowerCamelCase)
writer.write_table(lowerCamelCase)
writer.close()
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = str(tmp_path_factory.mktemp("""data""") / """dataset.json""")
A_ : List[str] = {"""data""": DATA}
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[Any]):
A_ : Any = str(tmp_path_factory.mktemp("""data""") / """dataset.json""")
A_ : Dict = {"""data""": DATA_DICT_OF_LISTS}
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int]):
A_ : List[Any] = str(tmp_path_factory.mktemp("""data""") / """dataset.jsonl""")
with open(lowerCamelCase , """w""") as f:
for item in DATA:
f.write(json.dumps(lowerCamelCase) + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Dict = str(tmp_path_factory.mktemp("""data""") / """dataset2.jsonl""")
with open(lowerCamelCase , """w""") as f:
for item in DATA:
f.write(json.dumps(lowerCamelCase) + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[Any]):
A_ : List[Any] = str(tmp_path_factory.mktemp("""data""") / """dataset_312.jsonl""")
with open(lowerCamelCase , """w""") as f:
for item in DATA_312:
f.write(json.dumps(lowerCamelCase) + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : int):
A_ : Dict = str(tmp_path_factory.mktemp("""data""") / """dataset-str.jsonl""")
with open(lowerCamelCase , """w""") as f:
for item in DATA_STR:
f.write(json.dumps(lowerCamelCase) + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : List[Any]):
import gzip
A_ : List[str] = str(tmp_path_factory.mktemp("""data""") / """dataset.txt.gz""")
with open(lowerCamelCase , """rb""") as orig_file:
with gzip.open(lowerCamelCase , """wb""") as zipped_file:
zipped_file.writelines(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Dict , lowerCamelCase : List[Any]):
import gzip
A_ : Union[str, Any] = str(tmp_path_factory.mktemp("""data""") / """dataset.jsonl.gz""")
with open(lowerCamelCase , """rb""") as orig_file:
with gzip.open(lowerCamelCase , """wb""") as zipped_file:
zipped_file.writelines(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : List[Any]):
A_ : Any = tmp_path_factory.mktemp("""data""") / """dataset.jsonl.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Dict , lowerCamelCase : int , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any]):
A_ : Tuple = tmp_path_factory.mktemp("""data""") / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.join("""nested""" , os.path.basename(lowerCamelCase)))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : str):
A_ : Any = tmp_path_factory.mktemp("""data""") / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(lowerCamelCase)))
f.write(lowerCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(lowerCamelCase)))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any]):
A_ : Any = tmp_path_factory.mktemp("""data""") / """dataset.jsonl.tar"""
with tarfile.TarFile(lowerCamelCase , """w""") as f:
f.add(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
f.add(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Dict , lowerCamelCase : Tuple , lowerCamelCase : List[str] , lowerCamelCase : Any):
A_ : str = tmp_path_factory.mktemp("""data""") / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(lowerCamelCase , """w""") as f:
f.add(lowerCamelCase , arcname=os.path.join("""nested""" , os.path.basename(lowerCamelCase)))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
A_ : Optional[Any] = ["""0""", """1""", """2""", """3"""]
A_ : List[Any] = str(tmp_path_factory.mktemp("""data""") / """dataset.txt""")
with open(lowerCamelCase , """w""") as f:
for item in data:
f.write(item + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[Any]):
A_ : Optional[int] = ["""0""", """1""", """2""", """3"""]
A_ : List[Any] = str(tmp_path_factory.mktemp("""data""") / """dataset2.txt""")
with open(lowerCamelCase , """w""") as f:
for item in data:
f.write(item + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : str):
A_ : Dict = ["""0""", """1""", """2""", """3"""]
A_ : Optional[int] = tmp_path_factory.mktemp("""data""") / """dataset.abc"""
with open(lowerCamelCase , """w""") as f:
for item in data:
f.write(item + """\n""")
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Any , lowerCamelCase : int):
A_ : List[Any] = tmp_path_factory.mktemp("""data""") / """dataset.text.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Tuple):
A_ : str = tmp_path_factory.mktemp("""data""") / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(lowerCamelCase)))
f.write(lowerCamelCase , arcname=os.path.join("""main_dir""" , os.path.basename(lowerCamelCase)))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : str , lowerCamelCase : List[Any]):
A_ : int = tmp_path_factory.mktemp("""data""") / """dataset.ext.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename("""unsupported.ext"""))
f.write(lowerCamelCase , arcname=os.path.basename("""unsupported_2.ext"""))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""])
A_ : Optional[Any] = str(tmp_path_factory.mktemp("""data""") / """dataset_with_unicode_new_lines.txt""")
with open(lowerCamelCase , """w""" , encoding="""utf-8""") as f:
f.write(lowerCamelCase)
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( ):
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""")
@pytest.fixture(scope="""session""")
def lowerCamelCase ( ):
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""")
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Optional[int]):
A_ : Any = tmp_path_factory.mktemp("""data""") / """dataset.img.zip"""
with zipfile.ZipFile(lowerCamelCase , """w""") as f:
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase))
f.write(lowerCamelCase , arcname=os.path.basename(lowerCamelCase).replace(""".jpg""" , """2.jpg"""))
return path
@pytest.fixture(scope="""session""")
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : 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""" * 10)
with open(data_dir / """subdir""" / """test.txt""" , """w""") as f:
f.write("""bar\n""" * 10)
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""") as f:
f.write("""bar\n""" * 10)
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""") as f:
f.write("""foo\n""" * 10)
with open(data_dir / """.subdir""" / """test.txt""" , """w""") as f:
f.write("""bar\n""" * 10)
return data_dir
| 27 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """ViltImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] ,_a : Optional[Any]=None ,_a : List[str]=None ,**_a : Any ):
'''simple docstring'''
A_ : 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.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[Any] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Any ,_a : Tuple ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
# add pixel_values + pixel_mask
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Any ,**_a : Any ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : int ,*_a : int ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : int ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : str ,_a : Any ):
'''simple docstring'''
A_ : Optional[int] = data
A_ : Optional[Any] = [0X67452301, 0XEFCDAB89, 0X98BADCFE, 0X10325476, 0XC3D2E1F0]
@staticmethod
def _a ( _a : Dict ,_a : Tuple ):
'''simple docstring'''
return ((n << b) | (n >> (32 - b))) & 0XFFFFFFFF
def _a ( self : Tuple ):
'''simple docstring'''
A_ : str = B"""\x80""" + B"""\x00""" * (63 - (len(self.data ) + 8) % 64)
A_ : Union[str, Any] = self.data + padding + struct.pack(""">Q""" ,8 * len(self.data ) )
return padded_data
def _a ( self : Optional[Any] ):
'''simple docstring'''
return [
self.padded_data[i : i + 64] for i in range(0 ,len(self.padded_data ) ,64 )
]
def _a ( self : Union[str, Any] ,_a : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = list(struct.unpack(""">16L""" ,_a ) ) + [0] * 64
for i in range(16 ,80 ):
A_ : List[str] = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) ,1 )
return w
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Optional[int] = self.padding()
A_ : Union[str, Any] = self.split_blocks()
for block in self.blocks:
A_ : Tuple = self.expand_block(_a )
A_ , A_ , A_ , A_ , A_ : str = self.h
for i in range(0 ,80 ):
if 0 <= i < 20:
A_ : Optional[Any] = (b & c) | ((~b) & d)
A_ : str = 0X5A827999
elif 20 <= i < 40:
A_ : int = b ^ c ^ d
A_ : Union[str, Any] = 0X6ED9EBA1
elif 40 <= i < 60:
A_ : List[Any] = (b & c) | (b & d) | (c & d)
A_ : Union[str, Any] = 0X8F1BBCDC
elif 60 <= i < 80:
A_ : List[str] = b ^ c ^ d
A_ : Tuple = 0XCA62C1D6
A_ , A_ , A_ , A_ , A_ : Tuple = (
self.rotate(_a ,5 ) + f + e + k + expanded_block[i] & 0XFFFFFFFF,
a,
self.rotate(_a ,30 ),
c,
d,
)
A_ : Union[str, Any] = (
self.h[0] + a & 0XFFFFFFFF,
self.h[1] + b & 0XFFFFFFFF,
self.h[2] + c & 0XFFFFFFFF,
self.h[3] + d & 0XFFFFFFFF,
self.h[4] + e & 0XFFFFFFFF,
)
return ("{:08x}" * 5).format(*self.h )
def lowerCamelCase ( ):
A_ : Union[str, Any] = b"""Test String"""
assert SHAaHash(lowerCamelCase).final_hash() == hashlib.shaa(lowerCamelCase).hexdigest() # noqa: S324
def lowerCamelCase ( ):
A_ : Tuple = argparse.ArgumentParser(description="""Process some strings or files""")
parser.add_argument(
"""--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument("""--file""" , dest="""input_file""" , help="""Hash contents of a file""")
A_ : Any = parser.parse_args()
A_ : Tuple = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""") as f:
A_ : List[str] = f.read()
else:
A_ : List[Any] = bytes(lowerCamelCase , """utf-8""")
print(SHAaHash(lowerCamelCase).final_hash())
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""torch""", """torchsde"""]
def __init__( self : Any ,*_a : Union[str, Any] ,**_a : Optional[int] ):
'''simple docstring'''
requires_backends(self ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : Optional[int] ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : List[Any] ,*_a : Tuple ,**_a : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
| 27 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]="pt"):
A_ : Optional[int] = {"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase) and not line.startswith(""" """) else {}
A_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , ):
A_ : Dict = input_ids.ne(lowerCamelCase).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Dict ,_a : Tuple ,_a : Tuple="train" ,_a : Optional[int]=None ,_a : Any=None ,_a : int=None ,_a : Union[str, Any]="" ,):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = Path(_a ).joinpath(type_path + """.source""" )
A_ : Any = Path(_a ).joinpath(type_path + """.target""" )
A_ : Dict = self.get_char_lens(self.src_file )
A_ : Optional[int] = max_source_length
A_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A_ : List[Any] = tokenizer
A_ : Optional[Any] = prefix
if n_obs is not None:
A_ : Any = self.src_lens[:n_obs]
A_ : Optional[int] = src_lang
A_ : Tuple = tgt_lang
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : List[str] ,_a : Tuple ):
'''simple docstring'''
A_ : int = index + 1 # linecache starts at 1
A_ : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,_a ).rstrip("""\n""" )
A_ : Dict = linecache.getline(str(self.tgt_file ) ,_a ).rstrip("""\n""" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ : List[str] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_a ) else self.tokenizer
)
A_ : Any = self.tokenizer.generator if isinstance(self.tokenizer ,_a ) else self.tokenizer
A_ : Optional[int] = encode_line(_a ,_a ,self.max_source_length ,"""right""" )
A_ : Optional[int] = encode_line(_a ,_a ,self.max_target_length ,"""right""" )
A_ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
A_ : Dict = target_inputs["""input_ids"""].squeeze()
A_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( _a : int ):
'''simple docstring'''
return [len(_a ) for x in Path(_a ).open().readlines()]
def _a ( self : Optional[int] ,_a : Dict ):
'''simple docstring'''
A_ : str = torch.stack([x["""input_ids"""] for x in batch] )
A_ : Optional[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
A_ : str = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : List[str] = trim_batch(_a ,_a )
A_ , A_ : Union[str, Any] = trim_batch(_a ,_a ,attention_mask=_a )
A_ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__magic_name__ = getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[List]):
return list(itertools.chain.from_iterable(lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json"""))
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : List[str]=4 , **lowerCamelCase : List[str]):
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase) as f:
return json.load(lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[str] = git.Repo(search_parent_directories=lowerCamelCase)
A_ : Union[str, Any] = {
"""repo_id""": str(lowerCamelCase),
"""repo_sha""": str(repo.head.object.hexsha),
"""repo_branch""": str(repo.active_branch),
"""hostname""": str(socket.gethostname()),
}
return repo_infos
def lowerCamelCase ( lowerCamelCase : Callable , lowerCamelCase : Iterable):
return list(map(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
with open(lowerCamelCase , """wb""") as f:
return pickle.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str]):
def remove_articles(lowerCamelCase : Any):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , lowerCamelCase)
def white_space_fix(lowerCamelCase : List[Any]):
return " ".join(text.split())
def remove_punc(lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(lowerCamelCase : List[str]):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = normalize_answer(lowerCamelCase).split()
A_ : Dict = normalize_answer(lowerCamelCase).split()
A_ : int = Counter(lowerCamelCase) & Counter(lowerCamelCase)
A_ : Any = sum(common.values())
if num_same == 0:
return 0
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Any):
return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[str]):
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Any = 0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase):
em += exact_match_score(lowerCamelCase , lowerCamelCase)
if len(lowerCamelCase) > 0:
em /= len(lowerCamelCase)
return {"em": em}
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return model_prefix.startswith("""rag""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
A_ : Optional[Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ : Tuple = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase):
if not hasattr(lowerCamelCase , lowerCamelCase) and not hasattr(lowerCamelCase , equivalent_param[p]):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
continue
A_ : Tuple = p if hasattr(lowerCamelCase , lowerCamelCase) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
return hparams, config
| 27 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]="pt"):
A_ : Optional[int] = {"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase) and not line.startswith(""" """) else {}
A_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , ):
A_ : Dict = input_ids.ne(lowerCamelCase).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Dict ,_a : Tuple ,_a : Tuple="train" ,_a : Optional[int]=None ,_a : Any=None ,_a : int=None ,_a : Union[str, Any]="" ,):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = Path(_a ).joinpath(type_path + """.source""" )
A_ : Any = Path(_a ).joinpath(type_path + """.target""" )
A_ : Dict = self.get_char_lens(self.src_file )
A_ : Optional[int] = max_source_length
A_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A_ : List[Any] = tokenizer
A_ : Optional[Any] = prefix
if n_obs is not None:
A_ : Any = self.src_lens[:n_obs]
A_ : Optional[int] = src_lang
A_ : Tuple = tgt_lang
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : List[str] ,_a : Tuple ):
'''simple docstring'''
A_ : int = index + 1 # linecache starts at 1
A_ : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,_a ).rstrip("""\n""" )
A_ : Dict = linecache.getline(str(self.tgt_file ) ,_a ).rstrip("""\n""" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ : List[str] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_a ) else self.tokenizer
)
A_ : Any = self.tokenizer.generator if isinstance(self.tokenizer ,_a ) else self.tokenizer
A_ : Optional[int] = encode_line(_a ,_a ,self.max_source_length ,"""right""" )
A_ : Optional[int] = encode_line(_a ,_a ,self.max_target_length ,"""right""" )
A_ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
A_ : Dict = target_inputs["""input_ids"""].squeeze()
A_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( _a : int ):
'''simple docstring'''
return [len(_a ) for x in Path(_a ).open().readlines()]
def _a ( self : Optional[int] ,_a : Dict ):
'''simple docstring'''
A_ : str = torch.stack([x["""input_ids"""] for x in batch] )
A_ : Optional[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
A_ : str = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : List[str] = trim_batch(_a ,_a )
A_ , A_ : Union[str, Any] = trim_batch(_a ,_a ,attention_mask=_a )
A_ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__magic_name__ = getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[List]):
return list(itertools.chain.from_iterable(lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json"""))
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : List[str]=4 , **lowerCamelCase : List[str]):
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase) as f:
return json.load(lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[str] = git.Repo(search_parent_directories=lowerCamelCase)
A_ : Union[str, Any] = {
"""repo_id""": str(lowerCamelCase),
"""repo_sha""": str(repo.head.object.hexsha),
"""repo_branch""": str(repo.active_branch),
"""hostname""": str(socket.gethostname()),
}
return repo_infos
def lowerCamelCase ( lowerCamelCase : Callable , lowerCamelCase : Iterable):
return list(map(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
with open(lowerCamelCase , """wb""") as f:
return pickle.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str]):
def remove_articles(lowerCamelCase : Any):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , lowerCamelCase)
def white_space_fix(lowerCamelCase : List[Any]):
return " ".join(text.split())
def remove_punc(lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(lowerCamelCase : List[str]):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = normalize_answer(lowerCamelCase).split()
A_ : Dict = normalize_answer(lowerCamelCase).split()
A_ : int = Counter(lowerCamelCase) & Counter(lowerCamelCase)
A_ : Any = sum(common.values())
if num_same == 0:
return 0
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Any):
return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[str]):
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Any = 0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase):
em += exact_match_score(lowerCamelCase , lowerCamelCase)
if len(lowerCamelCase) > 0:
em /= len(lowerCamelCase)
return {"em": em}
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return model_prefix.startswith("""rag""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
A_ : Optional[Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ : Tuple = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase):
if not hasattr(lowerCamelCase , lowerCamelCase) and not hasattr(lowerCamelCase , equivalent_param[p]):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
continue
A_ : Tuple = p if hasattr(lowerCamelCase , lowerCamelCase) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
return hparams, config
| 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 | 1 |
'''simple docstring'''
import os
import string
import sys
__magic_name__ = 1 << 8
__magic_name__ = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY_FLAG,
'mod_int': 91,
'undefined': sys.maxsize,
'interrupt': 3,
'insert': 50,
'delete': 51,
'pg_up': 53,
'pg_down': 54,
}
__magic_name__ = KEYMAP['up']
__magic_name__ = KEYMAP['left']
if sys.platform == "win32":
__magic_name__ = []
__magic_name__ = {
b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
}
for i in range(10):
__magic_name__ = ord(str(i))
def lowerCamelCase ( ):
if os.name == "nt":
import msvcrt
A_ : List[Any] = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(lowerCamelCase) == 0:
# Read the keystroke
A_ : Optional[int] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
A_ : Optional[int] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
A_ : Optional[int] = chr(WIN_KEYMAP[cha])
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]))
WIN_CH_BUFFER.append(lowerCamelCase)
if ord(lowerCamelCase) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126))
A_ : List[Any] = chr(KEYMAP["""esc"""])
except KeyError:
A_ : str = cha[1]
else:
A_ : List[Any] = ch.decode(lowerCamelCase)
else:
A_ : List[Any] = WIN_CH_BUFFER.pop(0)
elif os.name == "posix":
import termios
import tty
A_ : Optional[int] = sys.stdin.fileno()
A_ : Union[str, Any] = termios.tcgetattr(lowerCamelCase)
try:
tty.setraw(lowerCamelCase)
A_ : Optional[Any] = sys.stdin.read(1)
finally:
termios.tcsetattr(lowerCamelCase , termios.TCSADRAIN , lowerCamelCase)
return ch
def lowerCamelCase ( ):
A_ : Union[str, Any] = get_raw_chars()
if ord(lowerCamelCase) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(lowerCamelCase) == KEYMAP["esc"]:
A_ : str = get_raw_chars()
if ord(lowerCamelCase) == KEYMAP["mod_int"]:
A_ : List[str] = get_raw_chars()
if ord(lowerCamelCase) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowerCamelCase) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(lowerCamelCase) + ARROW_KEY_FLAG)
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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'''
a_ = KandinskyVaaControlnetPipeline
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a_ = False
@property
def _a ( self : Any ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return self.time_input_dim
@property
def _a ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 100
@property
def _a ( self : List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[Any] = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
A_ : Tuple = UNetaDConditionModel(**_a )
return model
@property
def _a ( self : List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Optional[Any] = self.dummy_unet
A_ : int = self.dummy_movq
A_ : Tuple = DDIMScheduler(
num_train_timesteps=1000 ,beta_schedule="""linear""" ,beta_start=0.00085 ,beta_end=0.012 ,clip_sample=_a ,set_alpha_to_one=_a ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=_a ,)
A_ : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _a ( self : Dict ,_a : str ,_a : Union[str, Any]=0 ):
'''simple docstring'''
A_ : Dict = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(_a ) ).to(_a )
A_ : int = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
_a )
# create hint
A_ : List[Any] = floats_tensor((1, 3, 64, 64) ,rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_a )
else:
A_ : str = torch.Generator(device=_a ).manual_seed(_a )
A_ : List[Any] = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[Any] = """cpu"""
A_ : List[str] = self.get_dummy_components()
A_ : Tuple = self.pipeline_class(**_a )
A_ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = pipe(**self.get_dummy_inputs(_a ) )
A_ : Tuple = output.images
A_ : Optional[Any] = pipe(
**self.get_dummy_inputs(_a ) ,return_dict=_a ,)[0]
A_ : Tuple = image[0, -3:, -3:, -1]
A_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : List[Any] = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
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 _a ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Any ):
'''simple docstring'''
A_ : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
A_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
A_ : Optional[int] = torch.from_numpy(np.array(_a ) ).float() / 255.0
A_ : List[Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
A_ : List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(_a )
A_ : Union[str, Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
A_ : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
A_ : Optional[Any] = """A robot, 4k photo"""
A_ : Any = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ , A_ : List[str] = pipe_prior(
_a ,generator=_a ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
A_ : int = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ : List[Any] = pipeline(
image_embeds=_a ,negative_image_embeds=_a ,hint=_a ,generator=_a ,num_inference_steps=100 ,output_type="""np""" ,)
A_ : Dict = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_a ,_a )
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if density <= 0:
raise ValueError("""Impossible fluid density""")
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""")
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """deberta-v2"""
def __init__( self : Optional[Any] ,_a : Union[str, Any]=128100 ,_a : Optional[int]=1536 ,_a : Dict=24 ,_a : int=24 ,_a : Tuple=6144 ,_a : Union[str, Any]="gelu" ,_a : List[Any]=0.1 ,_a : Dict=0.1 ,_a : int=512 ,_a : int=0 ,_a : int=0.02 ,_a : int=1e-7 ,_a : List[str]=False ,_a : Union[str, Any]=-1 ,_a : List[Any]=0 ,_a : Optional[Any]=True ,_a : Tuple=None ,_a : Any=0 ,_a : int="gelu" ,**_a : Any ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : List[Any] = initializer_range
A_ : int = relative_attention
A_ : Tuple = max_relative_positions
A_ : int = pad_token_id
A_ : Tuple = position_biased_input
# Backwards compatibility
if type(_a ) == str:
A_ : str = [x.strip() for x in pos_att_type.lower().split("""|""" )]
A_ : Any = pos_att_type
A_ : Optional[int] = vocab_size
A_ : Tuple = layer_norm_eps
A_ : Any = kwargs.get("""pooler_hidden_size""" ,_a )
A_ : Union[str, Any] = pooler_dropout
A_ : List[Any] = pooler_hidden_act
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : Any = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
return 12
def _a ( self : int ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 3 ,_a : int = 40 ,_a : int = 40 ,_a : "PreTrainedTokenizerBase" = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(preprocessor=_a ,framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : Tuple = 0
A_ : Union[str, Any] = len(lowerCamelCase)
for i in range(n - 1):
for j in range(i + 1 , lowerCamelCase):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
if len(lowerCamelCase) <= 1:
return arr, 0
A_ : Optional[Any] = len(lowerCamelCase) // 2
A_ : List[Any] = arr[0:mid]
A_ : Tuple = arr[mid:]
A_ , A_ : str = count_inversions_recursive(lowerCamelCase)
A_ , A_ : str = count_inversions_recursive(lowerCamelCase)
A_ , A_ : Any = _count_cross_inversions(lowerCamelCase , lowerCamelCase)
A_ : List[str] = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : str):
A_ : Optional[int] = []
A_ : Union[str, Any] = 0
while i < len(lowerCamelCase) and j < len(lowerCamelCase):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowerCamelCase) - i
r.append(q[j])
j += 1
else:
r.append(p[i])
i += 1
if i < len(lowerCamelCase):
r.extend(p[i:])
else:
r.extend(q[j:])
return r, num_inversion
def lowerCamelCase ( ):
A_ : str = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
A_ : Tuple = count_inversions_bf(lowerCamelCase)
A_ , A_ : Union[str, Any] = count_inversions_recursive(lowerCamelCase)
assert num_inversions_bf == num_inversions_recursive == 8
print("""number of inversions = """ , lowerCamelCase)
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
A_ : str = count_inversions_bf(lowerCamelCase)
A_ , A_ : str = count_inversions_recursive(lowerCamelCase)
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , lowerCamelCase)
# an empty list should also have zero inversions
A_ : Any = []
A_ : List[str] = count_inversions_bf(lowerCamelCase)
A_ , A_ : Tuple = count_inversions_recursive(lowerCamelCase)
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , lowerCamelCase)
if __name__ == "__main__":
main()
| 27 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__magic_name__ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__magic_name__ = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
__magic_name__ = BeautifulSoup(res.text, 'html.parser')
__magic_name__ = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f"""https://google.com{link.get('href')}""")
| 27 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""" ,return_dict=_a ).to(_a )
A_ : int = AutoTokenizer.from_pretrained("""google/mt5-small""" )
A_ : int = tokenizer("""Hello there""" ,return_tensors="""pt""" ).input_ids
A_ : Union[str, Any] = tokenizer("""Hi I am""" ,return_tensors="""pt""" ).input_ids
A_ : Tuple = model(input_ids.to(_a ) ,labels=labels.to(_a ) ).loss
A_ : Optional[int] = -(labels.shape[-1] * loss.item())
A_ : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 27 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = """nezha"""
def __init__( self : int ,_a : Union[str, Any]=21128 ,_a : int=768 ,_a : Any=12 ,_a : List[str]=12 ,_a : str=3072 ,_a : int="gelu" ,_a : int=0.1 ,_a : str=0.1 ,_a : Tuple=512 ,_a : List[Any]=64 ,_a : Dict=2 ,_a : List[Any]=0.02 ,_a : Optional[Any]=1e-12 ,_a : List[Any]=0.1 ,_a : Union[str, Any]=0 ,_a : Any=2 ,_a : Union[str, Any]=3 ,_a : int=True ,**_a : int ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Dict = max_position_embeddings
A_ : Optional[Any] = max_relative_position
A_ : List[Any] = type_vocab_size
A_ : int = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Dict = classifier_dropout
A_ : int = use_cache
| 27 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : List[str] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
A_ : Union[str, Any] = [144, 192, 240]
A_ : int = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
A_ : List[str] = [96, 120, 144]
A_ : Any = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
A_ : Any = [64, 80, 96]
A_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
A_ : Any = 0.05
A_ : List[Any] = 2.0
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : int = 512
A_ : Optional[int] = 16
A_ : List[Any] = 21
A_ : List[str] = """pascal-voc-id2label.json"""
else:
A_ : str = 1000
A_ : Any = """imagenet-1k-id2label.json"""
A_ : Any = """huggingface/label-files"""
A_ : List[str] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""") , """r"""))
A_ : str = {int(lowerCamelCase): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int=False):
for i in range(1 , 6):
if F'layer_{i}.' in name:
A_ : Tuple = name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.')
if "conv_1." in name:
A_ : Union[str, Any] = name.replace("""conv_1.""" , """conv_stem.""")
if ".block." in name:
A_ : Optional[Any] = name.replace(""".block.""" , """.""")
if "exp_1x1" in name:
A_ : Union[str, Any] = name.replace("""exp_1x1""" , """expand_1x1""")
if "red_1x1" in name:
A_ : int = name.replace("""red_1x1""" , """reduce_1x1""")
if ".local_rep.conv_3x3." in name:
A_ : List[str] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""")
if ".local_rep.conv_1x1." in name:
A_ : Optional[int] = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""")
if ".norm." in name:
A_ : Tuple = name.replace(""".norm.""" , """.normalization.""")
if ".conv." in name:
A_ : List[Any] = name.replace(""".conv.""" , """.convolution.""")
if ".conv_proj." in name:
A_ : str = name.replace(""".conv_proj.""" , """.conv_projection.""")
for i in range(0 , 2):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Tuple = name.replace(F'.{i}.{j}.' , F'.{i}.layer.{j}.')
for i in range(2 , 6):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Dict = name.replace(F'.{i}.{j}.' , F'.{i}.')
if "expand_1x1" in name:
A_ : Union[str, Any] = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""")
if "conv_3x3" in name:
A_ : str = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""")
if "reduce_1x1" in name:
A_ : Union[str, Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""")
for i in range(2 , 5):
if F'.global_rep.{i}.weight' in name:
A_ : List[Any] = name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight""")
if F'.global_rep.{i}.bias' in name:
A_ : Optional[int] = name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias""")
if ".global_rep." in name:
A_ : Optional[Any] = name.replace(""".global_rep.""" , """.transformer.""")
if ".pre_norm_mha.0." in name:
A_ : int = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""")
if ".pre_norm_mha.1.out_proj." in name:
A_ : Dict = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""")
if ".pre_norm_ffn.0." in name:
A_ : Dict = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""")
if ".pre_norm_ffn.1." in name:
A_ : Any = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""")
if ".pre_norm_ffn.4." in name:
A_ : Union[str, Any] = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""")
if ".transformer." in name:
A_ : Any = name.replace(""".transformer.""" , """.transformer.layer.""")
if ".aspp_layer." in name:
A_ : int = name.replace(""".aspp_layer.""" , """.""")
if ".aspp_pool." in name:
A_ : Tuple = name.replace(""".aspp_pool.""" , """.""")
if "seg_head." in name:
A_ : Optional[int] = name.replace("""seg_head.""" , """segmentation_head.""")
if "segmentation_head.classifier.classifier." in name:
A_ : List[str] = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""")
if "classifier.fc." in name:
A_ : str = name.replace("""classifier.fc.""" , """classifier.""")
elif (not base_model) and ("segmentation_head." not in name):
A_ : str = """mobilevit.""" + name
return name
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False):
if base_model:
A_ : Dict = """"""
else:
A_ : Any = """mobilevit."""
for key in orig_state_dict.copy().keys():
A_ : List[Any] = orig_state_dict.pop(lowerCamelCase)
if key[:8] == "encoder.":
A_ : int = key[8:]
if "qkv" in key:
A_ : Any = key.split(""".""")
A_ : str = int(key_split[0][6:]) - 1
A_ : int = int(key_split[3])
A_ : Optional[Any] = model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}')
A_ : Tuple = layer.transformer.layer[transformer_num].attention.attention.all_head_size
A_ : Optional[Any] = (
F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
)
if "weight" in key:
A_ : Dict = val[:dim, :]
A_ : Optional[int] = val[dim : dim * 2, :]
A_ : List[Any] = val[-dim:, :]
else:
A_ : Optional[Any] = val[:dim]
A_ : List[Any] = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : List[str] = val
return orig_state_dict
def lowerCamelCase ( ):
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase).raw)
return im
@torch.no_grad()
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : int=False):
A_ : Optional[Any] = get_mobilevit_config(lowerCamelCase)
# load original state_dict
A_ : List[Any] = torch.load(lowerCamelCase , map_location="""cpu""")
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : List[str] = MobileViTForSemanticSegmentation(lowerCamelCase).eval()
else:
A_ : str = MobileViTForImageClassification(lowerCamelCase).eval()
A_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase)
model.load_state_dict(lowerCamelCase)
# Check outputs on an image, prepared by MobileViTImageProcessor
A_ : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32)
A_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
A_ : List[Any] = model(**lowerCamelCase)
A_ : Dict = outputs.logits
if mobilevit_name.startswith("""deeplabv3_"""):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
A_ : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xs":
A_ : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
A_ : Tuple = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4)
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
A_ : Tuple = torch.tensor([-0.9866, 0.2392, -1.1241])
elif mobilevit_name == "mobilevit_xs":
A_ : Any = torch.tensor([-2.4761, -0.9399, -1.9587])
elif mobilevit_name == "mobilevit_xxs":
A_ : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4)
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowerCamelCase)
print(F'Saving image processor to {pytorch_dump_folder_path}')
image_processor.save_pretrained(lowerCamelCase)
if push_to_hub:
A_ : str = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""")
A_ : Union[str, Any] = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowerCamelCase , organization="""apple""")
model.push_to_hub(lowerCamelCase , organization="""apple""")
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
__magic_name__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] = stack.pop()
explored.add(lowerCamelCase)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(lowerCamelCase)
return explored
__magic_name__ = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 27 | 1 |
'''simple docstring'''
from collections import defaultdict
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,_a : List[str] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Optional[int] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
A_ : Union[str, Any] = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(_a ) )
]
A_ : Any = defaultdict(_a ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
A_ : str = (1 << len(_a )) - 1
def _a ( self : Union[str, Any] ,_a : Union[str, Any] ,_a : List[str] ):
'''simple docstring'''
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
A_ : Optional[int] = self.count_ways_until(_a ,task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) ,task_no + 1 )
# save the value.
A_ : int = total_ways_util
return self.dp[mask][task_no]
def _a ( self : List[Any] ,_a : Optional[Any] ):
'''simple docstring'''
for i in range(len(_a ) ):
for j in task_performed[i]:
self.task[j].append(_a )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 ,1 )
if __name__ == "__main__":
__magic_name__ = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__magic_name__ = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : List[str] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
A_ : Union[str, Any] = [144, 192, 240]
A_ : int = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
A_ : List[str] = [96, 120, 144]
A_ : Any = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
A_ : Any = [64, 80, 96]
A_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
A_ : Any = 0.05
A_ : List[Any] = 2.0
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : int = 512
A_ : Optional[int] = 16
A_ : List[Any] = 21
A_ : List[str] = """pascal-voc-id2label.json"""
else:
A_ : str = 1000
A_ : Any = """imagenet-1k-id2label.json"""
A_ : Any = """huggingface/label-files"""
A_ : List[str] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""") , """r"""))
A_ : str = {int(lowerCamelCase): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int=False):
for i in range(1 , 6):
if F'layer_{i}.' in name:
A_ : Tuple = name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.')
if "conv_1." in name:
A_ : Union[str, Any] = name.replace("""conv_1.""" , """conv_stem.""")
if ".block." in name:
A_ : Optional[Any] = name.replace(""".block.""" , """.""")
if "exp_1x1" in name:
A_ : Union[str, Any] = name.replace("""exp_1x1""" , """expand_1x1""")
if "red_1x1" in name:
A_ : int = name.replace("""red_1x1""" , """reduce_1x1""")
if ".local_rep.conv_3x3." in name:
A_ : List[str] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""")
if ".local_rep.conv_1x1." in name:
A_ : Optional[int] = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""")
if ".norm." in name:
A_ : Tuple = name.replace(""".norm.""" , """.normalization.""")
if ".conv." in name:
A_ : List[Any] = name.replace(""".conv.""" , """.convolution.""")
if ".conv_proj." in name:
A_ : str = name.replace(""".conv_proj.""" , """.conv_projection.""")
for i in range(0 , 2):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Tuple = name.replace(F'.{i}.{j}.' , F'.{i}.layer.{j}.')
for i in range(2 , 6):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Dict = name.replace(F'.{i}.{j}.' , F'.{i}.')
if "expand_1x1" in name:
A_ : Union[str, Any] = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""")
if "conv_3x3" in name:
A_ : str = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""")
if "reduce_1x1" in name:
A_ : Union[str, Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""")
for i in range(2 , 5):
if F'.global_rep.{i}.weight' in name:
A_ : List[Any] = name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight""")
if F'.global_rep.{i}.bias' in name:
A_ : Optional[int] = name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias""")
if ".global_rep." in name:
A_ : Optional[Any] = name.replace(""".global_rep.""" , """.transformer.""")
if ".pre_norm_mha.0." in name:
A_ : int = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""")
if ".pre_norm_mha.1.out_proj." in name:
A_ : Dict = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""")
if ".pre_norm_ffn.0." in name:
A_ : Dict = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""")
if ".pre_norm_ffn.1." in name:
A_ : Any = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""")
if ".pre_norm_ffn.4." in name:
A_ : Union[str, Any] = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""")
if ".transformer." in name:
A_ : Any = name.replace(""".transformer.""" , """.transformer.layer.""")
if ".aspp_layer." in name:
A_ : int = name.replace(""".aspp_layer.""" , """.""")
if ".aspp_pool." in name:
A_ : Tuple = name.replace(""".aspp_pool.""" , """.""")
if "seg_head." in name:
A_ : Optional[int] = name.replace("""seg_head.""" , """segmentation_head.""")
if "segmentation_head.classifier.classifier." in name:
A_ : List[str] = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""")
if "classifier.fc." in name:
A_ : str = name.replace("""classifier.fc.""" , """classifier.""")
elif (not base_model) and ("segmentation_head." not in name):
A_ : str = """mobilevit.""" + name
return name
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False):
if base_model:
A_ : Dict = """"""
else:
A_ : Any = """mobilevit."""
for key in orig_state_dict.copy().keys():
A_ : List[Any] = orig_state_dict.pop(lowerCamelCase)
if key[:8] == "encoder.":
A_ : int = key[8:]
if "qkv" in key:
A_ : Any = key.split(""".""")
A_ : str = int(key_split[0][6:]) - 1
A_ : int = int(key_split[3])
A_ : Optional[Any] = model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}')
A_ : Tuple = layer.transformer.layer[transformer_num].attention.attention.all_head_size
A_ : Optional[Any] = (
F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
)
if "weight" in key:
A_ : Dict = val[:dim, :]
A_ : Optional[int] = val[dim : dim * 2, :]
A_ : List[Any] = val[-dim:, :]
else:
A_ : Optional[Any] = val[:dim]
A_ : List[Any] = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : List[str] = val
return orig_state_dict
def lowerCamelCase ( ):
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase).raw)
return im
@torch.no_grad()
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : int=False):
A_ : Optional[Any] = get_mobilevit_config(lowerCamelCase)
# load original state_dict
A_ : List[Any] = torch.load(lowerCamelCase , map_location="""cpu""")
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : List[str] = MobileViTForSemanticSegmentation(lowerCamelCase).eval()
else:
A_ : str = MobileViTForImageClassification(lowerCamelCase).eval()
A_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase)
model.load_state_dict(lowerCamelCase)
# Check outputs on an image, prepared by MobileViTImageProcessor
A_ : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32)
A_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
A_ : List[Any] = model(**lowerCamelCase)
A_ : Dict = outputs.logits
if mobilevit_name.startswith("""deeplabv3_"""):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
A_ : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xs":
A_ : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
A_ : Tuple = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4)
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
A_ : Tuple = torch.tensor([-0.9866, 0.2392, -1.1241])
elif mobilevit_name == "mobilevit_xs":
A_ : Any = torch.tensor([-2.4761, -0.9399, -1.9587])
elif mobilevit_name == "mobilevit_xxs":
A_ : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4)
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowerCamelCase)
print(F'Saving image processor to {pytorch_dump_folder_path}')
image_processor.save_pretrained(lowerCamelCase)
if push_to_hub:
A_ : str = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""")
A_ : Union[str, Any] = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowerCamelCase , organization="""apple""")
model.push_to_hub(lowerCamelCase , organization="""apple""")
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
__magic_name__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 27 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_a : int ,_a : int=7 ,_a : str=3 ,_a : Dict=18 ,_a : Optional[int]=30 ,_a : int=400 ,_a : List[Any]=True ,_a : List[str]=None ,_a : Any=True ,_a : Any=[0.5, 0.5, 0.5] ,_a : Tuple=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
A_ : Union[str, Any] = size if size is not None else {"""height""": 18, """width""": 18}
A_ : Tuple = parent
A_ : Any = batch_size
A_ : Tuple = num_channels
A_ : int = image_size
A_ : Union[str, Any] = min_resolution
A_ : List[str] = max_resolution
A_ : str = do_resize
A_ : Dict = size
A_ : str = do_normalize
A_ : Union[str, Any] = image_mean
A_ : str = image_std
def _a ( self : Union[str, Any] ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = DPTImageProcessor if is_vision_available() else None
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Tuple = DPTImageProcessingTester(self )
@property
def _a ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self : int ):
'''simple docstring'''
A_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a ,"""image_mean""" ) )
self.assertTrue(hasattr(_a ,"""image_std""" ) )
self.assertTrue(hasattr(_a ,"""do_normalize""" ) )
self.assertTrue(hasattr(_a ,"""do_resize""" ) )
self.assertTrue(hasattr(_a ,"""size""" ) )
def _a ( self : Any ):
'''simple docstring'''
A_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 18} )
A_ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a ,Image.Image )
# Test not batched input
A_ : 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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
A_ : Union[str, Any] = image_processing(_a ,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 _a ( self : Tuple ):
'''simple docstring'''
A_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A_ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_a ,numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a ,np.ndarray )
# Test not batched input
A_ : List[Any] = 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
A_ : Optional[Any] = image_processing(_a ,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 _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A_ : str = prepare_image_inputs(self.image_processor_tester ,equal_resolution=_a ,torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a ,torch.Tensor )
# Test not batched input
A_ : List[str] = 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
A_ : Union[str, Any] = image_processing(_a ,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"""],
) ,)
| 27 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__magic_name__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : Optional[Any] ,_a : bool = True ,_a : Dict[str, int] = None ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : bool = True ,_a : Dict[str, int] = None ,_a : bool = True ,_a : Union[int, float] = 1 / 255 ,_a : bool = True ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = True ,**_a : Dict ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Tuple = size if size is not None else {"""shortest_edge""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
A_ : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a ,param_name="""crop_size""" )
A_ : Any = do_resize
A_ : List[str] = size
A_ : Union[str, Any] = resample
A_ : Dict = do_center_crop
A_ : List[str] = crop_size
A_ : Any = do_rescale
A_ : Union[str, Any] = rescale_factor
A_ : Any = do_normalize
A_ : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A_ : List[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
A_ : Tuple = do_convert_rgb
def _a ( self : Optional[int] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[Any] ,):
'''simple docstring'''
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A_ : Tuple = get_resize_output_image_size(_a ,size=size["""shortest_edge"""] ,default_to_square=_a )
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def _a ( self : List[Any] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[int] ,):
'''simple docstring'''
A_ : Optional[int] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_a ,size=(size["""height"""], size["""width"""]) ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[int, float] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Any ,):
'''simple docstring'''
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[float, List[float]] ,_a : Union[float, List[float]] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[str] ,):
'''simple docstring'''
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def _a ( self : Optional[Any] ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : int = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[ChannelDimension] = ChannelDimension.FIRST ,**_a : int ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : Tuple = size if size is not None else self.size
A_ : Optional[int] = get_size_dict(_a ,param_name="""size""" ,default_to_square=_a )
A_ : List[str] = resample if resample is not None else self.resample
A_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : Any = crop_size if crop_size is not None else self.crop_size
A_ : int = get_size_dict(_a ,param_name="""crop_size""" ,default_to_square=_a )
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : Any = do_normalize if do_normalize is not None else self.do_normalize
A_ : int = image_mean if image_mean is not None else self.image_mean
A_ : int = image_std if image_std is not None else self.image_std
A_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A_ : int = make_list_of_images(_a )
if not valid_images(_a ):
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.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A_ : Optional[int] = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
A_ : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
A_ : int = [self.resize(image=_a ,size=_a ,resample=_a ) for image in images]
if do_center_crop:
A_ : Tuple = [self.center_crop(image=_a ,size=_a ) for image in images]
if do_rescale:
A_ : List[str] = [self.rescale(image=_a ,scale=_a ) for image in images]
if do_normalize:
A_ : Any = [self.normalize(image=_a ,mean=_a ,std=_a ) for image in images]
A_ : List[str] = [to_channel_dimension_format(_a ,_a ) for image in images]
A_ : List[str] = {"""pixel_values""": images}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 | 1 |
'''simple docstring'''
from itertools import permutations
def lowerCamelCase ( lowerCamelCase : tuple):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
A_ : Union[str, Any] = [7, 11, 13, 17]
for i, test in enumerate(lowerCamelCase):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowerCamelCase ( lowerCamelCase : int = 10):
return sum(
int("""""".join(map(lowerCamelCase , lowerCamelCase)))
for num in permutations(range(lowerCamelCase))
if is_substring_divisible(lowerCamelCase))
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,*_a : Optional[Any] ,**_a : Optional[int] ):
'''simple docstring'''
warnings.warn(
"""The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use OwlViTImageProcessor instead.""" ,_a ,)
super().__init__(*_a ,**_a )
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCamelCase ( lowerCamelCase : int = 200_0000):
A_ : list[int] = [0]
A_ : int
for idx in range(1 , ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle_numbers[-1] + idx)
# we want this to be as close as possible to target
A_ : int = 0
# the area corresponding to the grid that gives the product closest to target
A_ : int = 0
# an estimate of b, using the quadratic formula
A_ : float
# the largest integer less than b_estimate
A_ : int
# the largest integer less than b_estimate
A_ : int
# the triangle number corresponding to b_floor
A_ : int
# the triangle number corresponding to b_ceil
A_ : int
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1):
A_ : Tuple = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
A_ : Tuple = floor(lowerCamelCase)
A_ : Optional[int] = ceil(lowerCamelCase)
A_ : Tuple = triangle_numbers[b_floor]
A_ : Optional[Any] = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a) < abs(
target - best_product):
A_ : str = triangle_b_first_guess * triangle_a
A_ : List[Any] = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a) < abs(
target - best_product):
A_ : Optional[int] = triangle_b_second_guess * triangle_a
A_ : int = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : complex , lowerCamelCase : str = "x" , lowerCamelCase : float = 10**-10 , lowerCamelCase : int = 1 , ):
A_ : int = symbols(lowerCamelCase)
A_ : List[Any] = lambdify(lowerCamelCase , lowerCamelCase)
A_ : List[str] = lambdify(lowerCamelCase , diff(lowerCamelCase , lowerCamelCase))
A_ : str = starting_point
while True:
if diff_function(lowerCamelCase) != 0:
A_ : int = prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
lowerCamelCase)
else:
raise ZeroDivisionError("""Could not find root""") from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess) < precision:
return next_guess
A_ : Union[str, Any] = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}""",
)
# Find root of cos(x)
print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
| 27 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__magic_name__ = 'examples/'
__magic_name__ = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__magic_name__ = {
'init': 'src/diffusers/__init__.py',
'setup': 'setup.py',
}
__magic_name__ = 'README.md'
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]):
with open(lowerCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""") as f:
A_ : int = f.read()
A_ , A_ : Union[str, Any] = REPLACE_PATTERNS[pattern]
A_ : Union[str, Any] = replace.replace("""VERSION""" , lowerCamelCase)
A_ : List[Any] = re_pattern.sub(lowerCamelCase , lowerCamelCase)
with open(lowerCamelCase , """w""" , encoding="""utf-8""" , newline="""\n""") as f:
f.write(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : str):
for folder, directories, fnames in os.walk(lowerCamelCase):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""")
if "legacy" in directories:
directories.remove("""legacy""")
for fname in fnames:
if fname.endswith(""".py"""):
update_version_in_file(os.path.join(lowerCamelCase , lowerCamelCase) , lowerCamelCase , pattern="""examples""")
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Tuple=False):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(lowerCamelCase , lowerCamelCase , lowerCamelCase)
if not patch:
update_version_in_examples(lowerCamelCase)
def lowerCamelCase ( ):
A_ : Optional[Any] = """🤗 Transformers currently provides the following architectures"""
A_ : Optional[int] = """1. Want to contribute a new model?"""
with open(lowerCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""") as f:
A_ : List[Any] = f.readlines()
# Find the start of the list.
A_ : Optional[int] = 0
while not lines[start_index].startswith(_start_prompt):
start_index += 1
start_index += 1
A_ : List[str] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt):
if lines[index].startswith("""1."""):
A_ : Optional[int] = lines[index].replace(
"""https://huggingface.co/docs/diffusers/main/model_doc""" , """https://huggingface.co/docs/diffusers/model_doc""" , )
index += 1
with open(lowerCamelCase , """w""" , encoding="""utf-8""" , newline="""\n""") as f:
f.writelines(lowerCamelCase)
def lowerCamelCase ( ):
with open(REPLACE_FILES["""init"""] , """r""") as f:
A_ : Optional[Any] = f.read()
A_ : int = REPLACE_PATTERNS["""init"""][0].search(lowerCamelCase).groups()[0]
return packaging.version.parse(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Dict=False):
A_ : Dict = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""")
if default_version.is_devrelease:
A_ : Optional[int] = default_version.base_version
elif patch:
A_ : int = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
A_ : Union[str, Any] = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
A_ : Tuple = input(F'Which version are you releasing? [{default_version}]')
if len(lowerCamelCase) == 0:
A_ : List[str] = default_version
print(F'Updating version to {version}.')
global_version_update(lowerCamelCase , patch=lowerCamelCase)
def lowerCamelCase ( ):
A_ : Optional[Any] = get_version()
A_ : Tuple = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
A_ : List[str] = current_version.base_version
# Check with the user we got that right.
A_ : List[Any] = input(F'Which version are we developing now? [{dev_version}]')
if len(lowerCamelCase) == 0:
A_ : Tuple = dev_version
print(F'Updating version to {version}.')
global_version_update(lowerCamelCase)
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
__magic_name__ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 27 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__magic_name__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_a : Dict ):
'''simple docstring'''
super().__init__()
A_ : List[str] = torchvision.models.resnetaaa(pretrained=_a )
A_ : int = list(model.children() )[:-2]
A_ : int = nn.Sequential(*_a )
A_ : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def _a ( self : str ,_a : Optional[int] ):
'''simple docstring'''
A_ : Tuple = self.pool(self.model(_a ) )
A_ : Any = torch.flatten(_a ,start_dim=2 )
A_ : str = out.transpose(1 ,2 ).contiguous()
return out # BxNx2048
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Dict ,_a : Dict ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Dict = [json.loads(_a ) for l in open(_a )]
A_ : Optional[int] = os.path.dirname(_a )
A_ : Optional[Any] = tokenizer
A_ : Optional[Any] = labels
A_ : List[Any] = len(_a )
A_ : str = max_seq_length
A_ : str = transforms
def __len__( self : str ):
'''simple docstring'''
return len(self.data )
def __getitem__( self : Tuple ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,add_special_tokens=_a ) )
A_ , A_ , A_ : Dict = sentence[0], sentence[1:-1], sentence[-1]
A_ : Optional[int] = sentence[: self.max_seq_length]
A_ : Any = torch.zeros(self.n_classes )
A_ : Tuple = 1
A_ : Optional[Any] = Image.open(os.path.join(self.data_dir ,self.data[index]["""img"""] ) ).convert("""RGB""" )
A_ : Union[str, Any] = self.transforms(_a )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def lowerCamelCase ( lowerCamelCase : str):
A_ : List[Any] = [len(row["""sentence"""]) for row in batch]
A_ , A_ : Dict = len(lowerCamelCase), max(lowerCamelCase)
A_ : Optional[int] = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
A_ : Tuple = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase , lowerCamelCase)):
A_ : str = input_row["""sentence"""]
A_ : Tuple = 1
A_ : int = torch.stack([row["""image"""] for row in batch])
A_ : str = torch.stack([row["""label"""] for row in batch])
A_ : List[Any] = torch.stack([row["""image_start_token"""] for row in batch])
A_ : Tuple = torch.stack([row["""image_end_token"""] for row in batch])
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase ( ):
return transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),
])
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 400_0000):
A_ : Dict = [0, 1]
A_ : str = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
A_ : Optional[int] = 0
for j in range(len(lowerCamelCase) - 1):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( lowerCamelCase : int):
if num <= 0:
A_ : List[Any] = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase)
A_ : str = [True] * (num + 1)
A_ : Tuple = []
A_ : str = 2
A_ : Any = int(math.sqrt(lowerCamelCase))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCamelCase)
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCamelCase):
if sieve[i] is True:
A_ : Union[str, Any] = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(lowerCamelCase)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 27 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : str = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Any = self.dummy_uncond_unet
A_ : List[str] = KarrasVeScheduler()
A_ : Union[str, Any] = KarrasVePipeline(unet=_a ,scheduler=_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = torch.manual_seed(0 )
A_ : Union[str, Any] = pipe(num_inference_steps=2 ,generator=_a ,output_type="""numpy""" ).images
A_ : Union[str, Any] = torch.manual_seed(0 )
A_ : Optional[int] = pipe(num_inference_steps=2 ,generator=_a ,output_type="""numpy""" ,return_dict=_a )[0]
A_ : Dict = image[0, -3:, -3:, -1]
A_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A_ : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Tuple = """google/ncsnpp-celebahq-256"""
A_ : Dict = UNetaDModel.from_pretrained(_a )
A_ : List[str] = KarrasVeScheduler()
A_ : Any = KarrasVePipeline(unet=_a ,scheduler=_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : str = torch.manual_seed(0 )
A_ : Optional[int] = pipe(num_inference_steps=20 ,generator=_a ,output_type="""numpy""" ).images
A_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
A_ : Dict = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 27 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
__magic_name__ = trt.Logger(trt.Logger.WARNING)
__magic_name__ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
__magic_name__ = logging.getLogger(__name__)
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=384,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=128,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
__magic_name__ = parser.parse_args()
if args.tokenizer_name:
__magic_name__ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
__magic_name__ = args.per_device_eval_batch_size
__magic_name__ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
__magic_name__ = True
__magic_name__ = 'temp_engine/bert-fp32.engine'
if args.fpaa:
__magic_name__ = 'temp_engine/bert-fp16.engine'
if args.inta:
__magic_name__ = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
__magic_name__ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
__magic_name__ = [network.get_input(i) for i in range(network.num_inputs)]
__magic_name__ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
__magic_name__ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
__magic_name__ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
__magic_name__ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[str]):
A_ : str = np.asarray(inputs["""input_ids"""] , dtype=np.intaa)
A_ : int = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa)
A_ : Optional[int] = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa)
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCamelCase)
# start time
A_ : List[Any] = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCamelCase) for d_inp in d_inputs] + [int(lowerCamelCase), int(lowerCamelCase)] , stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Synchronize the stream and take time
stream.synchronize()
# end time
A_ : str = time.time()
A_ : Tuple = end_time - start_time
A_ : Any = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__magic_name__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__magic_name__ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
__magic_name__ = raw_datasets['validation'].column_names
__magic_name__ = 'question' if 'question' in column_names else column_names[0]
__magic_name__ = 'context' if 'context' in column_names else column_names[1]
__magic_name__ = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
__magic_name__ = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
)
__magic_name__ = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCamelCase ( lowerCamelCase : Dict):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
A_ : List[Any] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
A_ : Optional[int] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=lowerCamelCase , stride=args.doc_stride , return_overflowing_tokens=lowerCamelCase , return_offsets_mapping=lowerCamelCase , padding="""max_length""" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
A_ : List[str] = tokenized_examples.pop("""overflow_to_sample_mapping""")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
A_ : Union[str, Any] = []
for i in range(len(tokenized_examples["""input_ids"""])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
A_ : Any = tokenized_examples.sequence_ids(lowerCamelCase)
A_ : Tuple = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
A_ : Union[str, Any] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["""id"""][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
A_ : Dict = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
]
return tokenized_examples
__magic_name__ = raw_datasets['validation']
# Validation Feature Creation
__magic_name__ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
__magic_name__ = default_data_collator
__magic_name__ = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__magic_name__ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any]="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
A_ : Tuple = postprocess_qa_predictions(
examples=lowerCamelCase , features=lowerCamelCase , predictions=lowerCamelCase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCamelCase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
A_ : Dict = [
{"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
]
else:
A_ : Union[str, Any] = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]
A_ : Any = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCamelCase , label_ids=lowerCamelCase)
__magic_name__ = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize
# Allocate device memory for inputs and outputs.
__magic_name__ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
__magic_name__ = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(f""" Num examples = {len(eval_dataset)}""")
logger.info(f""" Batch size = {args.per_device_eval_batch_size}""")
__magic_name__ = 0.0
__magic_name__ = 0
__magic_name__ = timeit.default_timer()
__magic_name__ = None
for step, batch in enumerate(eval_dataloader):
__magic_name__ , __magic_name__ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
__magic_name__ , __magic_name__ = outputs
__magic_name__ = torch.tensor(start_logits)
__magic_name__ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
__magic_name__ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
__magic_name__ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
__magic_name__ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
__magic_name__ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
__magic_name__ = nested_truncate(all_preds, len(eval_dataset))
__magic_name__ = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_000))
logger.info('Total Number of Inference = %d', niter)
__magic_name__ = post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")
| 27 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""onnx"""]
def __init__( self : Any ,*_a : int ,**_a : Optional[Any] ):
'''simple docstring'''
requires_backends(self ,["""onnx"""] )
@classmethod
def _a ( cls : List[Any] ,*_a : Tuple ,**_a : str ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
@classmethod
def _a ( cls : str ,*_a : List[Any] ,**_a : List[str] ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['ConvNextFeatureExtractor']
__magic_name__ = ['ConvNextImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvNextForImageClassification',
'ConvNextModel',
'ConvNextPreTrainedModel',
'ConvNextBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TFConvNextForImageClassification',
'TFConvNextModel',
'TFConvNextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 27 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
__magic_name__ = logging.getLogger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """token-classification"""
def __init__( self : Tuple ,_a : int ):
'''simple docstring'''
if type(_a ) == dict:
A_ : str = Namespace(**_a )
A_ : Dict = import_module("""tasks""" )
try:
A_ : Optional[Any] = getattr(_a ,hparams.task_type )
A_ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}' )
A_ : Tuple = self.token_classification_task.get_labels(hparams.labels )
A_ : str = CrossEntropyLoss().ignore_index
super().__init__(_a ,len(self.labels ) ,self.mode )
def _a ( self : int ,**_a : Any ):
'''simple docstring'''
return self.model(**_a )
def _a ( self : Optional[Any] ,_a : Union[str, Any] ,_a : int ):
'''simple docstring'''
A_ : str = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
A_ : str = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
A_ : str = self(**_a )
A_ : Dict = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _a ( self : Dict ):
'''simple docstring'''
A_ : Union[str, Any] = self.hparams
for mode in ["train", "dev", "test"]:
A_ : Union[str, Any] = self._feature_file(_a )
if os.path.exists(_a ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" ,_a )
A_ : Any = torch.load(_a )
else:
logger.info("""Creating features from dataset file at %s""" ,args.data_dir )
A_ : List[str] = self.token_classification_task.read_examples_from_file(args.data_dir ,_a )
A_ : Optional[Any] = self.token_classification_task.convert_examples_to_features(
_a ,self.labels ,args.max_seq_length ,self.tokenizer ,cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) ,cls_token=self.tokenizer.cls_token ,cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 ,sep_token=self.tokenizer.sep_token ,sep_token_extra=_a ,pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) ,pad_token=self.tokenizer.pad_token_id ,pad_token_segment_id=self.tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
logger.info("""Saving features into cached file %s""" ,_a )
torch.save(_a ,_a )
def _a ( self : Dict ,_a : int ,_a : int ,_a : bool = False ):
'''simple docstring'''
A_ : int = self._feature_file(_a )
logger.info("""Loading features from cached file %s""" ,_a )
A_ : str = torch.load(_a )
A_ : Union[str, Any] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long )
A_ : List[Any] = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long )
if features[0].token_type_ids is not None:
A_ : int = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long )
else:
A_ : Any = torch.tensor([0 for f in features] ,dtype=torch.long )
# HACK(we will not use this anymore soon)
A_ : List[Any] = torch.tensor([f.label_ids for f in features] ,dtype=torch.long )
return DataLoader(
TensorDataset(_a ,_a ,_a ,_a ) ,batch_size=_a )
def _a ( self : Optional[int] ,_a : List[str] ,_a : Tuple ):
'''simple docstring'''
"""Compute validation""" ""
A_ : List[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
A_ : List[str] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
A_ : Union[str, Any] = self(**_a )
A_ , A_ : int = outputs[:2]
A_ : List[Any] = logits.detach().cpu().numpy()
A_ : int = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _a ( self : Optional[Any] ,_a : List[str] ):
'''simple docstring'''
A_ : Tuple = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
A_ : Optional[Any] = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 )
A_ : List[str] = np.argmax(_a ,axis=2 )
A_ : Dict = np.concatenate([x["""target"""] for x in outputs] ,axis=0 )
A_ : Tuple = dict(enumerate(self.labels ) )
A_ : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
A_ : List[Any] = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
A_ : Union[str, Any] = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(_a ,_a ),
"""precision""": precision_score(_a ,_a ),
"""recall""": recall_score(_a ,_a ),
"""f1""": fa_score(_a ,_a ),
}
A_ : Any = dict(results.items() )
A_ : Any = results
return ret, preds_list, out_label_list
def _a ( self : Optional[Any] ,_a : Any ):
'''simple docstring'''
A_ , A_ , A_ : Tuple = self._eval_end(_a )
A_ : int = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _a ( self : Tuple ,_a : List[Any] ):
'''simple docstring'''
A_ , A_ , A_ : Tuple = self._eval_end(_a )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
A_ : str = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _a ( _a : Tuple ,_a : str ):
'''simple docstring'''
BaseTransformer.add_model_specific_args(_a ,_a )
parser.add_argument(
"""--task_type""" ,default="""NER""" ,type=_a ,help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" ,default=128 ,type=_a ,help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) ,)
parser.add_argument(
"""--labels""" ,default="""""" ,type=_a ,help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" ,)
parser.add_argument(
"""--gpus""" ,default=0 ,type=_a ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,)
parser.add_argument(
"""--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
__magic_name__ = NERTransformer.add_model_specific_args(parser, os.getcwd())
__magic_name__ = parser.parse_args()
__magic_name__ = NERTransformer(args)
__magic_name__ = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
__magic_name__ = sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
__magic_name__ = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 27 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_text_model"""
def __init__( self : Union[str, Any] ,_a : Any=49408 ,_a : Any=512 ,_a : Tuple=2048 ,_a : Dict=12 ,_a : Optional[int]=8 ,_a : Tuple=16 ,_a : Tuple="quick_gelu" ,_a : Optional[Any]=1e-5 ,_a : List[Any]=0.0 ,_a : Optional[int]=0.02 ,_a : Dict=1.0 ,_a : Dict=0 ,_a : Any=49406 ,_a : Tuple=49407 ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Optional[int] = intermediate_size
A_ : Optional[int] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : int = max_position_embeddings
A_ : str = hidden_act
A_ : Union[str, Any] = layer_norm_eps
A_ : Tuple = attention_dropout
A_ : Union[str, Any] = initializer_range
A_ : List[Any] = initializer_factor
@classmethod
def _a ( cls : List[str] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : int = cls.get_config_dict(_a ,**_a )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : Union[str, Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_vision_model"""
def __init__( self : List[Any] ,_a : Optional[Any]=768 ,_a : Tuple=3072 ,_a : Dict=12 ,_a : int=12 ,_a : Dict=3 ,_a : Tuple=768 ,_a : int=32 ,_a : int="quick_gelu" ,_a : List[Any]=1e-5 ,_a : Tuple=0.0 ,_a : List[Any]=0.02 ,_a : str=1.0 ,**_a : int ,):
'''simple docstring'''
super().__init__(**_a )
A_ : List[str] = hidden_size
A_ : Union[str, Any] = intermediate_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : int = num_channels
A_ : str = image_size
A_ : List[Any] = patch_size
A_ : int = hidden_act
A_ : List[Any] = layer_norm_eps
A_ : List[str] = attention_dropout
A_ : str = initializer_range
A_ : str = initializer_factor
@classmethod
def _a ( cls : List[Any] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : Optional[int] = cls.get_config_dict(_a ,**_a )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : List[str] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit"""
a_ = True
def __init__( self : Union[str, Any] ,_a : List[str]=None ,_a : List[str]=None ,_a : Dict=512 ,_a : List[Any]=2.6592 ,_a : Optional[Any]=True ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(**_a )
if text_config is None:
A_ : List[Any] = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
A_ : Tuple = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
A_ : Dict = OwlViTTextConfig(**_a )
A_ : Dict = OwlViTVisionConfig(**_a )
A_ : Any = projection_dim
A_ : Optional[int] = logit_scale_init_value
A_ : Optional[int] = return_dict
A_ : Dict = 1.0
@classmethod
def _a ( cls : Union[str, Any] ,_a : Union[str, os.PathLike] ,**_a : Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : List[Any] = cls.get_config_dict(_a ,**_a )
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
@classmethod
def _a ( cls : int ,_a : Dict ,_a : Dict ,**_a : List[str] ):
'''simple docstring'''
A_ : str = {}
A_ : int = text_config
A_ : Union[str, Any] = vision_config
return cls.from_dict(_a ,**_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = copy.deepcopy(self.__dict__ )
A_ : str = self.text_config.to_dict()
A_ : Optional[int] = self.vision_config.to_dict()
A_ : List[Any] = self.__class__.model_type
return output
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : int ):
'''simple docstring'''
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _a ( self : str ):
'''simple docstring'''
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
def _a ( self : int ,_a : "ProcessorMixin" ,_a : int = -1 ,_a : int = -1 ,_a : Optional["TensorType"] = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(
processor.tokenizer ,batch_size=_a ,seq_length=_a ,framework=_a )
A_ : Any = super().generate_dummy_inputs(
processor.image_processor ,batch_size=_a ,framework=_a )
return {**text_input_dict, **image_input_dict}
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 14
| 27 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """M-CLIP"""
def __init__( self : int ,_a : str=1024 ,_a : str=768 ,**_a : Optional[Any] ):
'''simple docstring'''
A_ : Any = transformerDimSize
A_ : Dict = imageDimSize
super().__init__(**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = MCLIPConfig
def __init__( self : Optional[Any] ,_a : List[Any] ,*_a : str ,**_a : List[Any] ):
'''simple docstring'''
super().__init__(_a ,*_a ,**_a )
A_ : Optional[Any] = XLMRobertaModel(_a )
A_ : Tuple = torch.nn.Linear(
in_features=config.transformerDimensions ,out_features=config.numDims )
def _a ( self : int ,_a : int ,_a : Any ):
'''simple docstring'''
A_ : Any = self.transformer(input_ids=_a ,attention_mask=_a )[0]
A_ : Any = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(_a ), embs
| 27 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""input_features""", """is_longer"""]
def __init__( self : Dict ,_a : Optional[int]=64 ,_a : List[Any]=48000 ,_a : str=480 ,_a : Optional[Any]=10 ,_a : Optional[int]=1024 ,_a : Tuple=0.0 ,_a : str=False ,_a : float = 0 ,_a : float = 14000 ,_a : int = None ,_a : str = "fusion" ,_a : str = "repeatpad" ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(
feature_size=_a ,sampling_rate=_a ,padding_value=_a ,return_attention_mask=_a ,**_a ,)
A_ : Tuple = top_db
A_ : Tuple = truncation
A_ : Optional[Any] = padding
A_ : Optional[int] = fft_window_size
A_ : Dict = (fft_window_size >> 1) + 1
A_ : Any = hop_length
A_ : List[Any] = max_length_s
A_ : Tuple = max_length_s * sampling_rate
A_ : Tuple = sampling_rate
A_ : Optional[int] = frequency_min
A_ : Tuple = frequency_max
A_ : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm=_a ,mel_scale="""htk""" ,)
A_ : Dict = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def _a ( self : int ):
'''simple docstring'''
A_ : int = copy.deepcopy(self.__dict__ )
A_ : Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _a ( self : Dict ,_a : np.array ,_a : Optional[np.array] = None ):
'''simple docstring'''
A_ : List[str] = spectrogram(
_a ,window_function(self.fft_window_size ,"""hann""" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=_a ,log_mel="""dB""" ,)
return log_mel_spectrogram.T
def _a ( self : Optional[int] ,_a : Dict ,_a : Optional[Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Dict = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : int = [0]
# randomly choose index for each part
A_ : List[str] = np.random.choice(ranges[0] )
A_ : int = np.random.choice(ranges[1] )
A_ : Optional[int] = np.random.choice(ranges[2] )
A_ : Tuple = mel[idx_front : idx_front + chunk_frames, :]
A_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
A_ : Dict = mel[idx_back : idx_back + chunk_frames, :]
A_ : Optional[int] = torch.tensor(mel[None, None, :] )
A_ : Dict = torch.nn.functional.interpolate(
_a ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=_a )
A_ : str = mel_shrink[0][0].numpy()
A_ : Tuple = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def _a ( self : Dict ,_a : np.array ,_a : Optional[Any] ,_a : int ,_a : Dict ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
A_ : Dict = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
A_ : Tuple = len(_a ) - max_length
A_ : Optional[int] = np.random.randint(0 ,overflow + 1 )
A_ : List[Any] = waveform[idx : idx + max_length]
A_ : Optional[Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
A_ : Dict = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Tuple = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
A_ : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
A_ : Optional[int] = np.stack([mel, mel, mel, mel] ,axis=0 )
A_ : str = False
else:
A_ : str = self._random_mel_fusion(_a ,_a ,_a )
A_ : Optional[Any] = True
else:
raise NotImplementedError(f'data_truncating {truncation} not implemented' )
else:
A_ : Optional[int] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
A_ : int = int(max_length / len(_a ) )
A_ : Any = np.stack(np.tile(_a ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
A_ : List[str] = int(max_length / len(_a ) )
A_ : Optional[Any] = np.stack(np.tile(_a ,_a ) )
A_ : Any = np.pad(_a ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
A_ : List[Any] = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
A_ : Union[str, Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[Any] ,_a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_a : str = None ,_a : Optional[str] = None ,_a : Optional[int] = None ,_a : Optional[int] = None ,_a : Optional[Union[str, TensorType]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : List[str] = truncation if truncation is not None else self.truncation
A_ : List[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
A_ : Any = isinstance(_a ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A_ : int = is_batched_numpy or (
isinstance(_a ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
A_ : Optional[int] = [np.asarray(_a ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_a ,np.ndarray ):
A_ : str = np.asarray(_a ,dtype=np.floataa )
elif isinstance(_a ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A_ : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ : Any = [np.asarray(_a )]
# convert to mel spectrogram, truncate and pad if needed.
A_ : str = [
self._get_input_mel(_a ,max_length if max_length else self.nb_max_samples ,_a ,_a )
for waveform in raw_speech
]
A_ : int = []
A_ : Any = []
for mel, longer in padded_inputs:
input_mel.append(_a )
is_longer.append(_a )
if truncation == "fusion" and sum(_a ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
A_ : List[Any] = np.random.randint(0 ,len(_a ) )
A_ : List[str] = True
if isinstance(input_mel[0] ,_a ):
A_ : Tuple = [np.asarray(_a ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
A_ : List[str] = [[longer] for longer in is_longer]
A_ : Optional[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
A_ : int = BatchFeature(_a )
if return_tensors is not None:
A_ : int = input_features.convert_to_tensors(_a )
return input_features
| 27 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 27 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : List[Any] ,_a : Dict=13 ,_a : List[Any]=7 ,_a : Optional[Any]=True ,_a : Any=True ,_a : Optional[int]=True ,_a : Union[str, Any]=99 ,_a : Union[str, Any]=32 ,_a : List[str]=5 ,_a : List[str]=4 ,_a : Dict=37 ,_a : List[Any]="gelu" ,_a : int=0.1 ,_a : Optional[int]=0.1 ,_a : Tuple=512 ,_a : Union[str, Any]=16 ,_a : Optional[Any]=2 ,_a : Optional[Any]=0.02 ,_a : Optional[int]=3 ,_a : str=4 ,_a : Optional[Any]=None ,):
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : str = batch_size
A_ : int = seq_length
A_ : Union[str, Any] = is_training
A_ : Optional[Any] = use_token_type_ids
A_ : int = use_labels
A_ : Dict = vocab_size
A_ : List[Any] = hidden_size
A_ : Tuple = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : int = intermediate_size
A_ : Tuple = hidden_act
A_ : int = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : Any = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Tuple = type_sequence_label_size
A_ : int = initializer_range
A_ : Optional[Any] = num_labels
A_ : str = num_choices
A_ : Optional[Any] = scope
A_ : List[Any] = self.vocab_size - 1
def _a ( self : Any ):
'''simple docstring'''
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : List[Any] = None
if self.use_token_type_ids:
A_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : int = None
A_ : str = None
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Any = ids_tensor([self.batch_size] ,self.num_choices )
A_ : List[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,pad_token_id=self.pad_token_id ,)
A_ : Tuple = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _a ( self : Optional[int] ,_a : List[str] ,_a : str ,_a : int ,_a : int ,*_a : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[Any] = OpenAIGPTModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,head_mask=_a )
A_ : str = model(_a ,token_type_ids=_a )
A_ : Dict = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Dict ,_a : Optional[int] ,_a : Union[str, Any] ,_a : Dict ,_a : List[str] ,*_a : str ):
'''simple docstring'''
A_ : str = OpenAIGPTLMHeadModel(_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Any ,_a : Dict ,_a : List[Any] ,_a : Dict ,_a : Union[str, Any] ,*_a : str ):
'''simple docstring'''
A_ : Any = OpenAIGPTDoubleHeadsModel(_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : List[str] ,_a : str ,_a : Tuple ,_a : Dict ,_a : Tuple ,*_a : Dict ):
'''simple docstring'''
A_ : List[str] = self.num_labels
A_ : int = OpenAIGPTForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Optional[Any] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : str = config_and_inputs
A_ : int = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
a_ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
a_ = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _a ( self : Tuple ,_a : Optional[int] ,_a : str ,_a : List[str] ,_a : List[str] ,_a : Any ):
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _a ( self : Optional[int] ,_a : str ,_a : Dict ,_a : Optional[int]=False ):
'''simple docstring'''
A_ : Any = super()._prepare_for_class(_a ,_a ,return_labels=_a )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
A_ : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,dtype=torch.long ,device=_a ,)
A_ : Any = inputs_dict["""labels"""]
A_ : Any = inputs_dict["""labels"""]
A_ : Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) ,dtype=torch.long ,device=_a ,)
A_ : int = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=_a )
return inputs_dict
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Tuple = OpenAIGPTModelTester(self )
A_ : Optional[int] = ConfigTester(self ,config_class=_a ,n_embd=37 )
def _a ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_a )
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a )
@slow
def _a ( self : List[Any] ):
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = OpenAIGPTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Dict = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(_a )
A_ : Dict = torch.tensor([[481, 4735, 544]] ,dtype=torch.long ,device=_a ) # the president is
A_ : Dict = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
A_ : int = model.generate(_a ,do_sample=_a )
self.assertListEqual(output_ids[0].tolist() ,_a )
| 27 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """FlavaImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Optional[int] ,_a : List[Any]=None ,_a : int=None ,**_a : Tuple ):
'''simple docstring'''
A_ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[str] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Tuple ,_a : Optional[ImageInput] = None ,_a : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = False ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
A_ : Optional[int] = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
if images is not None:
A_ : Dict = self.image_processor(
_a ,return_image_mask=_a ,return_codebook_pixels=_a ,return_tensors=_a ,**_a ,)
if text is not None and images is not None:
encoding.update(_a )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_a ) ,tensor_type=_a )
def _a ( self : Optional[int] ,*_a : Tuple ,**_a : int ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : Union[str, Any] ,*_a : List[Any] ,**_a : List[Any] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Any = self.tokenizer.model_input_names
A_ : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : Union[str, Any] ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 |
'''simple docstring'''
import baseaa
def lowerCamelCase ( lowerCamelCase : str):
return baseaa.aaaencode(string.encode("""utf-8"""))
def lowerCamelCase ( lowerCamelCase : bytes):
return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int):
A_ : Optional[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCamelCase ( lowerCamelCase : int = 100):
A_ : str = 1
A_ : int = 2
for i in range(2 , max_n + 1):
A_ : Any = pre_numerator
A_ : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1
A_ : str = cur_numerator
A_ : str = e_cont * pre_numerator + temp
return sum_digits(lowerCamelCase)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def lowerCamelCase ( lowerCamelCase : str):
# word like '180' or '身高' or '神'
for char in word:
A_ : Optional[Any] = ord(lowerCamelCase)
if not _is_chinese_char(lowerCamelCase):
return 0
return 1
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Any = set()
for token in tokens:
A_ : str = len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
if chinese_word:
word_set.add(lowerCamelCase)
A_ : Any = list(lowerCamelCase)
return word_list
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : set()):
if not chinese_word_set:
return bert_tokens
A_ : Any = max([len(lowerCamelCase) for w in chinese_word_set])
A_ : str = bert_tokens
A_ , A_ : Any = 0, len(lowerCamelCase)
while start < end:
A_ : Tuple = True
if is_chinese(bert_word[start]):
A_ : List[str] = min(end - start , lowerCamelCase)
for i in range(lowerCamelCase , 1 , -1):
A_ : Tuple = """""".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
A_ : Dict = """##""" + bert_word[j]
A_ : str = start + i
A_ : Dict = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : LTP , lowerCamelCase : BertTokenizer):
A_ : Union[str, Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : List[Any] = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""]).cws
A_ : int = [get_chinese_word(lowerCamelCase) for r in res]
ltp_res.extend(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : List[Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : Dict = bert_tokenizer(lines[i : i + 100] , add_special_tokens=lowerCamelCase , truncation=lowerCamelCase , max_length=512)
bert_res.extend(res["""input_ids"""])
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Union[str, Any] = []
for input_ids, chinese_word in zip(lowerCamelCase , lowerCamelCase):
A_ : List[Any] = []
for id in input_ids:
A_ : List[Any] = bert_tokenizer._convert_id_to_token(lowerCamelCase)
input_tokens.append(lowerCamelCase)
A_ : int = add_sub_symbol(lowerCamelCase , lowerCamelCase)
A_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCamelCase):
if token[:2] == "##":
A_ : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
ref_id.append(lowerCamelCase)
ref_ids.append(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
return ref_ids
def lowerCamelCase ( lowerCamelCase : Tuple):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""") as f:
A_ : Optional[int] = f.readlines()
A_ : Union[str, Any] = [line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ : Optional[Any] = LTP(args.ltp) # faster in GPU device
A_ : Dict = BertTokenizer.from_pretrained(args.bert)
A_ : str = prepare_ref(lowerCamelCase , lowerCamelCase , lowerCamelCase)
with open(args.save_path , """w""" , encoding="""utf-8""") as f:
A_ : Optional[Any] = [json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
f.writelines(lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = 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',
)
__magic_name__ = parser.parse_args()
main(args)
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : str ,_a : Dict ,):
'''simple docstring'''
A_ : Union[str, Any] = parent
A_ : Union[str, Any] = 13
A_ : List[str] = 7
A_ : int = True
A_ : Optional[int] = True
A_ : Any = True
A_ : Any = 99
A_ : Union[str, Any] = 32
A_ : Any = 2
A_ : Dict = 4
A_ : Tuple = 37
A_ : List[str] = """gelu"""
A_ : Dict = 0.1
A_ : str = 0.1
A_ : int = 512
A_ : str = 16
A_ : Dict = 2
A_ : Dict = 0.02
A_ : Dict = 3
A_ : int = 4
A_ : List[str] = None
def _a ( self : str ):
'''simple docstring'''
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : Union[str, Any] = None
if self.use_input_mask:
A_ : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : List[Any] = None
A_ : Optional[int] = None
A_ : str = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.num_choices )
A_ : Any = EsmConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,pad_token_id=1 ,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 ,initializer_range=self.initializer_range ,)
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : List[str] ):
'''simple docstring'''
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : str = self.prepare_config_and_inputs()
A_ : Optional[Any] = True
A_ : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
A_ : int = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _a ( self : Any ,_a : List[str] ,_a : str ,_a : Dict ,_a : Optional[Any] ,_a : Tuple ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : str = TFEsmModel(config=_a )
A_ : Union[str, Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
A_ : Tuple = model(_a )
A_ : Union[str, Any] = [input_ids, input_mask]
A_ : Dict = model(_a )
A_ : List[str] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] ,_a : str ,_a : int ,_a : List[str] ,_a : Dict ,_a : List[str] ,_a : Optional[int] ,_a : Any ,_a : int ,):
'''simple docstring'''
A_ : Dict = True
A_ : Tuple = TFEsmModel(config=_a )
A_ : Dict = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""encoder_hidden_states""": encoder_hidden_states,
"""encoder_attention_mask""": encoder_attention_mask,
}
A_ : Tuple = model(_a )
A_ : Union[str, Any] = [input_ids, input_mask]
A_ : Tuple = model(_a ,encoder_hidden_states=_a )
# Also check the case where encoder outputs are not passed
A_ : Union[str, Any] = model(_a ,attention_mask=_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : str ,_a : List[str] ,_a : List[Any] ,_a : Any ,_a : List[Any] ,_a : Dict ,_a : Any ):
'''simple docstring'''
A_ : Optional[Any] = TFEsmForMaskedLM(config=_a )
A_ : List[str] = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Optional[int] ,_a : str ,_a : int ,_a : List[Any] ,_a : Union[str, Any] ,_a : Any ,_a : List[Any] ):
'''simple docstring'''
A_ : List[Any] = self.num_labels
A_ : Optional[int] = TFEsmForTokenClassification(config=_a )
A_ : List[str] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
A_ : List[str] = model(_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : str ):
'''simple docstring'''
A_ : str = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Dict = config_and_inputs
A_ : List[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
a_ = (
{
"""feature-extraction""": TFEsmModel,
"""fill-mask""": TFEsmForMaskedLM,
"""text-classification""": TFEsmForSequenceClassification,
"""token-classification""": TFEsmForTokenClassification,
"""zero-shot""": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
a_ = False
a_ = False
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Tuple = TFEsmModelTester(self )
A_ : Union[str, Any] = ConfigTester(self ,config_class=_a ,hidden_size=37 )
def _a ( self : List[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self : Any ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_a )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_a )
def _a ( self : Any ):
'''simple docstring'''
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_a )
@slow
def _a ( self : List[Any] ):
'''simple docstring'''
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = TFEsmModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip("""Protein models do not support embedding resizing.""" )
def _a ( self : str ):
'''simple docstring'''
pass
@unittest.skip("""Protein models do not support embedding resizing.""" )
def _a ( self : Optional[int] ):
'''simple docstring'''
pass
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ , A_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_a )
assert isinstance(model.get_input_embeddings() ,tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
A_ : Dict = model.get_bias()
assert isinstance(_a ,_a )
for k, v in name.items():
assert isinstance(_a ,tf.Variable )
else:
A_ : str = model.get_output_embeddings()
assert x is None
A_ : Any = model.get_bias()
assert name is None
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : List[str] = TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
A_ : Dict = tf.constant([[0, 1, 2, 3, 4, 5]] )
A_ : int = model(_a )[0]
A_ : int = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) ,_a )
# compare the actual values for a slice.
A_ : List[Any] = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1e-2 ) )
@slow
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Tuple = TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
A_ : str = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
A_ : Optional[int] = model(_a )[0]
# compare the actual values for a slice.
A_ : Union[str, Any] = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1e-4 ) )
| 27 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """ViltImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] ,_a : Optional[Any]=None ,_a : List[str]=None ,**_a : Any ):
'''simple docstring'''
A_ : 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.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[Any] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Any ,_a : Tuple ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
# add pixel_values + pixel_mask
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Any ,**_a : Any ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : int ,*_a : int ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : int ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
__magic_name__ = None
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
__magic_name__ = {
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
),
},
}
__magic_name__ = {
'moussaKam/mbarthez': 1_024,
'moussaKam/barthez': 1_024,
'moussaKam/barthez-orangesum-title': 1_024,
}
__magic_name__ = '▁'
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = BarthezTokenizer
def __init__( self : List[str] ,_a : Dict=None ,_a : Optional[int]=None ,_a : int="<s>" ,_a : Dict="</s>" ,_a : Union[str, Any]="</s>" ,_a : int="<s>" ,_a : int="<unk>" ,_a : int="<pad>" ,_a : Union[str, Any]="<mask>" ,**_a : int ,):
'''simple docstring'''
A_ : int = AddedToken(_a ,lstrip=_a ,rstrip=_a ) if isinstance(_a ,_a ) else mask_token
super().__init__(
_a ,tokenizer_file=_a ,bos_token=_a ,eos_token=_a ,unk_token=_a ,sep_token=_a ,cls_token=_a ,pad_token=_a ,mask_token=_a ,**_a ,)
A_ : Any = vocab_file
A_ : Any = False if not self.vocab_file else True
def _a ( self : Any ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A_ : int = [self.cls_token_id]
A_ : List[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self : List[str] ,_a : List[int] ,_a : Optional[List[int]] = None ):
'''simple docstring'''
A_ : Optional[Any] = [self.sep_token_id]
A_ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self : Optional[Any] ,_a : str ,_a : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : Optional[Any] = os.path.join(
_a ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file ,_a )
return (out_vocab_file,)
| 27 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""torch""", """torchsde"""]
def __init__( self : Any ,*_a : Union[str, Any] ,**_a : Optional[int] ):
'''simple docstring'''
requires_backends(self ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : Optional[int] ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : List[Any] ,*_a : Tuple ,**_a : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
| 27 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def lowerCamelCase ( lowerCamelCase : int = 100_0000):
A_ : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2):
if outer_width**2 > limit:
A_ : List[str] = max(ceil(sqrt(outer_width**2 - limit)) , 1)
else:
A_ : Optional[int] = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]="pt"):
A_ : Optional[int] = {"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase) and not line.startswith(""" """) else {}
A_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , ):
A_ : Dict = input_ids.ne(lowerCamelCase).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Dict ,_a : Tuple ,_a : Tuple="train" ,_a : Optional[int]=None ,_a : Any=None ,_a : int=None ,_a : Union[str, Any]="" ,):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = Path(_a ).joinpath(type_path + """.source""" )
A_ : Any = Path(_a ).joinpath(type_path + """.target""" )
A_ : Dict = self.get_char_lens(self.src_file )
A_ : Optional[int] = max_source_length
A_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A_ : List[Any] = tokenizer
A_ : Optional[Any] = prefix
if n_obs is not None:
A_ : Any = self.src_lens[:n_obs]
A_ : Optional[int] = src_lang
A_ : Tuple = tgt_lang
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : List[str] ,_a : Tuple ):
'''simple docstring'''
A_ : int = index + 1 # linecache starts at 1
A_ : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,_a ).rstrip("""\n""" )
A_ : Dict = linecache.getline(str(self.tgt_file ) ,_a ).rstrip("""\n""" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ : List[str] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_a ) else self.tokenizer
)
A_ : Any = self.tokenizer.generator if isinstance(self.tokenizer ,_a ) else self.tokenizer
A_ : Optional[int] = encode_line(_a ,_a ,self.max_source_length ,"""right""" )
A_ : Optional[int] = encode_line(_a ,_a ,self.max_target_length ,"""right""" )
A_ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
A_ : Dict = target_inputs["""input_ids"""].squeeze()
A_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( _a : int ):
'''simple docstring'''
return [len(_a ) for x in Path(_a ).open().readlines()]
def _a ( self : Optional[int] ,_a : Dict ):
'''simple docstring'''
A_ : str = torch.stack([x["""input_ids"""] for x in batch] )
A_ : Optional[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
A_ : str = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : List[str] = trim_batch(_a ,_a )
A_ , A_ : Union[str, Any] = trim_batch(_a ,_a ,attention_mask=_a )
A_ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__magic_name__ = getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[List]):
return list(itertools.chain.from_iterable(lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json"""))
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : List[str]=4 , **lowerCamelCase : List[str]):
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase) as f:
return json.load(lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[str] = git.Repo(search_parent_directories=lowerCamelCase)
A_ : Union[str, Any] = {
"""repo_id""": str(lowerCamelCase),
"""repo_sha""": str(repo.head.object.hexsha),
"""repo_branch""": str(repo.active_branch),
"""hostname""": str(socket.gethostname()),
}
return repo_infos
def lowerCamelCase ( lowerCamelCase : Callable , lowerCamelCase : Iterable):
return list(map(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
with open(lowerCamelCase , """wb""") as f:
return pickle.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str]):
def remove_articles(lowerCamelCase : Any):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , lowerCamelCase)
def white_space_fix(lowerCamelCase : List[Any]):
return " ".join(text.split())
def remove_punc(lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(lowerCamelCase : List[str]):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = normalize_answer(lowerCamelCase).split()
A_ : Dict = normalize_answer(lowerCamelCase).split()
A_ : int = Counter(lowerCamelCase) & Counter(lowerCamelCase)
A_ : Any = sum(common.values())
if num_same == 0:
return 0
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Any):
return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[str]):
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Any = 0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase):
em += exact_match_score(lowerCamelCase , lowerCamelCase)
if len(lowerCamelCase) > 0:
em /= len(lowerCamelCase)
return {"em": em}
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return model_prefix.startswith("""rag""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
A_ : Optional[Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ : Tuple = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase):
if not hasattr(lowerCamelCase , lowerCamelCase) and not hasattr(lowerCamelCase , equivalent_param[p]):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
continue
A_ : Tuple = p if hasattr(lowerCamelCase , lowerCamelCase) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
return hparams, config
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : List[Any]): # noqa: E741
A_ : Optional[int] = len(lowerCamelCase)
A_ : Tuple = 0
A_ : Tuple = [0] * n
A_ : Dict = [False] * n
A_ : int = [False] * n
def dfs(lowerCamelCase : Tuple , lowerCamelCase : str , lowerCamelCase : Optional[Any] , lowerCamelCase : Union[str, Any]):
if parent == root:
out_edge_count += 1
A_ : Optional[int] = True
A_ : Any = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
A_ : str = dfs(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
A_ : Any = min(low[at] , low[to])
# AP found via bridge
if at < low[to]:
A_ : int = True
# AP found via cycle
if at == low[to]:
A_ : List[Any] = True
else:
A_ : Any = min(low[at] , lowerCamelCase)
return out_edge_count
for i in range(lowerCamelCase):
if not visited[i]:
A_ : Tuple = 0
A_ : int = dfs(lowerCamelCase , lowerCamelCase , -1 , lowerCamelCase)
A_ : Union[str, Any] = out_edge_count > 1
for x in range(len(lowerCamelCase)):
if is_art[x] is True:
print(lowerCamelCase)
# Adjacency list of graph
__magic_name__ = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(100, 0.2_5) = }""")
print(f"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")
| 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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'''
a_ = KandinskyVaaControlnetPipeline
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a_ = False
@property
def _a ( self : Any ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return self.time_input_dim
@property
def _a ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 100
@property
def _a ( self : List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[Any] = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
A_ : Tuple = UNetaDConditionModel(**_a )
return model
@property
def _a ( self : List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Optional[Any] = self.dummy_unet
A_ : int = self.dummy_movq
A_ : Tuple = DDIMScheduler(
num_train_timesteps=1000 ,beta_schedule="""linear""" ,beta_start=0.00085 ,beta_end=0.012 ,clip_sample=_a ,set_alpha_to_one=_a ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=_a ,)
A_ : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _a ( self : Dict ,_a : str ,_a : Union[str, Any]=0 ):
'''simple docstring'''
A_ : Dict = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(_a ) ).to(_a )
A_ : int = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
_a )
# create hint
A_ : List[Any] = floats_tensor((1, 3, 64, 64) ,rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_a )
else:
A_ : str = torch.Generator(device=_a ).manual_seed(_a )
A_ : List[Any] = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[Any] = """cpu"""
A_ : List[str] = self.get_dummy_components()
A_ : Tuple = self.pipeline_class(**_a )
A_ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = pipe(**self.get_dummy_inputs(_a ) )
A_ : Tuple = output.images
A_ : Optional[Any] = pipe(
**self.get_dummy_inputs(_a ) ,return_dict=_a ,)[0]
A_ : Tuple = image[0, -3:, -3:, -1]
A_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : List[Any] = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
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 _a ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Any ):
'''simple docstring'''
A_ : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
A_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
A_ : Optional[int] = torch.from_numpy(np.array(_a ) ).float() / 255.0
A_ : List[Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
A_ : List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(_a )
A_ : Union[str, Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
A_ : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
A_ : Optional[Any] = """A robot, 4k photo"""
A_ : Any = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ , A_ : List[str] = pipe_prior(
_a ,generator=_a ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
A_ : int = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ : List[Any] = pipeline(
image_embeds=_a ,negative_image_embeds=_a ,hint=_a ,generator=_a ,num_inference_steps=100 ,output_type="""np""" ,)
A_ : Dict = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_a ,_a )
| 27 | 1 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__magic_name__ = 6_3_7_8_1_3_7.0
__magic_name__ = 6_3_5_6_7_5_2.3_1_4_2_4_5
__magic_name__ = 6_378_137
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float):
A_ : List[Any] = (AXIS_A - AXIS_B) / AXIS_A
A_ : int = atan((1 - flattening) * tan(radians(lowerCamelCase)))
A_ : List[str] = atan((1 - flattening) * tan(radians(lowerCamelCase)))
A_ : int = radians(lowerCamelCase)
A_ : List[Any] = radians(lowerCamelCase)
# Equation
A_ : Any = sin((phi_a - phi_a) / 2)
A_ : int = sin((lambda_a - lambda_a) / 2)
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
A_ : List[str] = sqrt(sin_sq_phi + (cos(lowerCamelCase) * cos(lowerCamelCase) * sin_sq_lambda))
return 2 * RADIUS * asin(lowerCamelCase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """deberta-v2"""
def __init__( self : Optional[Any] ,_a : Union[str, Any]=128100 ,_a : Optional[int]=1536 ,_a : Dict=24 ,_a : int=24 ,_a : Tuple=6144 ,_a : Union[str, Any]="gelu" ,_a : List[Any]=0.1 ,_a : Dict=0.1 ,_a : int=512 ,_a : int=0 ,_a : int=0.02 ,_a : int=1e-7 ,_a : List[str]=False ,_a : Union[str, Any]=-1 ,_a : List[Any]=0 ,_a : Optional[Any]=True ,_a : Tuple=None ,_a : Any=0 ,_a : int="gelu" ,**_a : Any ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : List[Any] = initializer_range
A_ : int = relative_attention
A_ : Tuple = max_relative_positions
A_ : int = pad_token_id
A_ : Tuple = position_biased_input
# Backwards compatibility
if type(_a ) == str:
A_ : str = [x.strip() for x in pos_att_type.lower().split("""|""" )]
A_ : Any = pos_att_type
A_ : Optional[int] = vocab_size
A_ : Tuple = layer_norm_eps
A_ : Any = kwargs.get("""pooler_hidden_size""" ,_a )
A_ : Union[str, Any] = pooler_dropout
A_ : List[Any] = pooler_hidden_act
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : Any = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
return 12
def _a ( self : int ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 3 ,_a : int = 40 ,_a : int = 40 ,_a : "PreTrainedTokenizerBase" = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(preprocessor=_a ,framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 27 | 1 |
'''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""input_values""", """attention_mask"""]
def __init__( self : int ,_a : int = 1 ,_a : int = 16000 ,_a : float = 0.0 ,_a : bool = False ,_a : int = 80 ,_a : int = 16 ,_a : int = 64 ,_a : str = "hann_window" ,_a : float = 1.0 ,_a : float = 80 ,_a : float = 7600 ,_a : float = 1e-10 ,_a : int = 2 ,_a : bool = True ,**_a : str ,):
'''simple docstring'''
super().__init__(feature_size=_a ,sampling_rate=_a ,padding_value=_a ,**_a )
A_ : Any = do_normalize
A_ : Union[str, Any] = return_attention_mask
A_ : Optional[Any] = num_mel_bins
A_ : str = hop_length
A_ : Dict = win_length
A_ : Dict = win_function
A_ : List[Any] = frame_signal_scale
A_ : List[str] = fmin
A_ : List[str] = fmax
A_ : Any = mel_floor
A_ : Optional[Any] = reduction_factor
A_ : List[Any] = win_length * sampling_rate // 1000
A_ : List[Any] = hop_length * sampling_rate // 1000
A_ : List[str] = optimal_fft_length(self.sample_size )
A_ : Optional[int] = (self.n_fft // 2) + 1
A_ : Dict = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=_a )
A_ : Tuple = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.num_mel_bins ,min_frequency=self.fmin ,max_frequency=self.fmax ,sampling_rate=self.sampling_rate ,norm="""slaney""" ,mel_scale="""slaney""" ,)
if frame_signal_scale != 1.0:
warnings.warn(
"""The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers""" ,_a ,)
if reduction_factor != 2.0:
warnings.warn(
"""The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers""" ,_a ,)
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def _a ( _a : List[np.ndarray] ,_a : List[np.ndarray] ,_a : float = 0.0 ):
'''simple docstring'''
if attention_mask is not None:
A_ : Optional[Any] = np.array(_a ,np.intaa )
A_ : List[str] = []
for vector, length in zip(_a ,attention_mask.sum(-1 ) ):
A_ : Optional[int] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
A_ : Union[str, Any] = padding_value
normed_input_values.append(_a )
else:
A_ : Optional[Any] = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def _a ( self : List[Any] ,_a : np.ndarray ,):
'''simple docstring'''
A_ : Tuple = spectrogram(
_a ,window=self.window ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,mel_filters=self.mel_filters ,mel_floor=self.mel_floor ,log_mel="""log10""" ,)
return log_mel_spec.T
def __call__( self : Optional[Any] ,_a : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None ,_a : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Optional[int] = None ,_a : bool = False ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[int] = None ,**_a : int ,):
'''simple docstring'''
if audio is None and audio_target is None:
raise ValueError("""You must provide either `audio` or `audio_target` values.""" )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if audio is not None:
A_ : Optional[int] = self._process_audio(
_a ,_a ,_a ,_a ,_a ,_a ,_a ,_a ,**_a ,)
else:
A_ : Union[str, Any] = None
if audio_target is not None:
A_ : str = self._process_audio(
_a ,_a ,_a ,_a ,_a ,_a ,_a ,_a ,**_a ,)
if inputs is None:
return inputs_target
else:
A_ : Optional[int] = inputs_target["""input_values"""]
A_ : Tuple = inputs_target.get("""attention_mask""" )
if decoder_attention_mask is not None:
A_ : Optional[int] = decoder_attention_mask
return inputs
def _a ( self : Union[str, Any] ,_a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_a : bool = False ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Optional[int] = None ,_a : bool = False ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[Union[str, TensorType]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : Optional[Any] = isinstance(_a ,np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A_ : Dict = is_batched_numpy or (
isinstance(_a ,(list, tuple) ) and (isinstance(speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
A_ : Optional[Any] = [np.asarray(_a ,dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(_a ,np.ndarray ):
A_ : Union[str, Any] = np.asarray(_a ,dtype=np.floataa )
elif isinstance(_a ,np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
A_ : Optional[int] = speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ : Optional[Any] = [speech]
# needed to make pad() work on spectrogram inputs
A_ : Dict = self.feature_size
# convert into correct format for padding
if is_target:
A_ : Dict = [self._extract_mel_features(_a ) for waveform in speech]
A_ : Optional[int] = BatchFeature({"""input_values""": features} )
A_ : Optional[int] = self.num_mel_bins
else:
A_ : List[str] = BatchFeature({"""input_values""": speech} )
A_ : int = self.pad(
_a ,padding=_a ,max_length=_a ,truncation=_a ,pad_to_multiple_of=_a ,return_attention_mask=_a ,**_a ,)
A_ : str = feature_size_hack
# convert input values to correct format
A_ : List[Any] = padded_inputs["""input_values"""]
if not isinstance(input_values[0] ,np.ndarray ):
A_ : Optional[int] = [np.asarray(_a ,dtype=np.floataa ) for array in input_values]
elif (
not isinstance(_a ,np.ndarray )
and isinstance(input_values[0] ,np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
A_ : Union[str, Any] = [array.astype(np.floataa ) for array in input_values]
elif isinstance(_a ,np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
A_ : int = input_values.astype(np.floataa )
# convert attention_mask to correct format
A_ : List[Any] = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
A_ : Any = [np.asarray(_a ,dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
A_ : Tuple = (
attention_mask
if self._get_padding_strategies(_a ,max_length=_a ) is not PaddingStrategy.DO_NOT_PAD
else None
)
A_ : List[str] = self.zero_mean_unit_var_norm(
padded_inputs["""input_values"""] ,attention_mask=_a ,padding_value=self.padding_value )
if return_tensors is not None:
A_ : int = padded_inputs.convert_to_tensors(_a )
return padded_inputs
def _a ( self : Any ):
'''simple docstring'''
A_ : Dict = super().to_dict()
# Don't serialize these as they are derived from the other properties.
A_ : List[Any] = ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
for name in names:
if name in output:
del output[name]
return output
| 27 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__magic_name__ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__magic_name__ = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
__magic_name__ = BeautifulSoup(res.text, 'html.parser')
__magic_name__ = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f"""https://google.com{link.get('href')}""")
| 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__magic_name__ = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['HerbertTokenizerFast']
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = """nezha"""
def __init__( self : int ,_a : Union[str, Any]=21128 ,_a : int=768 ,_a : Any=12 ,_a : List[str]=12 ,_a : str=3072 ,_a : int="gelu" ,_a : int=0.1 ,_a : str=0.1 ,_a : Tuple=512 ,_a : List[Any]=64 ,_a : Dict=2 ,_a : List[Any]=0.02 ,_a : Optional[Any]=1e-12 ,_a : List[Any]=0.1 ,_a : Union[str, Any]=0 ,_a : Any=2 ,_a : Union[str, Any]=3 ,_a : int=True ,**_a : int ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Dict = max_position_embeddings
A_ : Optional[Any] = max_relative_position
A_ : List[Any] = type_vocab_size
A_ : int = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Dict = classifier_dropout
A_ : int = use_cache
| 27 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
set_seed(770)
__magic_name__ = {
'c_attn': 'att_proj',
'c_proj': 'out_proj',
'c_fc': 'in_proj',
'transformer.': '',
'h.': 'layers.',
'ln_1': 'layernorm_1',
'ln_2': 'layernorm_2',
'ln_f': 'layernorm_final',
'wpe': 'position_embeds_layer',
'wte': 'input_embeds_layer',
}
__magic_name__ = {
'text_small': {
'repo_id': 'suno/bark',
'file_name': 'text.pt',
},
'coarse_small': {
'repo_id': 'suno/bark',
'file_name': 'coarse.pt',
},
'fine_small': {
'repo_id': 'suno/bark',
'file_name': 'fine.pt',
},
'text': {
'repo_id': 'suno/bark',
'file_name': 'text_2.pt',
},
'coarse': {
'repo_id': 'suno/bark',
'file_name': 'coarse_2.pt',
},
'fine': {
'repo_id': 'suno/bark',
'file_name': 'fine_2.pt',
},
}
__magic_name__ = os.path.dirname(os.path.abspath(__file__))
__magic_name__ = os.path.join(os.path.expanduser('~'), '.cache')
__magic_name__ = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def lowerCamelCase ( lowerCamelCase : Dict , lowerCamelCase : str=False):
A_ : Union[str, Any] = model_type
if use_small:
key += "_small"
return os.path.join(lowerCamelCase , REMOTE_MODEL_PATHS[key]["""file_name"""])
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple):
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase)
hf_hub_download(repo_id=lowerCamelCase , filename=lowerCamelCase , local_dir=lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Tuple=False , lowerCamelCase : Tuple="text"):
if model_type == "text":
A_ : Dict = BarkSemanticModel
A_ : Dict = BarkSemanticConfig
A_ : List[Any] = BarkSemanticGenerationConfig
elif model_type == "coarse":
A_ : str = BarkCoarseModel
A_ : Any = BarkCoarseConfig
A_ : Union[str, Any] = BarkCoarseGenerationConfig
elif model_type == "fine":
A_ : int = BarkFineModel
A_ : Optional[Any] = BarkFineConfig
A_ : List[str] = BarkFineGenerationConfig
else:
raise NotImplementedError()
A_ : Any = F'{model_type}_small' if use_small else model_type
A_ : Optional[Any] = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(lowerCamelCase):
logger.info(F'{model_type} model not found, downloading into `{CACHE_DIR}`.')
_download(model_info["""repo_id"""] , model_info["""file_name"""])
A_ : int = torch.load(lowerCamelCase , map_location=lowerCamelCase)
# this is a hack
A_ : Any = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
A_ : List[str] = model_args["""vocab_size"""]
A_ : List[Any] = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
A_ : str = model_args.pop("""n_head""")
A_ : List[str] = model_args.pop("""n_embd""")
A_ : List[str] = model_args.pop("""n_layer""")
A_ : Optional[int] = ConfigClass(**checkpoint["""model_args"""])
A_ : List[str] = ModelClass(config=lowerCamelCase)
A_ : int = GenerationConfigClass()
A_ : List[Any] = model_generation_config
A_ : Any = checkpoint["""model"""]
# fixup checkpoint
A_ : List[str] = """_orig_mod."""
for k, v in list(state_dict.items()):
if k.startswith(lowerCamelCase):
# replace part of the key with corresponding layer name in HF implementation
A_ : Union[str, Any] = k[len(lowerCamelCase) :]
for old_layer_name in new_layer_name_dict:
A_ : List[str] = new_k.replace(lowerCamelCase , new_layer_name_dict[old_layer_name])
A_ : Union[str, Any] = state_dict.pop(lowerCamelCase)
A_ : Any = set(state_dict.keys()) - set(model.state_dict().keys())
A_ : Dict = {k for k in extra_keys if not k.endswith(""".attn.bias""")}
A_ : str = set(model.state_dict().keys()) - set(state_dict.keys())
A_ : Optional[Any] = {k for k in missing_keys if not k.endswith(""".attn.bias""")}
if len(lowerCamelCase) != 0:
raise ValueError(F'extra keys found: {extra_keys}')
if len(lowerCamelCase) != 0:
raise ValueError(F'missing keys: {missing_keys}')
model.load_state_dict(lowerCamelCase , strict=lowerCamelCase)
A_ : int = model.num_parameters(exclude_embeddings=lowerCamelCase)
A_ : Optional[Any] = checkpoint["""best_val_loss"""].item()
logger.info(F'model loaded: {round(n_params/1E6 , 1)}M params, {round(lowerCamelCase , 3)} loss')
model.eval()
model.to(lowerCamelCase)
del checkpoint, state_dict
return model
def lowerCamelCase ( lowerCamelCase : List[Any] , lowerCamelCase : Dict=False , lowerCamelCase : str="text"):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
A_ : Tuple = """cpu""" # do conversion on cpu
A_ : str = _get_ckpt_path(lowerCamelCase , use_small=lowerCamelCase)
A_ : Optional[Any] = _load_model(lowerCamelCase , lowerCamelCase , model_type=lowerCamelCase , use_small=lowerCamelCase)
# load bark initial model
A_ : Optional[Any] = _bark_load_model(lowerCamelCase , """cpu""" , model_type=lowerCamelCase , use_small=lowerCamelCase)
if model_type == "text":
A_ : Optional[int] = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=lowerCamelCase) != bark_model.get_num_params():
raise ValueError("""initial and new models don't have the same number of parameters""")
# check if same output as the bark model
A_ : Dict = 5
A_ : Dict = 10
if model_type in ["text", "coarse"]:
A_ : Optional[int] = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int)
A_ : Optional[int] = bark_model(lowerCamelCase)[0]
A_ : str = model(lowerCamelCase)
# take last logits
A_ : List[str] = output_new_model_total.logits[:, [-1], :]
else:
A_ : Tuple = 3
A_ : Tuple = 8
A_ : Tuple = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int)
A_ : int = model(lowerCamelCase , lowerCamelCase)
A_ : Any = bark_model(lowerCamelCase , lowerCamelCase)
A_ : Dict = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("""initial and new outputs don't have the same shape""")
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError("""initial and new outputs are not equal""")
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
model.save_pretrained(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Dict , lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : Dict , lowerCamelCase : List[Any] , lowerCamelCase : Dict , ):
A_ : List[str] = os.path.join(lowerCamelCase , lowerCamelCase)
A_ : Optional[int] = BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase , """config.json"""))
A_ : int = BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase , """config.json"""))
A_ : Any = BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase , """config.json"""))
A_ : Optional[Any] = EncodecConfig.from_pretrained("""facebook/encodec_24khz""")
A_ : Any = BarkSemanticModel.from_pretrained(lowerCamelCase)
A_ : Optional[Any] = BarkCoarseModel.from_pretrained(lowerCamelCase)
A_ : Dict = BarkFineModel.from_pretrained(lowerCamelCase)
A_ : str = EncodecModel.from_pretrained("""facebook/encodec_24khz""")
A_ : Any = BarkConfig.from_sub_model_configs(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase)
A_ : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config)
A_ : str = BarkModel(lowerCamelCase)
A_ : int = semantic
A_ : Tuple = coarseAcoustic
A_ : Any = fineAcoustic
A_ : Tuple = codec
A_ : List[Any] = bark_generation_config
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
bark.save_pretrained(lowerCamelCase , repo_id=lowerCamelCase , push_to_hub=lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('model_type', type=str, help='text, coarse or fine.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')
__magic_name__ = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] = stack.pop()
explored.add(lowerCamelCase)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(lowerCamelCase)
return explored
__magic_name__ = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 27 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""note_seq"""]
def __init__( self : int ,*_a : Optional[int] ,**_a : Any ):
'''simple docstring'''
requires_backends(self ,["""note_seq"""] )
@classmethod
def _a ( cls : Optional[int] ,*_a : Optional[Any] ,**_a : Optional[Any] ):
'''simple docstring'''
requires_backends(cls ,["""note_seq"""] )
@classmethod
def _a ( cls : Any ,*_a : Any ,**_a : List[str] ):
'''simple docstring'''
requires_backends(cls ,["""note_seq"""] )
| 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : List[str] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
A_ : Union[str, Any] = [144, 192, 240]
A_ : int = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
A_ : List[str] = [96, 120, 144]
A_ : Any = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
A_ : Any = [64, 80, 96]
A_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
A_ : Any = 0.05
A_ : List[Any] = 2.0
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : int = 512
A_ : Optional[int] = 16
A_ : List[Any] = 21
A_ : List[str] = """pascal-voc-id2label.json"""
else:
A_ : str = 1000
A_ : Any = """imagenet-1k-id2label.json"""
A_ : Any = """huggingface/label-files"""
A_ : List[str] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""") , """r"""))
A_ : str = {int(lowerCamelCase): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int=False):
for i in range(1 , 6):
if F'layer_{i}.' in name:
A_ : Tuple = name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.')
if "conv_1." in name:
A_ : Union[str, Any] = name.replace("""conv_1.""" , """conv_stem.""")
if ".block." in name:
A_ : Optional[Any] = name.replace(""".block.""" , """.""")
if "exp_1x1" in name:
A_ : Union[str, Any] = name.replace("""exp_1x1""" , """expand_1x1""")
if "red_1x1" in name:
A_ : int = name.replace("""red_1x1""" , """reduce_1x1""")
if ".local_rep.conv_3x3." in name:
A_ : List[str] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""")
if ".local_rep.conv_1x1." in name:
A_ : Optional[int] = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""")
if ".norm." in name:
A_ : Tuple = name.replace(""".norm.""" , """.normalization.""")
if ".conv." in name:
A_ : List[Any] = name.replace(""".conv.""" , """.convolution.""")
if ".conv_proj." in name:
A_ : str = name.replace(""".conv_proj.""" , """.conv_projection.""")
for i in range(0 , 2):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Tuple = name.replace(F'.{i}.{j}.' , F'.{i}.layer.{j}.')
for i in range(2 , 6):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Dict = name.replace(F'.{i}.{j}.' , F'.{i}.')
if "expand_1x1" in name:
A_ : Union[str, Any] = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""")
if "conv_3x3" in name:
A_ : str = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""")
if "reduce_1x1" in name:
A_ : Union[str, Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""")
for i in range(2 , 5):
if F'.global_rep.{i}.weight' in name:
A_ : List[Any] = name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight""")
if F'.global_rep.{i}.bias' in name:
A_ : Optional[int] = name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias""")
if ".global_rep." in name:
A_ : Optional[Any] = name.replace(""".global_rep.""" , """.transformer.""")
if ".pre_norm_mha.0." in name:
A_ : int = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""")
if ".pre_norm_mha.1.out_proj." in name:
A_ : Dict = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""")
if ".pre_norm_ffn.0." in name:
A_ : Dict = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""")
if ".pre_norm_ffn.1." in name:
A_ : Any = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""")
if ".pre_norm_ffn.4." in name:
A_ : Union[str, Any] = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""")
if ".transformer." in name:
A_ : Any = name.replace(""".transformer.""" , """.transformer.layer.""")
if ".aspp_layer." in name:
A_ : int = name.replace(""".aspp_layer.""" , """.""")
if ".aspp_pool." in name:
A_ : Tuple = name.replace(""".aspp_pool.""" , """.""")
if "seg_head." in name:
A_ : Optional[int] = name.replace("""seg_head.""" , """segmentation_head.""")
if "segmentation_head.classifier.classifier." in name:
A_ : List[str] = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""")
if "classifier.fc." in name:
A_ : str = name.replace("""classifier.fc.""" , """classifier.""")
elif (not base_model) and ("segmentation_head." not in name):
A_ : str = """mobilevit.""" + name
return name
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False):
if base_model:
A_ : Dict = """"""
else:
A_ : Any = """mobilevit."""
for key in orig_state_dict.copy().keys():
A_ : List[Any] = orig_state_dict.pop(lowerCamelCase)
if key[:8] == "encoder.":
A_ : int = key[8:]
if "qkv" in key:
A_ : Any = key.split(""".""")
A_ : str = int(key_split[0][6:]) - 1
A_ : int = int(key_split[3])
A_ : Optional[Any] = model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}')
A_ : Tuple = layer.transformer.layer[transformer_num].attention.attention.all_head_size
A_ : Optional[Any] = (
F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
)
if "weight" in key:
A_ : Dict = val[:dim, :]
A_ : Optional[int] = val[dim : dim * 2, :]
A_ : List[Any] = val[-dim:, :]
else:
A_ : Optional[Any] = val[:dim]
A_ : List[Any] = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : List[str] = val
return orig_state_dict
def lowerCamelCase ( ):
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase).raw)
return im
@torch.no_grad()
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : int=False):
A_ : Optional[Any] = get_mobilevit_config(lowerCamelCase)
# load original state_dict
A_ : List[Any] = torch.load(lowerCamelCase , map_location="""cpu""")
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : List[str] = MobileViTForSemanticSegmentation(lowerCamelCase).eval()
else:
A_ : str = MobileViTForImageClassification(lowerCamelCase).eval()
A_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase)
model.load_state_dict(lowerCamelCase)
# Check outputs on an image, prepared by MobileViTImageProcessor
A_ : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32)
A_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
A_ : List[Any] = model(**lowerCamelCase)
A_ : Dict = outputs.logits
if mobilevit_name.startswith("""deeplabv3_"""):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
A_ : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xs":
A_ : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
A_ : Tuple = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4)
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
A_ : Tuple = torch.tensor([-0.9866, 0.2392, -1.1241])
elif mobilevit_name == "mobilevit_xs":
A_ : Any = torch.tensor([-2.4761, -0.9399, -1.9587])
elif mobilevit_name == "mobilevit_xxs":
A_ : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4)
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowerCamelCase)
print(F'Saving image processor to {pytorch_dump_folder_path}')
image_processor.save_pretrained(lowerCamelCase)
if push_to_hub:
A_ : str = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""")
A_ : Union[str, Any] = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowerCamelCase , organization="""apple""")
model.push_to_hub(lowerCamelCase , organization="""apple""")
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
__magic_name__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 27 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
__magic_name__ = random.Random()
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : List[str]=1.0 , lowerCamelCase : Optional[int]=None , lowerCamelCase : List[str]=None):
if rng is None:
A_ : Dict = global_rng
A_ : List[str] = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : str=7 ,_a : int=400 ,_a : List[str]=2000 ,_a : Any=2048 ,_a : Any=128 ,_a : int=1 ,_a : Optional[int]=512 ,_a : Union[str, Any]=30 ,_a : Any=44100 ,):
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : str = batch_size
A_ : str = min_seq_length
A_ : Union[str, Any] = max_seq_length
A_ : List[Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
A_ : Union[str, Any] = spectrogram_length
A_ : Optional[int] = feature_size
A_ : Tuple = num_audio_channels
A_ : List[str] = hop_length
A_ : Union[str, Any] = chunk_length
A_ : Tuple = sampling_rate
def _a ( self : Dict ):
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def _a ( self : int ,_a : List[Any]=False ,_a : List[str]=False ):
'''simple docstring'''
def _flatten(_a : int ):
return list(itertools.chain(*_a ) )
if equal_length:
A_ : Union[str, Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
A_ : str = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
A_ : Tuple = [np.asarray(_a ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = TvltFeatureExtractor
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = TvltFeatureExtractionTester(self )
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_a ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(_a ,"""feature_size""" ) )
self.assertTrue(hasattr(_a ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(_a ,"""hop_length""" ) )
self.assertTrue(hasattr(_a ,"""chunk_length""" ) )
self.assertTrue(hasattr(_a ,"""sampling_rate""" ) )
def _a ( self : str ):
'''simple docstring'''
A_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A_ : List[Any] = feat_extract_first.save_pretrained(_a )[0]
check_json_file_has_correct_format(_a )
A_ : Any = self.feature_extraction_class.from_pretrained(_a )
A_ : Any = feat_extract_first.to_dict()
A_ : List[str] = feat_extract_second.to_dict()
A_ : Union[str, Any] = dict_first.pop("""mel_filters""" )
A_ : str = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(_a ,_a ) )
self.assertEqual(_a ,_a )
def _a ( self : int ):
'''simple docstring'''
A_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
A_ : Optional[Any] = os.path.join(_a ,"""feat_extract.json""" )
feat_extract_first.to_json_file(_a )
A_ : Optional[Any] = self.feature_extraction_class.from_json_file(_a )
A_ : Optional[int] = feat_extract_first.to_dict()
A_ : int = feat_extract_second.to_dict()
A_ : Optional[Any] = dict_first.pop("""mel_filters""" )
A_ : List[str] = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(_a ,_a ) )
self.assertEqual(_a ,_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Any = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
A_ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
A_ : Optional[Any] = [np.asarray(_a ) for speech_input in speech_inputs]
# Test not batched input
A_ : Union[str, Any] = feature_extractor(np_speech_inputs[0] ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
A_ : Union[str, Any] = feature_extractor(_a ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
A_ : str = feature_extractor(
_a ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=_a ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
A_ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
A_ : str = np.asarray(_a )
A_ : Tuple = feature_extractor(_a ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def _a ( self : Tuple ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : Union[str, Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
A_ : Any = ds.sort("""id""" ).select(range(_a ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self._load_datasamples(1 )
A_ : Dict = TvltFeatureExtractor()
A_ : int = feature_extractor(_a ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
A_ : Any = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,_a ,atol=1e-4 ) )
| 27 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__magic_name__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : Optional[Any] ,_a : bool = True ,_a : Dict[str, int] = None ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : bool = True ,_a : Dict[str, int] = None ,_a : bool = True ,_a : Union[int, float] = 1 / 255 ,_a : bool = True ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = True ,**_a : Dict ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Tuple = size if size is not None else {"""shortest_edge""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
A_ : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a ,param_name="""crop_size""" )
A_ : Any = do_resize
A_ : List[str] = size
A_ : Union[str, Any] = resample
A_ : Dict = do_center_crop
A_ : List[str] = crop_size
A_ : Any = do_rescale
A_ : Union[str, Any] = rescale_factor
A_ : Any = do_normalize
A_ : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A_ : List[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
A_ : Tuple = do_convert_rgb
def _a ( self : Optional[int] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[Any] ,):
'''simple docstring'''
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A_ : Tuple = get_resize_output_image_size(_a ,size=size["""shortest_edge"""] ,default_to_square=_a )
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def _a ( self : List[Any] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[int] ,):
'''simple docstring'''
A_ : Optional[int] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_a ,size=(size["""height"""], size["""width"""]) ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[int, float] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Any ,):
'''simple docstring'''
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[float, List[float]] ,_a : Union[float, List[float]] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[str] ,):
'''simple docstring'''
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def _a ( self : Optional[Any] ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : int = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[ChannelDimension] = ChannelDimension.FIRST ,**_a : int ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : Tuple = size if size is not None else self.size
A_ : Optional[int] = get_size_dict(_a ,param_name="""size""" ,default_to_square=_a )
A_ : List[str] = resample if resample is not None else self.resample
A_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : Any = crop_size if crop_size is not None else self.crop_size
A_ : int = get_size_dict(_a ,param_name="""crop_size""" ,default_to_square=_a )
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : Any = do_normalize if do_normalize is not None else self.do_normalize
A_ : int = image_mean if image_mean is not None else self.image_mean
A_ : int = image_std if image_std is not None else self.image_std
A_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A_ : int = make_list_of_images(_a )
if not valid_images(_a ):
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.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A_ : Optional[int] = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
A_ : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
A_ : int = [self.resize(image=_a ,size=_a ,resample=_a ) for image in images]
if do_center_crop:
A_ : Tuple = [self.center_crop(image=_a ,size=_a ) for image in images]
if do_rescale:
A_ : List[str] = [self.rescale(image=_a ,scale=_a ) for image in images]
if do_normalize:
A_ : Any = [self.normalize(image=_a ,mean=_a ,std=_a ) for image in images]
A_ : List[str] = [to_channel_dimension_format(_a ,_a ) for image in images]
A_ : List[str] = {"""pixel_values""": images}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 | 1 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__magic_name__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_a : Dict ):
'''simple docstring'''
super().__init__()
A_ : List[str] = torchvision.models.resnetaaa(pretrained=_a )
A_ : int = list(model.children() )[:-2]
A_ : int = nn.Sequential(*_a )
A_ : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def _a ( self : str ,_a : Optional[int] ):
'''simple docstring'''
A_ : Tuple = self.pool(self.model(_a ) )
A_ : Any = torch.flatten(_a ,start_dim=2 )
A_ : str = out.transpose(1 ,2 ).contiguous()
return out # BxNx2048
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Dict ,_a : Dict ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Dict = [json.loads(_a ) for l in open(_a )]
A_ : Optional[int] = os.path.dirname(_a )
A_ : Optional[Any] = tokenizer
A_ : Optional[Any] = labels
A_ : List[Any] = len(_a )
A_ : str = max_seq_length
A_ : str = transforms
def __len__( self : str ):
'''simple docstring'''
return len(self.data )
def __getitem__( self : Tuple ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,add_special_tokens=_a ) )
A_ , A_ , A_ : Dict = sentence[0], sentence[1:-1], sentence[-1]
A_ : Optional[int] = sentence[: self.max_seq_length]
A_ : Any = torch.zeros(self.n_classes )
A_ : Tuple = 1
A_ : Optional[Any] = Image.open(os.path.join(self.data_dir ,self.data[index]["""img"""] ) ).convert("""RGB""" )
A_ : Union[str, Any] = self.transforms(_a )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def lowerCamelCase ( lowerCamelCase : str):
A_ : List[Any] = [len(row["""sentence"""]) for row in batch]
A_ , A_ : Dict = len(lowerCamelCase), max(lowerCamelCase)
A_ : Optional[int] = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
A_ : Tuple = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase , lowerCamelCase)):
A_ : str = input_row["""sentence"""]
A_ : Tuple = 1
A_ : int = torch.stack([row["""image"""] for row in batch])
A_ : str = torch.stack([row["""label"""] for row in batch])
A_ : List[Any] = torch.stack([row["""image_start_token"""] for row in batch])
A_ : Tuple = torch.stack([row["""image_end_token"""] for row in batch])
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase ( ):
return transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),
])
| 27 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,*_a : Optional[Any] ,**_a : Optional[int] ):
'''simple docstring'''
warnings.warn(
"""The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use OwlViTImageProcessor instead.""" ,_a ,)
super().__init__(*_a ,**_a )
| 27 | 1 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """char"""
a_ = """bpe"""
a_ = """wp"""
__magic_name__ = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """char_tokenizer"""]
a_ = """ViTImageProcessor"""
a_ = """MgpstrTokenizer"""
def __init__( self : Dict ,_a : Optional[int]=None ,_a : Dict=None ,**_a : Optional[int] ):
'''simple docstring'''
A_ : str = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" ,_a ,)
A_ : Optional[int] = kwargs.pop("""feature_extractor""" )
A_ : Tuple = 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`.""" )
A_ : Union[str, Any] = tokenizer
A_ : List[Any] = AutoTokenizer.from_pretrained("""gpt2""" )
A_ : str = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(_a ,_a )
def __call__( self : Optional[int] ,_a : str=None ,_a : int=None ,_a : int=None ,**_a : Any ):
'''simple docstring'''
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:
A_ : int = self.image_processor(_a ,return_tensors=_a ,**_a )
if text is not None:
A_ : Tuple = self.char_tokenizer(_a ,return_tensors=_a ,**_a )
if text is None:
return inputs
elif images is None:
return encodings
else:
A_ : int = encodings["""input_ids"""]
return inputs
def _a ( self : Optional[Any] ,_a : Any ):
'''simple docstring'''
A_ , A_ , A_ : List[str] = sequences
A_ : List[str] = char_preds.size(0 )
A_ , A_ : Dict = self._decode_helper(_a ,"""char""" )
A_ , A_ : List[str] = self._decode_helper(_a ,"""bpe""" )
A_ , A_ : Union[str, Any] = self._decode_helper(_a ,"""wp""" )
A_ : str = []
A_ : Any = []
for i in range(_a ):
A_ : List[Any] = [char_scores[i], bpe_scores[i], wp_scores[i]]
A_ : List[Any] = [char_strs[i], bpe_strs[i], wp_strs[i]]
A_ : Any = scores.index(max(_a ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
A_ : Optional[int] = {}
A_ : Dict = final_strs
A_ : Dict = final_scores
A_ : List[Any] = char_strs
A_ : Optional[Any] = bpe_strs
A_ : Optional[int] = wp_strs
return out
def _a ( self : List[str] ,_a : Dict ,_a : str ):
'''simple docstring'''
if format == DecodeType.CHARACTER:
A_ : Dict = self.char_decode
A_ : int = 1
A_ : List[str] = """[s]"""
elif format == DecodeType.BPE:
A_ : Optional[int] = self.bpe_decode
A_ : List[Any] = 2
A_ : Tuple = """#"""
elif format == DecodeType.WORDPIECE:
A_ : int = self.wp_decode
A_ : Optional[Any] = 102
A_ : List[str] = """[SEP]"""
else:
raise ValueError(f'Format {format} is not supported.' )
A_ , A_ : Any = [], []
A_ : Tuple = pred_logits.size(0 )
A_ : Union[str, Any] = pred_logits.size(1 )
A_ , A_ : List[str] = pred_logits.topk(1 ,dim=-1 ,largest=_a ,sorted=_a )
A_ : Optional[int] = preds_index.view(-1 ,_a )[:, 1:]
A_ : Dict = decoder(_a )
A_ , A_ : Tuple = torch.nn.functional.softmax(_a ,dim=2 ).max(dim=2 )
A_ : str = preds_max_prob[:, 1:]
for index in range(_a ):
A_ : List[str] = preds_str[index].find(_a )
A_ : int = preds_str[index][:pred_eos]
A_ : Union[str, Any] = preds_index[index].cpu().tolist()
A_ : Union[str, Any] = pred_index.index(_a ) if eos_token in pred_index else -1
A_ : Any = preds_max_prob[index][: pred_eos_index + 1]
A_ : List[str] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(_a )
conf_scores.append(_a )
return dec_strs, conf_scores
def _a ( self : Optional[Any] ,_a : Any ):
'''simple docstring'''
A_ : str = [seq.replace(""" """ ,"""""" ) for seq in self.char_tokenizer.batch_decode(_a )]
return decode_strs
def _a ( self : Union[str, Any] ,_a : Optional[Any] ):
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(_a )
def _a ( self : int ,_a : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[Any] = [seq.replace(""" """ ,"""""" ) for seq in self.wp_tokenizer.batch_decode(_a )]
return decode_strs
| 27 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : complex , lowerCamelCase : str = "x" , lowerCamelCase : float = 10**-10 , lowerCamelCase : int = 1 , ):
A_ : int = symbols(lowerCamelCase)
A_ : List[Any] = lambdify(lowerCamelCase , lowerCamelCase)
A_ : List[str] = lambdify(lowerCamelCase , diff(lowerCamelCase , lowerCamelCase))
A_ : str = starting_point
while True:
if diff_function(lowerCamelCase) != 0:
A_ : int = prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
lowerCamelCase)
else:
raise ZeroDivisionError("""Could not find root""") from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess) < precision:
return next_guess
A_ : Union[str, Any] = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}""",
)
# Find root of cos(x)
print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Dict , lowerCamelCase : Union[str, Any]):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
A_ : List[Any] = (boundary[1] - boundary[0]) / steps
A_ : Optional[Any] = boundary[0]
A_ : List[Any] = boundary[1]
A_ : Optional[int] = make_points(lowerCamelCase , lowerCamelCase , lowerCamelCase)
A_ : Union[str, Any] = 0.0
y += (h / 2.0) * f(lowerCamelCase)
for i in x_i:
# print(i)
y += h * f(lowerCamelCase)
y += (h / 2.0) * f(lowerCamelCase)
return y
def lowerCamelCase ( lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict):
A_ : List[Any] = a + h
while x < (b - h):
yield x
A_ : str = x + h
def lowerCamelCase ( lowerCamelCase : Union[str, Any]): # enter your function here
A_ : Tuple = (x - 0) * (x - 0)
return y
def lowerCamelCase ( ):
A_ : int = 0.0 # Lower bound of integration
A_ : Optional[Any] = 1.0 # Upper bound of integration
A_ : Optional[Any] = 10.0 # define number of steps or resolution
A_ : Optional[Any] = [a, b] # define boundary of integration
A_ : Tuple = method_a(lowerCamelCase , lowerCamelCase)
print(F'y = {y}')
if __name__ == "__main__":
main()
| 27 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__magic_name__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_a : Dict ):
'''simple docstring'''
super().__init__()
A_ : List[str] = torchvision.models.resnetaaa(pretrained=_a )
A_ : int = list(model.children() )[:-2]
A_ : int = nn.Sequential(*_a )
A_ : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def _a ( self : str ,_a : Optional[int] ):
'''simple docstring'''
A_ : Tuple = self.pool(self.model(_a ) )
A_ : Any = torch.flatten(_a ,start_dim=2 )
A_ : str = out.transpose(1 ,2 ).contiguous()
return out # BxNx2048
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Dict ,_a : Dict ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Dict = [json.loads(_a ) for l in open(_a )]
A_ : Optional[int] = os.path.dirname(_a )
A_ : Optional[Any] = tokenizer
A_ : Optional[Any] = labels
A_ : List[Any] = len(_a )
A_ : str = max_seq_length
A_ : str = transforms
def __len__( self : str ):
'''simple docstring'''
return len(self.data )
def __getitem__( self : Tuple ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,add_special_tokens=_a ) )
A_ , A_ , A_ : Dict = sentence[0], sentence[1:-1], sentence[-1]
A_ : Optional[int] = sentence[: self.max_seq_length]
A_ : Any = torch.zeros(self.n_classes )
A_ : Tuple = 1
A_ : Optional[Any] = Image.open(os.path.join(self.data_dir ,self.data[index]["""img"""] ) ).convert("""RGB""" )
A_ : Union[str, Any] = self.transforms(_a )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def lowerCamelCase ( lowerCamelCase : str):
A_ : List[Any] = [len(row["""sentence"""]) for row in batch]
A_ , A_ : Dict = len(lowerCamelCase), max(lowerCamelCase)
A_ : Optional[int] = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
A_ : Tuple = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase , lowerCamelCase)):
A_ : str = input_row["""sentence"""]
A_ : Tuple = 1
A_ : int = torch.stack([row["""image"""] for row in batch])
A_ : str = torch.stack([row["""label"""] for row in batch])
A_ : List[Any] = torch.stack([row["""image_start_token"""] for row in batch])
A_ : Tuple = torch.stack([row["""image_end_token"""] for row in batch])
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase ( ):
return transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),
])
| 27 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
__magic_name__ = logging.get_logger(__name__) # pylint: disable=invalid-name
__magic_name__ = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n >>> pipe_prior.to("cuda")\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n >>> pipe.to("cuda")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save("cat.png")\n ```\n'
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : int=8):
A_ : Tuple = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
A_ : List[Any] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : str ,_a : UNetaDConditionModel ,_a : DDPMScheduler ,_a : VQModel ,):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_a ,scheduler=_a ,movq=_a ,)
A_ : Optional[int] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _a ( self : Dict ,_a : Any ,_a : List[Any] ,_a : List[Any] ,_a : Union[str, Any] ,_a : List[str] ,_a : Optional[int] ):
'''simple docstring'''
if latents is None:
A_ : Dict = randn_tensor(_a ,generator=_a ,device=_a ,dtype=_a )
else:
if latents.shape != shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
A_ : Dict = latents.to(_a )
A_ : int = latents * scheduler.init_noise_sigma
return latents
def _a ( self : str ,_a : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
A_ : Tuple = torch.device(f'cuda:{gpu_id}' )
A_ : List[Any] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_a ,_a )
def _a ( self : int ,_a : List[Any]=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" ,"""0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
A_ : List[Any] = torch.device(f'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to("""cpu""" ,silence_dtype_warnings=_a )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
A_ : Dict = None
for cpu_offloaded_model in [self.unet, self.movq]:
A_ , A_ : Union[str, Any] = cpu_offload_with_hook(_a ,_a ,prev_module_hook=_a )
# We'll offload the last model manually.
A_ : List[str] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _a ( self : Tuple ):
'''simple docstring'''
if not hasattr(self.unet ,"""_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_a ,"""_hf_hook""" )
and hasattr(module._hf_hook ,"""execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_a )
def __call__( self : str ,_a : Union[torch.FloatTensor, List[torch.FloatTensor]] ,_a : Union[torch.FloatTensor, List[torch.FloatTensor]] ,_a : int = 512 ,_a : int = 512 ,_a : int = 100 ,_a : float = 4.0 ,_a : int = 1 ,_a : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,_a : Optional[torch.FloatTensor] = None ,_a : Optional[str] = "pil" ,_a : bool = True ,):
'''simple docstring'''
A_ : Union[str, Any] = self._execution_device
A_ : str = guidance_scale > 1.0
if isinstance(_a ,_a ):
A_ : str = torch.cat(_a ,dim=0 )
A_ : Optional[Any] = image_embeds.shape[0] * num_images_per_prompt
if isinstance(_a ,_a ):
A_ : Dict = torch.cat(_a ,dim=0 )
if do_classifier_free_guidance:
A_ : str = image_embeds.repeat_interleave(_a ,dim=0 )
A_ : Union[str, Any] = negative_image_embeds.repeat_interleave(_a ,dim=0 )
A_ : Tuple = torch.cat([negative_image_embeds, image_embeds] ,dim=0 ).to(dtype=self.unet.dtype ,device=_a )
self.scheduler.set_timesteps(_a ,device=_a )
A_ : str = self.scheduler.timesteps
A_ : Optional[Any] = self.unet.config.in_channels
A_ , A_ : Any = downscale_height_and_width(_a ,_a ,self.movq_scale_factor )
# create initial latent
A_ : int = self.prepare_latents(
(batch_size, num_channels_latents, height, width) ,image_embeds.dtype ,_a ,_a ,_a ,self.scheduler ,)
for i, t in enumerate(self.progress_bar(_a ) ):
# expand the latents if we are doing classifier free guidance
A_ : List[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A_ : List[Any] = {"""image_embeds""": image_embeds}
A_ : int = self.unet(
sample=_a ,timestep=_a ,encoder_hidden_states=_a ,added_cond_kwargs=_a ,return_dict=_a ,)[0]
if do_classifier_free_guidance:
A_ , A_ : Any = noise_pred.split(latents.shape[1] ,dim=1 )
A_ , A_ : List[Any] = noise_pred.chunk(2 )
A_ , A_ : int = variance_pred.chunk(2 )
A_ : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
A_ : Any = torch.cat([noise_pred, variance_pred_text] ,dim=1 )
if not (
hasattr(self.scheduler.config ,"""variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
A_ , A_ : Tuple = noise_pred.split(latents.shape[1] ,dim=1 )
# compute the previous noisy sample x_t -> x_t-1
A_ : Any = self.scheduler.step(
_a ,_a ,_a ,generator=_a ,)[0]
# post-processing
A_ : Tuple = self.movq.decode(_a ,force_not_quantize=_a )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
A_ : int = image * 0.5 + 0.5
A_ : int = image.clamp(0 ,1 )
A_ : Any = image.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
if output_type == "pil":
A_ : List[str] = self.numpy_to_pil(_a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_a )
| 27 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( lowerCamelCase : int):
if num <= 0:
A_ : List[Any] = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase)
A_ : str = [True] * (num + 1)
A_ : Tuple = []
A_ : str = 2
A_ : Any = int(math.sqrt(lowerCamelCase))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCamelCase)
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCamelCase):
if sieve[i] is True:
A_ : Union[str, Any] = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(lowerCamelCase)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__magic_name__ = 0
__magic_name__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__magic_name__ = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__magic_name__ = tuple[int, int]
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : int ,_a : int ,_a : int ,_a : int ,_a : int ,_a : Node | None ,):
'''simple docstring'''
A_ : Union[str, Any] = pos_x
A_ : Union[str, Any] = pos_y
A_ : Optional[int] = (pos_y, pos_x)
A_ : Optional[Any] = goal_x
A_ : Union[str, Any] = goal_y
A_ : Any = g_cost
A_ : Optional[Any] = parent
A_ : Tuple = self.calculate_heuristic()
A_ : Union[str, Any] = self.g_cost + self.h_cost
def _a ( self : Any ):
'''simple docstring'''
A_ : Dict = self.pos_x - self.goal_x
A_ : Optional[Any] = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(_a ) + abs(_a )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : List[Any] ,_a : Node ):
'''simple docstring'''
return self.f_cost < other.f_cost
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,_a : TPosition ,_a : TPosition ):
'''simple docstring'''
A_ : str = Node(start[1] ,start[0] ,goal[1] ,goal[0] ,0 ,_a )
A_ : List[str] = Node(goal[1] ,goal[0] ,goal[1] ,goal[0] ,99999 ,_a )
A_ : List[str] = [self.start]
A_ : list[Node] = []
A_ : int = False
def _a ( self : Dict ):
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
A_ : Dict = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(_a )
self.closed_nodes.append(_a )
A_ : Union[str, Any] = self.get_successors(_a )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(_a )
else:
# retrieve the best current path
A_ : List[Any] = self.open_nodes.pop(self.open_nodes.index(_a ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(_a )
else:
self.open_nodes.append(_a )
return [self.start.pos]
def _a ( self : List[str] ,_a : Node ):
'''simple docstring'''
A_ : Optional[Any] = []
for action in delta:
A_ : int = parent.pos_x + action[1]
A_ : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
_a ,_a ,self.target.pos_y ,self.target.pos_x ,parent.g_cost + 1 ,_a ,) )
return successors
def _a ( self : List[Any] ,_a : Node | None ):
'''simple docstring'''
A_ : Dict = node
A_ : Any = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
A_ : Any = current_node.parent
path.reverse()
return path
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,_a : TPosition ,_a : TPosition ):
'''simple docstring'''
A_ : Optional[Any] = AStar(_a ,_a )
A_ : Dict = AStar(_a ,_a )
A_ : Dict = False
def _a ( self : List[Any] ):
'''simple docstring'''
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
A_ : List[Any] = self.fwd_astar.open_nodes.pop(0 )
A_ : Union[str, Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
_a ,_a )
self.fwd_astar.closed_nodes.append(_a )
self.bwd_astar.closed_nodes.append(_a )
A_ : Any = current_bwd_node
A_ : List[Any] = current_fwd_node
A_ : int = {
self.fwd_astar: self.fwd_astar.get_successors(_a ),
self.bwd_astar: self.bwd_astar.get_successors(_a ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(_a )
else:
# retrieve the best current path
A_ : str = astar.open_nodes.pop(
astar.open_nodes.index(_a ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(_a )
else:
astar.open_nodes.append(_a )
return [self.fwd_astar.start.pos]
def _a ( self : str ,_a : Node ,_a : Node ):
'''simple docstring'''
A_ : List[Any] = self.fwd_astar.retrace_path(_a )
A_ : Dict = self.bwd_astar.retrace_path(_a )
bwd_path.pop()
bwd_path.reverse()
A_ : Optional[Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__magic_name__ = (0, 0)
__magic_name__ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__magic_name__ = time.time()
__magic_name__ = AStar(init, goal)
__magic_name__ = a_star.search()
__magic_name__ = time.time() - start_time
print(f"""AStar execution time = {end_time:f} seconds""")
__magic_name__ = time.time()
__magic_name__ = BidirectionalAStar(init, goal)
__magic_name__ = time.time() - bd_start_time
print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")
| 27 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
__magic_name__ = trt.Logger(trt.Logger.WARNING)
__magic_name__ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
__magic_name__ = logging.getLogger(__name__)
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=384,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=128,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
__magic_name__ = parser.parse_args()
if args.tokenizer_name:
__magic_name__ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
__magic_name__ = args.per_device_eval_batch_size
__magic_name__ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
__magic_name__ = True
__magic_name__ = 'temp_engine/bert-fp32.engine'
if args.fpaa:
__magic_name__ = 'temp_engine/bert-fp16.engine'
if args.inta:
__magic_name__ = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
__magic_name__ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
__magic_name__ = [network.get_input(i) for i in range(network.num_inputs)]
__magic_name__ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
__magic_name__ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
__magic_name__ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
__magic_name__ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[str]):
A_ : str = np.asarray(inputs["""input_ids"""] , dtype=np.intaa)
A_ : int = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa)
A_ : Optional[int] = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa)
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCamelCase)
# start time
A_ : List[Any] = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCamelCase) for d_inp in d_inputs] + [int(lowerCamelCase), int(lowerCamelCase)] , stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Synchronize the stream and take time
stream.synchronize()
# end time
A_ : str = time.time()
A_ : Tuple = end_time - start_time
A_ : Any = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__magic_name__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__magic_name__ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
__magic_name__ = raw_datasets['validation'].column_names
__magic_name__ = 'question' if 'question' in column_names else column_names[0]
__magic_name__ = 'context' if 'context' in column_names else column_names[1]
__magic_name__ = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
__magic_name__ = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
)
__magic_name__ = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCamelCase ( lowerCamelCase : Dict):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
A_ : List[Any] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
A_ : Optional[int] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=lowerCamelCase , stride=args.doc_stride , return_overflowing_tokens=lowerCamelCase , return_offsets_mapping=lowerCamelCase , padding="""max_length""" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
A_ : List[str] = tokenized_examples.pop("""overflow_to_sample_mapping""")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
A_ : Union[str, Any] = []
for i in range(len(tokenized_examples["""input_ids"""])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
A_ : Any = tokenized_examples.sequence_ids(lowerCamelCase)
A_ : Tuple = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
A_ : Union[str, Any] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["""id"""][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
A_ : Dict = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
]
return tokenized_examples
__magic_name__ = raw_datasets['validation']
# Validation Feature Creation
__magic_name__ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
__magic_name__ = default_data_collator
__magic_name__ = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__magic_name__ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any]="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
A_ : Tuple = postprocess_qa_predictions(
examples=lowerCamelCase , features=lowerCamelCase , predictions=lowerCamelCase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCamelCase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
A_ : Dict = [
{"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
]
else:
A_ : Union[str, Any] = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]
A_ : Any = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCamelCase , label_ids=lowerCamelCase)
__magic_name__ = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize
# Allocate device memory for inputs and outputs.
__magic_name__ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
__magic_name__ = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(f""" Num examples = {len(eval_dataset)}""")
logger.info(f""" Batch size = {args.per_device_eval_batch_size}""")
__magic_name__ = 0.0
__magic_name__ = 0
__magic_name__ = timeit.default_timer()
__magic_name__ = None
for step, batch in enumerate(eval_dataloader):
__magic_name__ , __magic_name__ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
__magic_name__ , __magic_name__ = outputs
__magic_name__ = torch.tensor(start_logits)
__magic_name__ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
__magic_name__ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
__magic_name__ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
__magic_name__ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
__magic_name__ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
__magic_name__ = nested_truncate(all_preds, len(eval_dataset))
__magic_name__ = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_000))
logger.info('Total Number of Inference = %d', niter)
__magic_name__ = post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = None
a_ = None
def lowerCamelCase ( lowerCamelCase : TreeNode | None):
# Validation
def is_valid_tree(lowerCamelCase : TreeNode | None) -> bool:
if node is None:
return True
if not isinstance(lowerCamelCase , lowerCamelCase):
return False
try:
float(node.data)
except (TypeError, ValueError):
return False
return is_valid_tree(node.left) and is_valid_tree(node.right)
if not is_valid_tree(lowerCamelCase):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""")
def is_binary_search_tree_recursive_check(
lowerCamelCase : TreeNode | None , lowerCamelCase : float , lowerCamelCase : float) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , lowerCamelCase , node.data)
and is_binary_search_tree_recursive_check(
node.right , node.data , lowerCamelCase)
)
return is_binary_search_tree_recursive_check(lowerCamelCase , -float("""inf""") , float("""inf"""))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['ConvNextFeatureExtractor']
__magic_name__ = ['ConvNextImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvNextForImageClassification',
'ConvNextModel',
'ConvNextPreTrainedModel',
'ConvNextBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TFConvNextForImageClassification',
'TFConvNextModel',
'TFConvNextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 27 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self : Optional[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : List[str] = StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
A_ : int = sd_pipe.to(_a )
sd_pipe.set_progress_bar_config(disable=_a )
sd_pipe.set_scheduler("""sample_euler""" )
A_ : str = """A painting of a squirrel eating a burger"""
A_ : Any = torch.manual_seed(0 )
A_ : Dict = sd_pipe([prompt] ,generator=_a ,guidance_scale=9.0 ,num_inference_steps=20 ,output_type="""np""" )
A_ : str = output.images
A_ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : int = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Any ):
'''simple docstring'''
A_ : str = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
A_ : Optional[int] = sd_pipe.to(_a )
sd_pipe.set_progress_bar_config(disable=_a )
sd_pipe.set_scheduler("""sample_euler""" )
A_ : List[Any] = """A painting of a squirrel eating a burger"""
A_ : Optional[Any] = torch.manual_seed(0 )
A_ : str = sd_pipe([prompt] ,generator=_a ,guidance_scale=9.0 ,num_inference_steps=20 ,output_type="""np""" )
A_ : int = output.images
A_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : Optional[Any] = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Any = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
A_ : Any = sd_pipe.to(_a )
sd_pipe.set_progress_bar_config(disable=_a )
sd_pipe.set_scheduler("""sample_dpmpp_2m""" )
A_ : Tuple = """A painting of a squirrel eating a burger"""
A_ : Union[str, Any] = torch.manual_seed(0 )
A_ : Optional[int] = sd_pipe(
[prompt] ,generator=_a ,guidance_scale=7.5 ,num_inference_steps=15 ,output_type="""np""" ,use_karras_sigmas=_a ,)
A_ : Tuple = output.images
A_ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : Tuple = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 27 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_text_model"""
def __init__( self : Union[str, Any] ,_a : Any=49408 ,_a : Any=512 ,_a : Tuple=2048 ,_a : Dict=12 ,_a : Optional[int]=8 ,_a : Tuple=16 ,_a : Tuple="quick_gelu" ,_a : Optional[Any]=1e-5 ,_a : List[Any]=0.0 ,_a : Optional[int]=0.02 ,_a : Dict=1.0 ,_a : Dict=0 ,_a : Any=49406 ,_a : Tuple=49407 ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Optional[int] = intermediate_size
A_ : Optional[int] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : int = max_position_embeddings
A_ : str = hidden_act
A_ : Union[str, Any] = layer_norm_eps
A_ : Tuple = attention_dropout
A_ : Union[str, Any] = initializer_range
A_ : List[Any] = initializer_factor
@classmethod
def _a ( cls : List[str] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : int = cls.get_config_dict(_a ,**_a )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : Union[str, Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_vision_model"""
def __init__( self : List[Any] ,_a : Optional[Any]=768 ,_a : Tuple=3072 ,_a : Dict=12 ,_a : int=12 ,_a : Dict=3 ,_a : Tuple=768 ,_a : int=32 ,_a : int="quick_gelu" ,_a : List[Any]=1e-5 ,_a : Tuple=0.0 ,_a : List[Any]=0.02 ,_a : str=1.0 ,**_a : int ,):
'''simple docstring'''
super().__init__(**_a )
A_ : List[str] = hidden_size
A_ : Union[str, Any] = intermediate_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : int = num_channels
A_ : str = image_size
A_ : List[Any] = patch_size
A_ : int = hidden_act
A_ : List[Any] = layer_norm_eps
A_ : List[str] = attention_dropout
A_ : str = initializer_range
A_ : str = initializer_factor
@classmethod
def _a ( cls : List[Any] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : Optional[int] = cls.get_config_dict(_a ,**_a )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : List[str] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit"""
a_ = True
def __init__( self : Union[str, Any] ,_a : List[str]=None ,_a : List[str]=None ,_a : Dict=512 ,_a : List[Any]=2.6592 ,_a : Optional[Any]=True ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(**_a )
if text_config is None:
A_ : List[Any] = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
A_ : Tuple = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
A_ : Dict = OwlViTTextConfig(**_a )
A_ : Dict = OwlViTVisionConfig(**_a )
A_ : Any = projection_dim
A_ : Optional[int] = logit_scale_init_value
A_ : Optional[int] = return_dict
A_ : Dict = 1.0
@classmethod
def _a ( cls : Union[str, Any] ,_a : Union[str, os.PathLike] ,**_a : Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : List[Any] = cls.get_config_dict(_a ,**_a )
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
@classmethod
def _a ( cls : int ,_a : Dict ,_a : Dict ,**_a : List[str] ):
'''simple docstring'''
A_ : str = {}
A_ : int = text_config
A_ : Union[str, Any] = vision_config
return cls.from_dict(_a ,**_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = copy.deepcopy(self.__dict__ )
A_ : str = self.text_config.to_dict()
A_ : Optional[int] = self.vision_config.to_dict()
A_ : List[Any] = self.__class__.model_type
return output
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : int ):
'''simple docstring'''
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _a ( self : str ):
'''simple docstring'''
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
def _a ( self : int ,_a : "ProcessorMixin" ,_a : int = -1 ,_a : int = -1 ,_a : Optional["TensorType"] = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(
processor.tokenizer ,batch_size=_a ,seq_length=_a ,framework=_a )
A_ : Any = super().generate_dummy_inputs(
processor.image_processor ,batch_size=_a ,framework=_a )
return {**text_input_dict, **image_input_dict}
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 14
| 27 | 1 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__magic_name__ = '.'
if __name__ == "__main__":
__magic_name__ = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
__magic_name__ = []
__magic_name__ = []
with open(doctest_file_path) as fp:
for line in fp:
__magic_name__ = line.strip()
__magic_name__ = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
__magic_name__ = '\n'.join(non_existent_paths)
raise ValueError(f"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
if all_paths != sorted(all_paths):
raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')
| 27 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""input_features""", """is_longer"""]
def __init__( self : Dict ,_a : Optional[int]=64 ,_a : List[Any]=48000 ,_a : str=480 ,_a : Optional[Any]=10 ,_a : Optional[int]=1024 ,_a : Tuple=0.0 ,_a : str=False ,_a : float = 0 ,_a : float = 14000 ,_a : int = None ,_a : str = "fusion" ,_a : str = "repeatpad" ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(
feature_size=_a ,sampling_rate=_a ,padding_value=_a ,return_attention_mask=_a ,**_a ,)
A_ : Tuple = top_db
A_ : Tuple = truncation
A_ : Optional[Any] = padding
A_ : Optional[int] = fft_window_size
A_ : Dict = (fft_window_size >> 1) + 1
A_ : Any = hop_length
A_ : List[Any] = max_length_s
A_ : Tuple = max_length_s * sampling_rate
A_ : Tuple = sampling_rate
A_ : Optional[int] = frequency_min
A_ : Tuple = frequency_max
A_ : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm=_a ,mel_scale="""htk""" ,)
A_ : Dict = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def _a ( self : int ):
'''simple docstring'''
A_ : int = copy.deepcopy(self.__dict__ )
A_ : Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _a ( self : Dict ,_a : np.array ,_a : Optional[np.array] = None ):
'''simple docstring'''
A_ : List[str] = spectrogram(
_a ,window_function(self.fft_window_size ,"""hann""" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=_a ,log_mel="""dB""" ,)
return log_mel_spectrogram.T
def _a ( self : Optional[int] ,_a : Dict ,_a : Optional[Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Dict = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : int = [0]
# randomly choose index for each part
A_ : List[str] = np.random.choice(ranges[0] )
A_ : int = np.random.choice(ranges[1] )
A_ : Optional[int] = np.random.choice(ranges[2] )
A_ : Tuple = mel[idx_front : idx_front + chunk_frames, :]
A_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
A_ : Dict = mel[idx_back : idx_back + chunk_frames, :]
A_ : Optional[int] = torch.tensor(mel[None, None, :] )
A_ : Dict = torch.nn.functional.interpolate(
_a ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=_a )
A_ : str = mel_shrink[0][0].numpy()
A_ : Tuple = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def _a ( self : Dict ,_a : np.array ,_a : Optional[Any] ,_a : int ,_a : Dict ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
A_ : Dict = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
A_ : Tuple = len(_a ) - max_length
A_ : Optional[int] = np.random.randint(0 ,overflow + 1 )
A_ : List[Any] = waveform[idx : idx + max_length]
A_ : Optional[Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
A_ : Dict = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Tuple = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
A_ : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
A_ : Optional[int] = np.stack([mel, mel, mel, mel] ,axis=0 )
A_ : str = False
else:
A_ : str = self._random_mel_fusion(_a ,_a ,_a )
A_ : Optional[Any] = True
else:
raise NotImplementedError(f'data_truncating {truncation} not implemented' )
else:
A_ : Optional[int] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
A_ : int = int(max_length / len(_a ) )
A_ : Any = np.stack(np.tile(_a ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
A_ : List[str] = int(max_length / len(_a ) )
A_ : Optional[Any] = np.stack(np.tile(_a ,_a ) )
A_ : Any = np.pad(_a ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
A_ : List[Any] = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
A_ : Union[str, Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[Any] ,_a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_a : str = None ,_a : Optional[str] = None ,_a : Optional[int] = None ,_a : Optional[int] = None ,_a : Optional[Union[str, TensorType]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : List[str] = truncation if truncation is not None else self.truncation
A_ : List[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
A_ : Any = isinstance(_a ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A_ : int = is_batched_numpy or (
isinstance(_a ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
A_ : Optional[int] = [np.asarray(_a ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_a ,np.ndarray ):
A_ : str = np.asarray(_a ,dtype=np.floataa )
elif isinstance(_a ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A_ : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ : Any = [np.asarray(_a )]
# convert to mel spectrogram, truncate and pad if needed.
A_ : str = [
self._get_input_mel(_a ,max_length if max_length else self.nb_max_samples ,_a ,_a )
for waveform in raw_speech
]
A_ : int = []
A_ : Any = []
for mel, longer in padded_inputs:
input_mel.append(_a )
is_longer.append(_a )
if truncation == "fusion" and sum(_a ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
A_ : List[Any] = np.random.randint(0 ,len(_a ) )
A_ : List[str] = True
if isinstance(input_mel[0] ,_a ):
A_ : Tuple = [np.asarray(_a ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
A_ : List[str] = [[longer] for longer in is_longer]
A_ : Optional[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
A_ : int = BatchFeature(_a )
if return_tensors is not None:
A_ : int = input_features.convert_to_tensors(_a )
return input_features
| 27 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : str):
if isinstance(lowerCamelCase , (list, tuple)) and isinstance(videos[0] , (list, tuple)) and is_valid_image(videos[0][0]):
return videos
elif isinstance(lowerCamelCase , (list, tuple)) and is_valid_image(videos[0]):
return [videos]
elif is_valid_image(lowerCamelCase):
return [[videos]]
raise ValueError(F'Could not make batched video from {videos}')
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : List[Any] ,_a : bool = True ,_a : Dict[str, int] = None ,_a : PILImageResampling = PILImageResampling.BILINEAR ,_a : bool = True ,_a : Dict[str, int] = None ,_a : bool = True ,_a : Union[int, float] = 1 / 255 ,_a : bool = True ,_a : bool = True ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = size if size is not None else {"""shortest_edge""": 256}
A_ : Any = get_size_dict(_a ,default_to_square=_a )
A_ : List[str] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
A_ : str = get_size_dict(_a ,param_name="""crop_size""" )
A_ : Any = do_resize
A_ : List[str] = size
A_ : Optional[int] = do_center_crop
A_ : Dict = crop_size
A_ : str = resample
A_ : Tuple = do_rescale
A_ : int = rescale_factor
A_ : Dict = offset
A_ : Optional[Any] = do_normalize
A_ : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
A_ : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a ( self : List[Any] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : PILImageResampling = PILImageResampling.BILINEAR ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Dict ,):
'''simple docstring'''
A_ : int = get_size_dict(_a ,default_to_square=_a )
if "shortest_edge" in size:
A_ : Dict = get_resize_output_image_size(_a ,size["""shortest_edge"""] ,default_to_square=_a )
elif "height" in size and "width" in size:
A_ : List[Any] = (size["""height"""], size["""width"""])
else:
raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Dict[str, int] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[Any] ,):
'''simple docstring'''
A_ : Tuple = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
return center_crop(_a ,size=(size["""height"""], size["""width"""]) ,data_format=_a ,**_a )
def _a ( self : int ,_a : np.ndarray ,_a : Union[int, float] ,_a : bool = True ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[str] ,):
'''simple docstring'''
A_ : Optional[int] = image.astype(np.floataa )
if offset:
A_ : Optional[int] = image - (scale / 2)
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Optional[Any] ,_a : np.ndarray ,_a : Union[float, List[float]] ,_a : Union[float, List[float]] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[Any] ,):
'''simple docstring'''
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def _a ( self : Tuple ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : Dict[str, int] = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[ChannelDimension] = ChannelDimension.FIRST ,):
'''simple docstring'''
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_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.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
A_ : Optional[int] = to_numpy_array(_a )
if do_resize:
A_ : Tuple = self.resize(image=_a ,size=_a ,resample=_a )
if do_center_crop:
A_ : Optional[int] = self.center_crop(_a ,size=_a )
if do_rescale:
A_ : Any = self.rescale(image=_a ,scale=_a ,offset=_a )
if do_normalize:
A_ : Tuple = self.normalize(image=_a ,mean=_a ,std=_a )
A_ : int = to_channel_dimension_format(_a ,_a )
return image
def _a ( self : int ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : Dict[str, int] = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[str, TensorType]] = None ,_a : ChannelDimension = ChannelDimension.FIRST ,**_a : List[Any] ,):
'''simple docstring'''
A_ : str = do_resize if do_resize is not None else self.do_resize
A_ : Tuple = resample if resample is not None else self.resample
A_ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : Dict = do_rescale if do_rescale is not None else self.do_rescale
A_ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : int = offset if offset is not None else self.offset
A_ : List[str] = do_normalize if do_normalize is not None else self.do_normalize
A_ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
A_ : int = image_std if image_std is not None else self.image_std
A_ : str = size if size is not None else self.size
A_ : List[Any] = get_size_dict(_a ,default_to_square=_a )
A_ : Tuple = crop_size if crop_size is not None else self.crop_size
A_ : List[str] = get_size_dict(_a ,param_name="""crop_size""" )
if not valid_images(_a ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
A_ : List[Any] = make_batched(_a )
A_ : List[Any] = [
[
self._preprocess_image(
image=_a ,do_resize=_a ,size=_a ,resample=_a ,do_center_crop=_a ,crop_size=_a ,do_rescale=_a ,rescale_factor=_a ,offset=_a ,do_normalize=_a ,image_mean=_a ,image_std=_a ,data_format=_a ,)
for img in video
]
for video in videos
]
A_ : List[Any] = {"""pixel_values""": videos}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : List[Any] ,_a : Dict=13 ,_a : List[Any]=7 ,_a : Optional[Any]=True ,_a : Any=True ,_a : Optional[int]=True ,_a : Union[str, Any]=99 ,_a : Union[str, Any]=32 ,_a : List[str]=5 ,_a : List[str]=4 ,_a : Dict=37 ,_a : List[Any]="gelu" ,_a : int=0.1 ,_a : Optional[int]=0.1 ,_a : Tuple=512 ,_a : Union[str, Any]=16 ,_a : Optional[Any]=2 ,_a : Optional[Any]=0.02 ,_a : Optional[int]=3 ,_a : str=4 ,_a : Optional[Any]=None ,):
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : str = batch_size
A_ : int = seq_length
A_ : Union[str, Any] = is_training
A_ : Optional[Any] = use_token_type_ids
A_ : int = use_labels
A_ : Dict = vocab_size
A_ : List[Any] = hidden_size
A_ : Tuple = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : int = intermediate_size
A_ : Tuple = hidden_act
A_ : int = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : Any = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Tuple = type_sequence_label_size
A_ : int = initializer_range
A_ : Optional[Any] = num_labels
A_ : str = num_choices
A_ : Optional[Any] = scope
A_ : List[Any] = self.vocab_size - 1
def _a ( self : Any ):
'''simple docstring'''
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : List[Any] = None
if self.use_token_type_ids:
A_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : int = None
A_ : str = None
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Any = ids_tensor([self.batch_size] ,self.num_choices )
A_ : List[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,pad_token_id=self.pad_token_id ,)
A_ : Tuple = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _a ( self : Optional[int] ,_a : List[str] ,_a : str ,_a : int ,_a : int ,*_a : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[Any] = OpenAIGPTModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,head_mask=_a )
A_ : str = model(_a ,token_type_ids=_a )
A_ : Dict = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Dict ,_a : Optional[int] ,_a : Union[str, Any] ,_a : Dict ,_a : List[str] ,*_a : str ):
'''simple docstring'''
A_ : str = OpenAIGPTLMHeadModel(_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Any ,_a : Dict ,_a : List[Any] ,_a : Dict ,_a : Union[str, Any] ,*_a : str ):
'''simple docstring'''
A_ : Any = OpenAIGPTDoubleHeadsModel(_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : List[str] ,_a : str ,_a : Tuple ,_a : Dict ,_a : Tuple ,*_a : Dict ):
'''simple docstring'''
A_ : List[str] = self.num_labels
A_ : int = OpenAIGPTForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Optional[Any] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : str = config_and_inputs
A_ : int = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
a_ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
a_ = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _a ( self : Tuple ,_a : Optional[int] ,_a : str ,_a : List[str] ,_a : List[str] ,_a : Any ):
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _a ( self : Optional[int] ,_a : str ,_a : Dict ,_a : Optional[int]=False ):
'''simple docstring'''
A_ : Any = super()._prepare_for_class(_a ,_a ,return_labels=_a )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
A_ : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,dtype=torch.long ,device=_a ,)
A_ : Any = inputs_dict["""labels"""]
A_ : Any = inputs_dict["""labels"""]
A_ : Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) ,dtype=torch.long ,device=_a ,)
A_ : int = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=_a )
return inputs_dict
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Tuple = OpenAIGPTModelTester(self )
A_ : Optional[int] = ConfigTester(self ,config_class=_a ,n_embd=37 )
def _a ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_a )
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a )
@slow
def _a ( self : List[Any] ):
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = OpenAIGPTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Dict = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(_a )
A_ : Dict = torch.tensor([[481, 4735, 544]] ,dtype=torch.long ,device=_a ) # the president is
A_ : Dict = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
A_ : int = model.generate(_a ,do_sample=_a )
self.assertListEqual(output_ids[0].tolist() ,_a )
| 27 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def lowerCamelCase ( lowerCamelCase : int = 100_0000 , lowerCamelCase : int = 10):
A_ : defaultdict = defaultdict(lowerCamelCase)
for outer_width in range(3 , (t_limit // 4) + 2):
if outer_width * outer_width > t_limit:
A_ : Optional[Any] = max(
ceil(sqrt(outer_width * outer_width - t_limit)) , 1)
else:
A_ : Dict = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(lowerCamelCase , outer_width - 1 , 2):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
import baseaa
def lowerCamelCase ( lowerCamelCase : str):
return baseaa.aaaencode(string.encode("""utf-8"""))
def lowerCamelCase ( lowerCamelCase : bytes):
return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
'''simple docstring'''
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 _a ( self : int ):
'''simple docstring'''
super().tearDown()
gc.collect()
def _a ( self : Tuple ):
'''simple docstring'''
A_ , A_ : Tuple = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" ,from_pt=_a ,dtype=jnp.bfloataa )
A_ , A_ : Dict = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,controlnet=_a ,from_pt=_a ,dtype=jnp.bfloataa )
A_ : List[str] = controlnet_params
A_ : Any = """bird"""
A_ : str = jax.device_count()
A_ : Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
A_ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
A_ : Union[str, Any] = pipe.prepare_image_inputs([canny_image] * num_samples )
A_ : Optional[Any] = jax.random.PRNGKey(0 )
A_ : Union[str, Any] = jax.random.split(_a ,jax.device_count() )
A_ : int = replicate(_a )
A_ : List[str] = shard(_a )
A_ : str = shard(_a )
A_ : Optional[Any] = pipe(
prompt_ids=_a ,image=_a ,params=_a ,prng_seed=_a ,num_inference_steps=50 ,jit=_a ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
A_ : str = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
A_ : Union[str, Any] = images[0, 253:256, 253:256, -1]
A_ : Optional[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
A_ : Optional[int] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ , A_ : Dict = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" ,from_pt=_a ,dtype=jnp.bfloataa )
A_ , A_ : int = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,controlnet=_a ,from_pt=_a ,dtype=jnp.bfloataa )
A_ : Tuple = controlnet_params
A_ : int = """Chef in the kitchen"""
A_ : Any = jax.device_count()
A_ : Any = pipe.prepare_text_inputs([prompts] * num_samples )
A_ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
A_ : str = pipe.prepare_image_inputs([pose_image] * num_samples )
A_ : Optional[Any] = jax.random.PRNGKey(0 )
A_ : Dict = jax.random.split(_a ,jax.device_count() )
A_ : Any = replicate(_a )
A_ : Union[str, Any] = shard(_a )
A_ : int = shard(_a )
A_ : str = pipe(
prompt_ids=_a ,image=_a ,params=_a ,prng_seed=_a ,num_inference_steps=50 ,jit=_a ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
A_ : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
A_ : Union[str, Any] = images[0, 253:256, 253:256, -1]
A_ : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
A_ : Optional[Any] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def lowerCamelCase ( lowerCamelCase : str):
# word like '180' or '身高' or '神'
for char in word:
A_ : Optional[Any] = ord(lowerCamelCase)
if not _is_chinese_char(lowerCamelCase):
return 0
return 1
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Any = set()
for token in tokens:
A_ : str = len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
if chinese_word:
word_set.add(lowerCamelCase)
A_ : Any = list(lowerCamelCase)
return word_list
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : set()):
if not chinese_word_set:
return bert_tokens
A_ : Any = max([len(lowerCamelCase) for w in chinese_word_set])
A_ : str = bert_tokens
A_ , A_ : Any = 0, len(lowerCamelCase)
while start < end:
A_ : Tuple = True
if is_chinese(bert_word[start]):
A_ : List[str] = min(end - start , lowerCamelCase)
for i in range(lowerCamelCase , 1 , -1):
A_ : Tuple = """""".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
A_ : Dict = """##""" + bert_word[j]
A_ : str = start + i
A_ : Dict = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : LTP , lowerCamelCase : BertTokenizer):
A_ : Union[str, Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : List[Any] = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""]).cws
A_ : int = [get_chinese_word(lowerCamelCase) for r in res]
ltp_res.extend(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : List[Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : Dict = bert_tokenizer(lines[i : i + 100] , add_special_tokens=lowerCamelCase , truncation=lowerCamelCase , max_length=512)
bert_res.extend(res["""input_ids"""])
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Union[str, Any] = []
for input_ids, chinese_word in zip(lowerCamelCase , lowerCamelCase):
A_ : List[Any] = []
for id in input_ids:
A_ : List[Any] = bert_tokenizer._convert_id_to_token(lowerCamelCase)
input_tokens.append(lowerCamelCase)
A_ : int = add_sub_symbol(lowerCamelCase , lowerCamelCase)
A_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCamelCase):
if token[:2] == "##":
A_ : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
ref_id.append(lowerCamelCase)
ref_ids.append(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
return ref_ids
def lowerCamelCase ( lowerCamelCase : Tuple):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""") as f:
A_ : Optional[int] = f.readlines()
A_ : Union[str, Any] = [line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ : Optional[Any] = LTP(args.ltp) # faster in GPU device
A_ : Dict = BertTokenizer.from_pretrained(args.bert)
A_ : str = prepare_ref(lowerCamelCase , lowerCamelCase , lowerCamelCase)
with open(args.save_path , """w""" , encoding="""utf-8""") as f:
A_ : Optional[Any] = [json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
f.writelines(lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = 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',
)
__magic_name__ = parser.parse_args()
main(args)
| 27 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__magic_name__ = logging.getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Tuple):
return (preds == labels).mean()
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
a_ = field(metadata={"""help""": """Should contain the data files for the task."""} )
a_ = 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."""
)
} , )
a_ = field(
default=__SCREAMING_SNAKE_CASE , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def lowerCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
A_ : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
A_ , A_ , A_ : Any = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""")
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase)
# Set seed
set_seed(training_args.seed)
try:
A_ : List[str] = processors[data_args.task_name]()
A_ : Optional[int] = processor.get_labels()
A_ : List[str] = len(lowerCamelCase)
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name))
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A_ : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
A_ : Tuple = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
A_ : Tuple = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path) , config=lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
A_ : Any = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
A_ : List[Any] = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(lowerCamelCase : EvalPrediction) -> Dict:
A_ : Dict = np.argmax(p.predictions , axis=1)
return {"acc": simple_accuracy(lowerCamelCase , p.label_ids)}
# Data collator
A_ : str = DataCollatorWithPadding(lowerCamelCase , pad_to_multiple_of=8) if training_args.fpaa else None
# Initialize our Trainer
A_ : Optional[Any] = Trainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , compute_metrics=lowerCamelCase , data_collator=lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
A_ : List[Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""")
A_ : List[str] = trainer.evaluate()
A_ : Union[str, Any] = os.path.join(training_args.output_dir , """eval_results.txt""")
if trainer.is_world_master():
with open(lowerCamelCase , """w""") as writer:
logger.info("""***** Eval results *****""")
for key, value in result.items():
logger.info(""" %s = %s""" , lowerCamelCase , lowerCamelCase)
writer.write("""%s = %s\n""" % (key, value))
results.update(lowerCamelCase)
return results
def lowerCamelCase ( lowerCamelCase : Any):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 27 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """ViltImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] ,_a : Optional[Any]=None ,_a : List[str]=None ,**_a : Any ):
'''simple docstring'''
A_ : 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.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[Any] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Any ,_a : Tuple ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
# add pixel_values + pixel_mask
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Any ,**_a : Any ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : int ,*_a : int ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : int ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 | 1 |
'''simple docstring'''
import os
from collections.abc import Iterator
def lowerCamelCase ( lowerCamelCase : str = "."):
for dir_path, dir_names, filenames in os.walk(lowerCamelCase):
A_ : Dict = [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(lowerCamelCase)[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase).lstrip("""./""")
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
return F'{i * " "}*' if i else "\n##"
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : str = old_path.split(os.sep)
for i, new_part in enumerate(new_path.split(os.sep)):
if (i + 1 > len(lowerCamelCase) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase)} {new_part.replace("_" , " ").title()}')
return new_path
def lowerCamelCase ( lowerCamelCase : str = "."):
A_ : Dict = """"""
for filepath in sorted(good_file_paths(lowerCamelCase)):
A_ , A_ : int = os.path.split(lowerCamelCase)
if filepath != old_path:
A_ : Optional[int] = print_path(lowerCamelCase , lowerCamelCase)
A_ : Optional[int] = (filepath.count(os.sep) + 1) if filepath else 0
A_ : str = F'{filepath}/{filename}'.replace(""" """ , """%20""")
A_ : Optional[Any] = os.path.splitext(filename.replace("""_""" , """ """).title())[0]
print(F'{md_prefix(lowerCamelCase)} [{filename}]({url})')
if __name__ == "__main__":
print_directory_md('.')
| 27 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""torch""", """torchsde"""]
def __init__( self : Any ,*_a : Union[str, Any] ,**_a : Optional[int] ):
'''simple docstring'''
requires_backends(self ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : Optional[int] ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : List[Any] ,*_a : Tuple ,**_a : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
| 27 | 1 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : Dict ,_a : Any=2 ,_a : Dict=8 ,_a : str=True ,_a : List[Any]=True ,_a : int=True ,_a : Union[str, Any]=True ,_a : List[str]=99 ,_a : Any=16 ,_a : Optional[Any]=5 ,_a : Any=2 ,_a : List[Any]=36 ,_a : Any="gelu" ,_a : str=0.0 ,_a : List[str]=0.0 ,_a : Union[str, Any]=512 ,_a : Dict=16 ,_a : str=2 ,_a : Any=0.02 ,_a : Union[str, Any]=3 ,_a : List[str]=4 ,_a : Optional[int]=None ,):
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : Optional[Any] = batch_size
A_ : Optional[Any] = seq_length
A_ : List[str] = is_training
A_ : Optional[int] = use_input_mask
A_ : List[str] = use_token_type_ids
A_ : Optional[Any] = use_labels
A_ : int = vocab_size
A_ : List[str] = hidden_size
A_ : Optional[int] = num_hidden_layers
A_ : Dict = num_attention_heads
A_ : Optional[int] = intermediate_size
A_ : int = hidden_act
A_ : str = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Optional[int] = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Union[str, Any] = type_sequence_label_size
A_ : Optional[Any] = initializer_range
A_ : Optional[int] = num_labels
A_ : Dict = num_choices
A_ : str = scope
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : int = None
if self.use_input_mask:
A_ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
A_ : Optional[Any] = None
if self.use_token_type_ids:
A_ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : Dict = None
A_ : List[str] = None
A_ : Dict = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : int = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : List[Any] = ids_tensor([self.batch_size] ,self.num_choices )
A_ : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ):
'''simple docstring'''
return MraConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=_a ,initializer_range=self.initializer_range ,)
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = self.get_config()
A_ : Dict = 300
return config
def _a ( self : Union[str, Any] ):
'''simple docstring'''
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Union[str, Any] = self.prepare_config_and_inputs()
A_ : Optional[int] = True
A_ : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
A_ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _a ( self : Tuple ,_a : str ,_a : Optional[int] ,_a : List[Any] ,_a : Optional[int] ,_a : str ,_a : str ,_a : List[Any] ):
'''simple docstring'''
A_ : List[Any] = MraModel(config=_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,attention_mask=_a ,token_type_ids=_a )
A_ : List[str] = model(_a ,token_type_ids=_a )
A_ : str = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : int ,_a : List[Any] ,_a : Optional[Any] ,_a : List[str] ,_a : Optional[Any] ,_a : List[str] ,_a : int ,_a : Union[str, Any] ,_a : str ,_a : List[Any] ,):
'''simple docstring'''
A_ : Optional[int] = True
A_ : Any = MraModel(_a )
model.to(_a )
model.eval()
A_ : List[Any] = model(
_a ,attention_mask=_a ,token_type_ids=_a ,encoder_hidden_states=_a ,encoder_attention_mask=_a ,)
A_ : str = model(
_a ,attention_mask=_a ,token_type_ids=_a ,encoder_hidden_states=_a ,)
A_ : Union[str, Any] = model(_a ,attention_mask=_a ,token_type_ids=_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Optional[Any] ,_a : int ,_a : Optional[Any] ,_a : int ,_a : Tuple ,_a : Tuple ,_a : Dict ,_a : str ):
'''simple docstring'''
A_ : Optional[Any] = MraForMaskedLM(config=_a )
model.to(_a )
model.eval()
A_ : 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.vocab_size) )
def _a ( self : Optional[Any] ,_a : str ,_a : Optional[Any] ,_a : Tuple ,_a : Union[str, Any] ,_a : int ,_a : List[Any] ,_a : str ):
'''simple docstring'''
A_ : str = MraForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
A_ : 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 _a ( self : str ,_a : int ,_a : str ,_a : Optional[int] ,_a : Any ,_a : Optional[Any] ,_a : str ,_a : int ):
'''simple docstring'''
A_ : Optional[Any] = self.num_labels
A_ : Dict = MraForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : List[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 _a ( self : Any ,_a : Optional[Any] ,_a : Optional[int] ,_a : str ,_a : Optional[int] ,_a : Optional[Any] ,_a : Tuple ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Any = self.num_labels
A_ : Union[str, Any] = MraForTokenClassification(config=_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,attention_mask=_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _a ( self : Union[str, Any] ,_a : str ,_a : List[str] ,_a : Tuple ,_a : Optional[Any] ,_a : str ,_a : Tuple ,_a : Tuple ):
'''simple docstring'''
A_ : Any = self.num_choices
A_ : int = MraForMultipleChoice(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : Any = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
A_ : Optional[int] = model(
_a ,attention_mask=_a ,token_type_ids=_a ,labels=_a ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _a ( self : str ):
'''simple docstring'''
A_ : Any = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Optional[Any] = config_and_inputs
A_ : Dict = {"""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 , unittest.TestCase ):
'''simple docstring'''
a_ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
a_ = False
a_ = False
a_ = False
a_ = False
a_ = ()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : List[str] = MraModelTester(self )
A_ : Optional[int] = ConfigTester(self ,config_class=_a ,hidden_size=37 )
def _a ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Any = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A_ : List[Any] = type
self.model_tester.create_and_check_model(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_a )
def _a ( self : List[str] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_a )
def _a ( self : int ):
'''simple docstring'''
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_a )
@slow
def _a ( self : Tuple ):
'''simple docstring'''
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Dict = MraModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason="""MRA does not output attentions""" )
def _a ( self : Tuple ):
'''simple docstring'''
return
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : str ):
'''simple docstring'''
A_ : Any = MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
A_ : Dict = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
A_ : Any = model(_a )[0]
A_ : str = torch.Size((1, 256, 768) )
self.assertEqual(output.shape ,_a )
A_ : List[Any] = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,_a ,atol=1e-4 ) )
@slow
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Optional[int] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
A_ : Union[str, Any] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
A_ : Tuple = model(_a )[0]
A_ : str = 50265
A_ : Dict = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape ,_a )
A_ : Tuple = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,_a ,atol=1e-4 ) )
@slow
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : str = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
A_ : Optional[int] = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
A_ : List[Any] = model(_a )[0]
A_ : List[str] = 50265
A_ : Union[str, Any] = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape ,_a )
A_ : Optional[int] = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,_a ,atol=1e-4 ) )
| 27 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]="pt"):
A_ : Optional[int] = {"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase) and not line.startswith(""" """) else {}
A_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , ):
A_ : Dict = input_ids.ne(lowerCamelCase).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Dict ,_a : Tuple ,_a : Tuple="train" ,_a : Optional[int]=None ,_a : Any=None ,_a : int=None ,_a : Union[str, Any]="" ,):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = Path(_a ).joinpath(type_path + """.source""" )
A_ : Any = Path(_a ).joinpath(type_path + """.target""" )
A_ : Dict = self.get_char_lens(self.src_file )
A_ : Optional[int] = max_source_length
A_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A_ : List[Any] = tokenizer
A_ : Optional[Any] = prefix
if n_obs is not None:
A_ : Any = self.src_lens[:n_obs]
A_ : Optional[int] = src_lang
A_ : Tuple = tgt_lang
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : List[str] ,_a : Tuple ):
'''simple docstring'''
A_ : int = index + 1 # linecache starts at 1
A_ : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,_a ).rstrip("""\n""" )
A_ : Dict = linecache.getline(str(self.tgt_file ) ,_a ).rstrip("""\n""" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ : List[str] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_a ) else self.tokenizer
)
A_ : Any = self.tokenizer.generator if isinstance(self.tokenizer ,_a ) else self.tokenizer
A_ : Optional[int] = encode_line(_a ,_a ,self.max_source_length ,"""right""" )
A_ : Optional[int] = encode_line(_a ,_a ,self.max_target_length ,"""right""" )
A_ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
A_ : Dict = target_inputs["""input_ids"""].squeeze()
A_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( _a : int ):
'''simple docstring'''
return [len(_a ) for x in Path(_a ).open().readlines()]
def _a ( self : Optional[int] ,_a : Dict ):
'''simple docstring'''
A_ : str = torch.stack([x["""input_ids"""] for x in batch] )
A_ : Optional[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
A_ : str = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : List[str] = trim_batch(_a ,_a )
A_ , A_ : Union[str, Any] = trim_batch(_a ,_a ,attention_mask=_a )
A_ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__magic_name__ = getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[List]):
return list(itertools.chain.from_iterable(lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json"""))
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : List[str]=4 , **lowerCamelCase : List[str]):
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase) as f:
return json.load(lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[str] = git.Repo(search_parent_directories=lowerCamelCase)
A_ : Union[str, Any] = {
"""repo_id""": str(lowerCamelCase),
"""repo_sha""": str(repo.head.object.hexsha),
"""repo_branch""": str(repo.active_branch),
"""hostname""": str(socket.gethostname()),
}
return repo_infos
def lowerCamelCase ( lowerCamelCase : Callable , lowerCamelCase : Iterable):
return list(map(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
with open(lowerCamelCase , """wb""") as f:
return pickle.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str]):
def remove_articles(lowerCamelCase : Any):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , lowerCamelCase)
def white_space_fix(lowerCamelCase : List[Any]):
return " ".join(text.split())
def remove_punc(lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(lowerCamelCase : List[str]):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = normalize_answer(lowerCamelCase).split()
A_ : Dict = normalize_answer(lowerCamelCase).split()
A_ : int = Counter(lowerCamelCase) & Counter(lowerCamelCase)
A_ : Any = sum(common.values())
if num_same == 0:
return 0
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Any):
return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[str]):
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Any = 0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase):
em += exact_match_score(lowerCamelCase , lowerCamelCase)
if len(lowerCamelCase) > 0:
em /= len(lowerCamelCase)
return {"em": em}
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return model_prefix.startswith("""rag""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
A_ : Optional[Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ : Tuple = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase):
if not hasattr(lowerCamelCase , lowerCamelCase) and not hasattr(lowerCamelCase , equivalent_param[p]):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
continue
A_ : Tuple = p if hasattr(lowerCamelCase , lowerCamelCase) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
return hparams, config
| 27 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
__magic_name__ = False
__magic_name__ = False
def lowerCamelCase ( lowerCamelCase : Namespace):
return TrainCommand(lowerCamelCase)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
def _a ( _a : ArgumentParser ):
'''simple docstring'''
A_ : Union[str, Any] = parser.add_parser("""train""" ,help="""CLI tool to train a model on a task.""" )
train_parser.add_argument(
"""--train_data""" ,type=_a ,required=_a ,help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.""" ,)
train_parser.add_argument(
"""--column_label""" ,type=_a ,default=0 ,help="""Column of the dataset csv file with example labels.""" )
train_parser.add_argument(
"""--column_text""" ,type=_a ,default=1 ,help="""Column of the dataset csv file with example texts.""" )
train_parser.add_argument(
"""--column_id""" ,type=_a ,default=2 ,help="""Column of the dataset csv file with example ids.""" )
train_parser.add_argument(
"""--skip_first_row""" ,action="""store_true""" ,help="""Skip the first row of the csv file (headers).""" )
train_parser.add_argument("""--validation_data""" ,type=_a ,default="""""" ,help="""path to validation dataset.""" )
train_parser.add_argument(
"""--validation_split""" ,type=_a ,default=0.1 ,help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset.""" ,)
train_parser.add_argument("""--output""" ,type=_a ,default="""./""" ,help="""path to saved the trained model.""" )
train_parser.add_argument(
"""--task""" ,type=_a ,default="""text_classification""" ,help="""Task to train the model on.""" )
train_parser.add_argument(
"""--model""" ,type=_a ,default="""bert-base-uncased""" ,help="""Model's name or path to stored model.""" )
train_parser.add_argument("""--train_batch_size""" ,type=_a ,default=32 ,help="""Batch size for training.""" )
train_parser.add_argument("""--valid_batch_size""" ,type=_a ,default=64 ,help="""Batch size for validation.""" )
train_parser.add_argument("""--learning_rate""" ,type=_a ,default=3e-5 ,help="""Learning rate.""" )
train_parser.add_argument("""--adam_epsilon""" ,type=_a ,default=1e-08 ,help="""Epsilon for Adam optimizer.""" )
train_parser.set_defaults(func=_a )
def __init__( self : Optional[Any] ,_a : Namespace ):
'''simple docstring'''
A_ : int = logging.get_logger("""transformers-cli/training""" )
A_ : Optional[Any] = """tf""" if is_tf_available() else """torch"""
os.makedirs(args.output ,exist_ok=_a )
A_ : Any = args.output
A_ : str = args.column_label
A_ : List[Any] = args.column_text
A_ : Dict = args.column_id
self.logger.info(f'Loading {args.task} pipeline for {args.model}' )
if args.task == "text_classification":
A_ : List[Any] = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(f'Loading dataset from {args.train_data}' )
A_ : Tuple = Processor.create_from_csv(
args.train_data ,column_label=args.column_label ,column_text=args.column_text ,column_id=args.column_id ,skip_first_row=args.skip_first_row ,)
A_ : List[Any] = None
if args.validation_data:
self.logger.info(f'Loading validation dataset from {args.validation_data}' )
A_ : Tuple = Processor.create_from_csv(
args.validation_data ,column_label=args.column_label ,column_text=args.column_text ,column_id=args.column_id ,skip_first_row=args.skip_first_row ,)
A_ : int = args.validation_split
A_ : Optional[Any] = args.train_batch_size
A_ : List[Any] = args.valid_batch_size
A_ : List[Any] = args.learning_rate
A_ : Optional[int] = args.adam_epsilon
def _a ( self : Optional[int] ):
'''simple docstring'''
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def _a ( self : List[Any] ):
'''simple docstring'''
raise NotImplementedError
def _a ( self : Dict ):
'''simple docstring'''
self.pipeline.fit(
self.train_dataset ,validation_data=self.valid_dataset ,validation_split=self.validation_split ,learning_rate=self.learning_rate ,adam_epsilon=self.adam_epsilon ,train_batch_size=self.train_batch_size ,valid_batch_size=self.valid_batch_size ,)
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_text_model"""
def __init__( self : Union[str, Any] ,_a : Any=49408 ,_a : Any=512 ,_a : Tuple=2048 ,_a : Dict=12 ,_a : Optional[int]=8 ,_a : Tuple=16 ,_a : Tuple="quick_gelu" ,_a : Optional[Any]=1e-5 ,_a : List[Any]=0.0 ,_a : Optional[int]=0.02 ,_a : Dict=1.0 ,_a : Dict=0 ,_a : Any=49406 ,_a : Tuple=49407 ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Optional[int] = intermediate_size
A_ : Optional[int] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : int = max_position_embeddings
A_ : str = hidden_act
A_ : Union[str, Any] = layer_norm_eps
A_ : Tuple = attention_dropout
A_ : Union[str, Any] = initializer_range
A_ : List[Any] = initializer_factor
@classmethod
def _a ( cls : List[str] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : int = cls.get_config_dict(_a ,**_a )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : Union[str, Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_vision_model"""
def __init__( self : List[Any] ,_a : Optional[Any]=768 ,_a : Tuple=3072 ,_a : Dict=12 ,_a : int=12 ,_a : Dict=3 ,_a : Tuple=768 ,_a : int=32 ,_a : int="quick_gelu" ,_a : List[Any]=1e-5 ,_a : Tuple=0.0 ,_a : List[Any]=0.02 ,_a : str=1.0 ,**_a : int ,):
'''simple docstring'''
super().__init__(**_a )
A_ : List[str] = hidden_size
A_ : Union[str, Any] = intermediate_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : int = num_channels
A_ : str = image_size
A_ : List[Any] = patch_size
A_ : int = hidden_act
A_ : List[Any] = layer_norm_eps
A_ : List[str] = attention_dropout
A_ : str = initializer_range
A_ : str = initializer_factor
@classmethod
def _a ( cls : List[Any] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : Optional[int] = cls.get_config_dict(_a ,**_a )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : List[str] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit"""
a_ = True
def __init__( self : Union[str, Any] ,_a : List[str]=None ,_a : List[str]=None ,_a : Dict=512 ,_a : List[Any]=2.6592 ,_a : Optional[Any]=True ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(**_a )
if text_config is None:
A_ : List[Any] = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
A_ : Tuple = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
A_ : Dict = OwlViTTextConfig(**_a )
A_ : Dict = OwlViTVisionConfig(**_a )
A_ : Any = projection_dim
A_ : Optional[int] = logit_scale_init_value
A_ : Optional[int] = return_dict
A_ : Dict = 1.0
@classmethod
def _a ( cls : Union[str, Any] ,_a : Union[str, os.PathLike] ,**_a : Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : List[Any] = cls.get_config_dict(_a ,**_a )
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
@classmethod
def _a ( cls : int ,_a : Dict ,_a : Dict ,**_a : List[str] ):
'''simple docstring'''
A_ : str = {}
A_ : int = text_config
A_ : Union[str, Any] = vision_config
return cls.from_dict(_a ,**_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = copy.deepcopy(self.__dict__ )
A_ : str = self.text_config.to_dict()
A_ : Optional[int] = self.vision_config.to_dict()
A_ : List[Any] = self.__class__.model_type
return output
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : int ):
'''simple docstring'''
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _a ( self : str ):
'''simple docstring'''
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
def _a ( self : int ,_a : "ProcessorMixin" ,_a : int = -1 ,_a : int = -1 ,_a : Optional["TensorType"] = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(
processor.tokenizer ,batch_size=_a ,seq_length=_a ,framework=_a )
A_ : Any = super().generate_dummy_inputs(
processor.image_processor ,batch_size=_a ,framework=_a )
return {**text_input_dict, **image_input_dict}
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 14
| 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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'''
a_ = KandinskyVaaControlnetPipeline
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a_ = False
@property
def _a ( self : Any ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return self.time_input_dim
@property
def _a ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 100
@property
def _a ( self : List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[Any] = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
A_ : Tuple = UNetaDConditionModel(**_a )
return model
@property
def _a ( self : List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Optional[Any] = self.dummy_unet
A_ : int = self.dummy_movq
A_ : Tuple = DDIMScheduler(
num_train_timesteps=1000 ,beta_schedule="""linear""" ,beta_start=0.00085 ,beta_end=0.012 ,clip_sample=_a ,set_alpha_to_one=_a ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=_a ,)
A_ : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _a ( self : Dict ,_a : str ,_a : Union[str, Any]=0 ):
'''simple docstring'''
A_ : Dict = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(_a ) ).to(_a )
A_ : int = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
_a )
# create hint
A_ : List[Any] = floats_tensor((1, 3, 64, 64) ,rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_a )
else:
A_ : str = torch.Generator(device=_a ).manual_seed(_a )
A_ : List[Any] = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[Any] = """cpu"""
A_ : List[str] = self.get_dummy_components()
A_ : Tuple = self.pipeline_class(**_a )
A_ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = pipe(**self.get_dummy_inputs(_a ) )
A_ : Tuple = output.images
A_ : Optional[Any] = pipe(
**self.get_dummy_inputs(_a ) ,return_dict=_a ,)[0]
A_ : Tuple = image[0, -3:, -3:, -1]
A_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : List[Any] = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
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 _a ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Any ):
'''simple docstring'''
A_ : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
A_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
A_ : Optional[int] = torch.from_numpy(np.array(_a ) ).float() / 255.0
A_ : List[Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
A_ : List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(_a )
A_ : Union[str, Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
A_ : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
A_ : Optional[Any] = """A robot, 4k photo"""
A_ : Any = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ , A_ : List[str] = pipe_prior(
_a ,generator=_a ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
A_ : int = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ : List[Any] = pipeline(
image_embeds=_a ,negative_image_embeds=_a ,hint=_a ,generator=_a ,num_inference_steps=100 ,output_type="""np""" ,)
A_ : Dict = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_a ,_a )
| 27 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'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'''
a_ = """roformer"""
def __init__( self : List[Any] ,_a : Tuple=50000 ,_a : List[str]=None ,_a : int=768 ,_a : List[str]=12 ,_a : Optional[Any]=12 ,_a : Union[str, Any]=3072 ,_a : Optional[int]="gelu" ,_a : Dict=0.1 ,_a : List[str]=0.1 ,_a : Any=1536 ,_a : Optional[Any]=2 ,_a : List[Any]=0.02 ,_a : Dict=1e-12 ,_a : Union[str, Any]=0 ,_a : List[str]=False ,_a : str=True ,**_a : Optional[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,**_a )
A_ : Optional[int] = vocab_size
A_ : str = hidden_size if embedding_size is None else embedding_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : List[str] = hidden_act
A_ : str = intermediate_size
A_ : Union[str, Any] = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : Union[str, Any] = max_position_embeddings
A_ : List[str] = type_vocab_size
A_ : List[str] = initializer_range
A_ : List[str] = layer_norm_eps
A_ : Optional[Any] = rotary_value
A_ : Union[str, Any] = use_cache
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Tuple = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : int = {0: """batch""", 1: """sequence"""}
A_ : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 27 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """deberta-v2"""
def __init__( self : Optional[Any] ,_a : Union[str, Any]=128100 ,_a : Optional[int]=1536 ,_a : Dict=24 ,_a : int=24 ,_a : Tuple=6144 ,_a : Union[str, Any]="gelu" ,_a : List[Any]=0.1 ,_a : Dict=0.1 ,_a : int=512 ,_a : int=0 ,_a : int=0.02 ,_a : int=1e-7 ,_a : List[str]=False ,_a : Union[str, Any]=-1 ,_a : List[Any]=0 ,_a : Optional[Any]=True ,_a : Tuple=None ,_a : Any=0 ,_a : int="gelu" ,**_a : Any ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : List[Any] = initializer_range
A_ : int = relative_attention
A_ : Tuple = max_relative_positions
A_ : int = pad_token_id
A_ : Tuple = position_biased_input
# Backwards compatibility
if type(_a ) == str:
A_ : str = [x.strip() for x in pos_att_type.lower().split("""|""" )]
A_ : Any = pos_att_type
A_ : Optional[int] = vocab_size
A_ : Tuple = layer_norm_eps
A_ : Any = kwargs.get("""pooler_hidden_size""" ,_a )
A_ : Union[str, Any] = pooler_dropout
A_ : List[Any] = pooler_hidden_act
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : Any = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
return 12
def _a ( self : int ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 3 ,_a : int = 40 ,_a : int = 40 ,_a : "PreTrainedTokenizerBase" = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(preprocessor=_a ,framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 27 | 1 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,*_a : List[Any] ,_a : Optional[Any]=None ,_a : int=None ,**_a : Tuple ):
'''simple docstring'''
super().__init__(*_a ,**_a )
A_ : Tuple = eval_examples
A_ : Optional[int] = post_process_function
def _a ( self : Tuple ,_a : Tuple=None ,_a : Union[str, Any]=None ,_a : List[Any]=None ,_a : str = "eval" ):
'''simple docstring'''
A_ : str = self.eval_dataset if eval_dataset is None else eval_dataset
A_ : Optional[Any] = self.get_eval_dataloader(_a )
A_ : Union[str, Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
A_ : List[str] = self.compute_metrics
A_ : Tuple = None
A_ : Optional[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
A_ : List[str] = time.time()
try:
A_ : Dict = eval_loop(
_a ,description="""Evaluation""" ,prediction_loss_only=True if compute_metrics is None else None ,ignore_keys=_a ,metric_key_prefix=_a ,)
finally:
A_ : Optional[Any] = compute_metrics
A_ : Tuple = self.args.eval_batch_size * self.args.world_size
if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
_a ,_a ,num_samples=output.num_samples ,num_steps=math.ceil(output.num_samples / total_batch_size ) ,) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
A_ : int = self.post_process_function(_a ,_a ,output.predictions )
A_ : str = self.compute_metrics(_a )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'{metric_key_prefix}_' ):
A_ : List[Any] = metrics.pop(_a )
metrics.update(output.metrics )
else:
A_ : List[Any] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_a )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
A_ : List[Any] = self.callback_handler.on_evaluate(self.args ,self.state ,self.control ,_a )
return metrics
def _a ( self : Optional[Any] ,_a : Dict ,_a : List[Any] ,_a : List[Any]=None ,_a : str = "test" ):
'''simple docstring'''
A_ : str = self.get_test_dataloader(_a )
# Temporarily disable metric computation, we will do it in the loop here.
A_ : Any = self.compute_metrics
A_ : List[Any] = None
A_ : int = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
A_ : int = time.time()
try:
A_ : str = eval_loop(
_a ,description="""Prediction""" ,prediction_loss_only=True if compute_metrics is None else None ,ignore_keys=_a ,metric_key_prefix=_a ,)
finally:
A_ : Tuple = compute_metrics
A_ : Optional[Any] = self.args.eval_batch_size * self.args.world_size
if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
_a ,_a ,num_samples=output.num_samples ,num_steps=math.ceil(output.num_samples / total_batch_size ) ,) )
if self.post_process_function is None or self.compute_metrics is None:
return output
A_ : Any = self.post_process_function(_a ,_a ,output.predictions ,"""predict""" )
A_ : Union[str, Any] = self.compute_metrics(_a )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'{metric_key_prefix}_' ):
A_ : str = metrics.pop(_a )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions ,label_ids=predictions.label_ids ,metrics=_a )
| 27 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__magic_name__ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__magic_name__ = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
__magic_name__ = BeautifulSoup(res.text, 'html.parser')
__magic_name__ = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f"""https://google.com{link.get('href')}""")
| 27 | 1 |
'''simple docstring'''
import cmath
import math
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float):
A_ : Optional[Any] = math.radians(lowerCamelCase)
A_ : Any = math.radians(lowerCamelCase)
# Convert voltage and current to rectangular form
A_ : Optional[int] = cmath.rect(lowerCamelCase , lowerCamelCase)
A_ : Optional[int] = cmath.rect(lowerCamelCase , lowerCamelCase)
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = """nezha"""
def __init__( self : int ,_a : Union[str, Any]=21128 ,_a : int=768 ,_a : Any=12 ,_a : List[str]=12 ,_a : str=3072 ,_a : int="gelu" ,_a : int=0.1 ,_a : str=0.1 ,_a : Tuple=512 ,_a : List[Any]=64 ,_a : Dict=2 ,_a : List[Any]=0.02 ,_a : Optional[Any]=1e-12 ,_a : List[Any]=0.1 ,_a : Union[str, Any]=0 ,_a : Any=2 ,_a : Union[str, Any]=3 ,_a : int=True ,**_a : int ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Dict = max_position_embeddings
A_ : Optional[Any] = max_relative_position
A_ : List[Any] = type_vocab_size
A_ : int = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Dict = classifier_dropout
A_ : int = use_cache
| 27 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """t5"""
a_ = ["""past_key_values"""]
a_ = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self : List[Any] ,_a : List[str]=32128 ,_a : Any=512 ,_a : Dict=64 ,_a : List[Any]=2048 ,_a : str=6 ,_a : List[Any]=None ,_a : Optional[Any]=8 ,_a : List[str]=32 ,_a : Dict=128 ,_a : Optional[Any]=0.1 ,_a : str=1e-6 ,_a : Optional[Any]=1.0 ,_a : List[Any]="relu" ,_a : Optional[Any]=True ,_a : Optional[int]=True ,_a : Optional[Any]=0 ,_a : Optional[int]=1 ,**_a : List[str] ,):
'''simple docstring'''
A_ : Optional[int] = vocab_size
A_ : Dict = d_model
A_ : List[Any] = d_kv
A_ : Tuple = d_ff
A_ : Any = num_layers
A_ : Optional[Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
A_ : List[Any] = num_heads
A_ : Optional[Any] = relative_attention_num_buckets
A_ : Optional[Any] = relative_attention_max_distance
A_ : int = dropout_rate
A_ : List[Any] = layer_norm_epsilon
A_ : int = initializer_factor
A_ : Any = feed_forward_proj
A_ : List[str] = use_cache
A_ : Optional[Any] = self.feed_forward_proj.split("""-""" )
A_ : Optional[int] = act_info[-1]
A_ : Tuple = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
A_ : Dict = """gelu_new"""
super().__init__(
pad_token_id=_a ,eos_token_id=_a ,is_encoder_decoder=_a ,**_a ,)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
A_ : Union[str, Any] = """past_encoder_sequence + sequence"""
A_ : int = {0: """batch"""}
A_ : int = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
A_ : Union[str, Any] = {0: """batch""", 1: """decoder_sequence"""}
A_ : Union[str, Any] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a ,direction="""inputs""" )
return common_inputs
@property
def _a ( self : List[str] ):
'''simple docstring'''
return 13
| 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] = stack.pop()
explored.add(lowerCamelCase)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(lowerCamelCase)
return explored
__magic_name__ = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( ):
for n in range(1 , 100_0000):
yield n * (n + 1) // 2
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
A_ : List[Any] = 1
A_ : Optional[int] = 2
while i * i <= n:
A_ : List[str] = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCamelCase ( ):
return next(i for i in triangle_number_generator() if count_divisors(lowerCamelCase) > 500)
if __name__ == "__main__":
print(solution())
| 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : List[str] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
A_ : Union[str, Any] = [144, 192, 240]
A_ : int = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
A_ : List[str] = [96, 120, 144]
A_ : Any = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
A_ : Any = [64, 80, 96]
A_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
A_ : Any = 0.05
A_ : List[Any] = 2.0
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : int = 512
A_ : Optional[int] = 16
A_ : List[Any] = 21
A_ : List[str] = """pascal-voc-id2label.json"""
else:
A_ : str = 1000
A_ : Any = """imagenet-1k-id2label.json"""
A_ : Any = """huggingface/label-files"""
A_ : List[str] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""") , """r"""))
A_ : str = {int(lowerCamelCase): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int=False):
for i in range(1 , 6):
if F'layer_{i}.' in name:
A_ : Tuple = name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.')
if "conv_1." in name:
A_ : Union[str, Any] = name.replace("""conv_1.""" , """conv_stem.""")
if ".block." in name:
A_ : Optional[Any] = name.replace(""".block.""" , """.""")
if "exp_1x1" in name:
A_ : Union[str, Any] = name.replace("""exp_1x1""" , """expand_1x1""")
if "red_1x1" in name:
A_ : int = name.replace("""red_1x1""" , """reduce_1x1""")
if ".local_rep.conv_3x3." in name:
A_ : List[str] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""")
if ".local_rep.conv_1x1." in name:
A_ : Optional[int] = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""")
if ".norm." in name:
A_ : Tuple = name.replace(""".norm.""" , """.normalization.""")
if ".conv." in name:
A_ : List[Any] = name.replace(""".conv.""" , """.convolution.""")
if ".conv_proj." in name:
A_ : str = name.replace(""".conv_proj.""" , """.conv_projection.""")
for i in range(0 , 2):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Tuple = name.replace(F'.{i}.{j}.' , F'.{i}.layer.{j}.')
for i in range(2 , 6):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Dict = name.replace(F'.{i}.{j}.' , F'.{i}.')
if "expand_1x1" in name:
A_ : Union[str, Any] = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""")
if "conv_3x3" in name:
A_ : str = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""")
if "reduce_1x1" in name:
A_ : Union[str, Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""")
for i in range(2 , 5):
if F'.global_rep.{i}.weight' in name:
A_ : List[Any] = name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight""")
if F'.global_rep.{i}.bias' in name:
A_ : Optional[int] = name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias""")
if ".global_rep." in name:
A_ : Optional[Any] = name.replace(""".global_rep.""" , """.transformer.""")
if ".pre_norm_mha.0." in name:
A_ : int = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""")
if ".pre_norm_mha.1.out_proj." in name:
A_ : Dict = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""")
if ".pre_norm_ffn.0." in name:
A_ : Dict = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""")
if ".pre_norm_ffn.1." in name:
A_ : Any = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""")
if ".pre_norm_ffn.4." in name:
A_ : Union[str, Any] = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""")
if ".transformer." in name:
A_ : Any = name.replace(""".transformer.""" , """.transformer.layer.""")
if ".aspp_layer." in name:
A_ : int = name.replace(""".aspp_layer.""" , """.""")
if ".aspp_pool." in name:
A_ : Tuple = name.replace(""".aspp_pool.""" , """.""")
if "seg_head." in name:
A_ : Optional[int] = name.replace("""seg_head.""" , """segmentation_head.""")
if "segmentation_head.classifier.classifier." in name:
A_ : List[str] = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""")
if "classifier.fc." in name:
A_ : str = name.replace("""classifier.fc.""" , """classifier.""")
elif (not base_model) and ("segmentation_head." not in name):
A_ : str = """mobilevit.""" + name
return name
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False):
if base_model:
A_ : Dict = """"""
else:
A_ : Any = """mobilevit."""
for key in orig_state_dict.copy().keys():
A_ : List[Any] = orig_state_dict.pop(lowerCamelCase)
if key[:8] == "encoder.":
A_ : int = key[8:]
if "qkv" in key:
A_ : Any = key.split(""".""")
A_ : str = int(key_split[0][6:]) - 1
A_ : int = int(key_split[3])
A_ : Optional[Any] = model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}')
A_ : Tuple = layer.transformer.layer[transformer_num].attention.attention.all_head_size
A_ : Optional[Any] = (
F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
)
if "weight" in key:
A_ : Dict = val[:dim, :]
A_ : Optional[int] = val[dim : dim * 2, :]
A_ : List[Any] = val[-dim:, :]
else:
A_ : Optional[Any] = val[:dim]
A_ : List[Any] = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : List[str] = val
return orig_state_dict
def lowerCamelCase ( ):
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase).raw)
return im
@torch.no_grad()
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : int=False):
A_ : Optional[Any] = get_mobilevit_config(lowerCamelCase)
# load original state_dict
A_ : List[Any] = torch.load(lowerCamelCase , map_location="""cpu""")
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : List[str] = MobileViTForSemanticSegmentation(lowerCamelCase).eval()
else:
A_ : str = MobileViTForImageClassification(lowerCamelCase).eval()
A_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase)
model.load_state_dict(lowerCamelCase)
# Check outputs on an image, prepared by MobileViTImageProcessor
A_ : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32)
A_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
A_ : List[Any] = model(**lowerCamelCase)
A_ : Dict = outputs.logits
if mobilevit_name.startswith("""deeplabv3_"""):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
A_ : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xs":
A_ : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
A_ : Tuple = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4)
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
A_ : Tuple = torch.tensor([-0.9866, 0.2392, -1.1241])
elif mobilevit_name == "mobilevit_xs":
A_ : Any = torch.tensor([-2.4761, -0.9399, -1.9587])
elif mobilevit_name == "mobilevit_xxs":
A_ : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4)
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowerCamelCase)
print(F'Saving image processor to {pytorch_dump_folder_path}')
image_processor.save_pretrained(lowerCamelCase)
if push_to_hub:
A_ : str = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""")
A_ : Union[str, Any] = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowerCamelCase , organization="""apple""")
model.push_to_hub(lowerCamelCase , organization="""apple""")
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
__magic_name__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 27 | 1 |
'''simple docstring'''
__magic_name__ = 'Alexander Joslin'
import operator as op
from .stack import Stack
def lowerCamelCase ( lowerCamelCase : str):
A_ : List[str] = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
A_ : Stack[int] = Stack()
A_ : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowerCamelCase))
elif i in operators:
# RULE 2
operator_stack.push(lowerCamelCase)
elif i == ")":
# RULE 4
A_ : List[str] = operator_stack.peek()
operator_stack.pop()
A_ : List[Any] = operand_stack.peek()
operand_stack.pop()
A_ : List[Any] = operand_stack.peek()
operand_stack.pop()
A_ : Any = operators[opr](lowerCamelCase , lowerCamelCase)
operand_stack.push(lowerCamelCase)
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__magic_name__ = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")
| 27 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__magic_name__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : Optional[Any] ,_a : bool = True ,_a : Dict[str, int] = None ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : bool = True ,_a : Dict[str, int] = None ,_a : bool = True ,_a : Union[int, float] = 1 / 255 ,_a : bool = True ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = True ,**_a : Dict ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Tuple = size if size is not None else {"""shortest_edge""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
A_ : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a ,param_name="""crop_size""" )
A_ : Any = do_resize
A_ : List[str] = size
A_ : Union[str, Any] = resample
A_ : Dict = do_center_crop
A_ : List[str] = crop_size
A_ : Any = do_rescale
A_ : Union[str, Any] = rescale_factor
A_ : Any = do_normalize
A_ : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A_ : List[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
A_ : Tuple = do_convert_rgb
def _a ( self : Optional[int] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[Any] ,):
'''simple docstring'''
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A_ : Tuple = get_resize_output_image_size(_a ,size=size["""shortest_edge"""] ,default_to_square=_a )
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def _a ( self : List[Any] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[int] ,):
'''simple docstring'''
A_ : Optional[int] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_a ,size=(size["""height"""], size["""width"""]) ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[int, float] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Any ,):
'''simple docstring'''
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[float, List[float]] ,_a : Union[float, List[float]] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[str] ,):
'''simple docstring'''
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def _a ( self : Optional[Any] ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : int = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[ChannelDimension] = ChannelDimension.FIRST ,**_a : int ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : Tuple = size if size is not None else self.size
A_ : Optional[int] = get_size_dict(_a ,param_name="""size""" ,default_to_square=_a )
A_ : List[str] = resample if resample is not None else self.resample
A_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : Any = crop_size if crop_size is not None else self.crop_size
A_ : int = get_size_dict(_a ,param_name="""crop_size""" ,default_to_square=_a )
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : Any = do_normalize if do_normalize is not None else self.do_normalize
A_ : int = image_mean if image_mean is not None else self.image_mean
A_ : int = image_std if image_std is not None else self.image_std
A_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A_ : int = make_list_of_images(_a )
if not valid_images(_a ):
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.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A_ : Optional[int] = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
A_ : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
A_ : int = [self.resize(image=_a ,size=_a ,resample=_a ) for image in images]
if do_center_crop:
A_ : Tuple = [self.center_crop(image=_a ,size=_a ) for image in images]
if do_rescale:
A_ : List[str] = [self.rescale(image=_a ,scale=_a ) for image in images]
if do_normalize:
A_ : Any = [self.normalize(image=_a ,mean=_a ,std=_a ) for image in images]
A_ : List[str] = [to_channel_dimension_format(_a ,_a ) for image in images]
A_ : List[str] = {"""pixel_values""": images}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 | 1 |
'''simple docstring'''
import numpy as np
def lowerCamelCase ( lowerCamelCase : np.array):
return (2 / (1 + np.exp(-2 * vector))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,*_a : Optional[Any] ,**_a : Optional[int] ):
'''simple docstring'''
warnings.warn(
"""The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use OwlViTImageProcessor instead.""" ,_a ,)
super().__init__(*_a ,**_a )
| 27 | 1 |
'''simple docstring'''
from math import pi, sqrt, tan
def lowerCamelCase ( lowerCamelCase : float):
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""")
return 6 * side_length**2
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("""surface_area_cuboid() only accepts non-negative values""")
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def lowerCamelCase ( lowerCamelCase : float):
if radius < 0:
raise ValueError("""surface_area_sphere() only accepts non-negative values""")
return 4 * pi * radius**2
def lowerCamelCase ( lowerCamelCase : float):
if radius < 0:
raise ValueError("""surface_area_hemisphere() only accepts non-negative values""")
return 3 * pi * radius**2
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if radius < 0 or height < 0:
raise ValueError("""surface_area_cone() only accepts non-negative values""")
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"""surface_area_conical_frustum() only accepts non-negative values""")
A_ : List[str] = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if radius < 0 or height < 0:
raise ValueError("""surface_area_cylinder() only accepts non-negative values""")
return 2 * pi * radius * (height + radius)
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("""surface_area_torus() only accepts non-negative values""")
if torus_radius < tube_radius:
raise ValueError(
"""surface_area_torus() does not support spindle or self intersecting tori""")
return 4 * pow(lowerCamelCase , 2) * torus_radius * tube_radius
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if length < 0 or width < 0:
raise ValueError("""area_rectangle() only accepts non-negative values""")
return length * width
def lowerCamelCase ( lowerCamelCase : float):
if side_length < 0:
raise ValueError("""area_square() only accepts non-negative values""")
return side_length**2
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if base < 0 or height < 0:
raise ValueError("""area_triangle() only accepts non-negative values""")
return (base * height) / 2
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("""area_triangle_three_sides() only accepts non-negative values""")
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("""Given three sides do not form a triangle""")
A_ : List[str] = (sidea + sidea + sidea) / 2
A_ : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if base < 0 or height < 0:
raise ValueError("""area_parallelogram() only accepts non-negative values""")
return base * height
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("""area_trapezium() only accepts non-negative values""")
return 1 / 2 * (basea + basea) * height
def lowerCamelCase ( lowerCamelCase : float):
if radius < 0:
raise ValueError("""area_circle() only accepts non-negative values""")
return pi * radius**2
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if radius_x < 0 or radius_y < 0:
raise ValueError("""area_ellipse() only accepts non-negative values""")
return pi * radius_x * radius_y
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("""area_rhombus() only accepts non-negative values""")
return 1 / 2 * diagonal_a * diagonal_a
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : float):
if not isinstance(lowerCamelCase , lowerCamelCase) or sides < 3:
raise ValueError(
"""area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides""")
elif length < 0:
raise ValueError(
"""area_reg_polygon() only accepts non-negative values as \
length of a side""")
return (sides * length**2) / (4 * tan(pi / sides))
return (sides * length**2) / (4 * tan(pi / sides))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(f"""Rectangle: {area_rectangle(10, 20) = }""")
print(f"""Square: {area_square(10) = }""")
print(f"""Triangle: {area_triangle(10, 10) = }""")
print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(f"""Rhombus: {area_rhombus(10, 20) = }""")
print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(f"""Circle: {area_circle(20) = }""")
print(f"""Ellipse: {area_ellipse(10, 20) = }""")
print('\nSurface Areas of various geometric shapes: \n')
print(f"""Cube: {surface_area_cube(20) = }""")
print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(f"""Sphere: {surface_area_sphere(20) = }""")
print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(f"""Cone: {surface_area_cone(10, 20) = }""")
print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(f"""Torus: {surface_area_torus(20, 10) = }""")
print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(f"""Square: {area_reg_polygon(4, 10) = }""")
print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 27 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : complex , lowerCamelCase : str = "x" , lowerCamelCase : float = 10**-10 , lowerCamelCase : int = 1 , ):
A_ : int = symbols(lowerCamelCase)
A_ : List[Any] = lambdify(lowerCamelCase , lowerCamelCase)
A_ : List[str] = lambdify(lowerCamelCase , diff(lowerCamelCase , lowerCamelCase))
A_ : str = starting_point
while True:
if diff_function(lowerCamelCase) != 0:
A_ : int = prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
lowerCamelCase)
else:
raise ZeroDivisionError("""Could not find root""") from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess) < precision:
return next_guess
A_ : Union[str, Any] = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
# Find root of polynomial
# Find fourth Root of 5
print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}""",
)
# Find root of cos(x)
print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
| 27 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Any ,*_a : str ,**_a : int ):
'''simple docstring'''
warnings.warn(
"""The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use ImageGPTImageProcessor instead.""" ,_a ,)
super().__init__(*_a ,**_a )
| 27 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__magic_name__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_a : Dict ):
'''simple docstring'''
super().__init__()
A_ : List[str] = torchvision.models.resnetaaa(pretrained=_a )
A_ : int = list(model.children() )[:-2]
A_ : int = nn.Sequential(*_a )
A_ : Optional[int] = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def _a ( self : str ,_a : Optional[int] ):
'''simple docstring'''
A_ : Tuple = self.pool(self.model(_a ) )
A_ : Any = torch.flatten(_a ,start_dim=2 )
A_ : str = out.transpose(1 ,2 ).contiguous()
return out # BxNx2048
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : int ,_a : Optional[Any] ,_a : Optional[Any] ,_a : Dict ,_a : Dict ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Dict = [json.loads(_a ) for l in open(_a )]
A_ : Optional[int] = os.path.dirname(_a )
A_ : Optional[Any] = tokenizer
A_ : Optional[Any] = labels
A_ : List[Any] = len(_a )
A_ : str = max_seq_length
A_ : str = transforms
def __len__( self : str ):
'''simple docstring'''
return len(self.data )
def __getitem__( self : Tuple ,_a : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,add_special_tokens=_a ) )
A_ , A_ , A_ : Dict = sentence[0], sentence[1:-1], sentence[-1]
A_ : Optional[int] = sentence[: self.max_seq_length]
A_ : Any = torch.zeros(self.n_classes )
A_ : Tuple = 1
A_ : Optional[Any] = Image.open(os.path.join(self.data_dir ,self.data[index]["""img"""] ) ).convert("""RGB""" )
A_ : Union[str, Any] = self.transforms(_a )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def lowerCamelCase ( lowerCamelCase : str):
A_ : List[Any] = [len(row["""sentence"""]) for row in batch]
A_ , A_ : Dict = len(lowerCamelCase), max(lowerCamelCase)
A_ : Optional[int] = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
A_ : Tuple = torch.zeros(lowerCamelCase , lowerCamelCase , dtype=torch.long)
for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase , lowerCamelCase)):
A_ : str = input_row["""sentence"""]
A_ : Tuple = 1
A_ : int = torch.stack([row["""image"""] for row in batch])
A_ : str = torch.stack([row["""label"""] for row in batch])
A_ : List[Any] = torch.stack([row["""image_start_token"""] for row in batch])
A_ : Tuple = torch.stack([row["""image_end_token"""] for row in batch])
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase ( ):
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase ( ):
return transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4677_7044, 0.4453_1429, 0.4066_1017] , std=[0.1222_1994, 0.1214_5835, 0.1438_0469] , ),
])
| 27 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
},
{
"""framework""": """pytorch""",
"""script""": """run_ddp.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
},
{
"""framework""": """tensorflow""",
"""script""": """run_tf_dist.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
},
] )
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self : List[str] ):
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split() ,encoding="""utf-8""" ,check=_a ,)
assert hasattr(self ,"""env""" )
def _a ( self : Optional[Any] ,_a : Dict ):
'''simple docstring'''
A_ : str = f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
# distributed data settings
A_ : Dict = {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None
# creates estimator
return HuggingFace(
entry_point=self.script ,source_dir=self.env.test_path ,role=self.env.role ,image_uri=self.env.image_uri ,base_job_name=_a ,instance_count=_a ,instance_type=self.instance_type ,debugger_hook_config=_a ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path} ,metric_definitions=self.env.metric_definitions ,distribution=_a ,py_version="""py36""" ,)
def _a ( self : Tuple ,_a : Tuple ):
'''simple docstring'''
TrainingJobAnalytics(_a ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv' )
@parameterized.expand([(2,)] )
def _a ( self : Any ,_a : str ):
'''simple docstring'''
A_ : List[Any] = self.create_estimator(_a )
# run training
estimator.fit()
# result dataframe
A_ : Optional[int] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
A_ : Optional[Any] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
A_ : List[Any] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
A_ : List[str] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" ,999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'{estimator.latest_training_job.name}.json' ,"""w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} ,_a )
| 27 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase ( lowerCamelCase : int):
if num <= 0:
A_ : List[Any] = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase)
A_ : str = [True] * (num + 1)
A_ : Tuple = []
A_ : str = 2
A_ : Any = int(math.sqrt(lowerCamelCase))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(lowerCamelCase)
# Set multiples of start be False
for i in range(start * start , num + 1 , lowerCamelCase):
if sieve[i] is True:
A_ : Union[str, Any] = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(lowerCamelCase)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
return int((input_a, input_a).count(0) != 0)
def lowerCamelCase ( ):
assert nand_gate(0 , 0) == 1
assert nand_gate(0 , 1) == 1
assert nand_gate(1 , 0) == 1
assert nand_gate(1 , 1) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 27 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
__magic_name__ = trt.Logger(trt.Logger.WARNING)
__magic_name__ = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
__magic_name__ = logging.getLogger(__name__)
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--onnx_model_path',
default=None,
type=str,
required=True,
help='Path to ONNX model: ',
)
parser.add_argument(
'--output_dir',
default=None,
type=str,
required=True,
help='The output directory where the model checkpoints and predictions will be written.',
)
# Other parameters
parser.add_argument(
'--tokenizer_name',
default='',
type=str,
required=True,
help='Pretrained tokenizer name or path if not the same as model_name',
)
parser.add_argument(
'--version_2_with_negative',
action='store_true',
help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help='If null_score - best_non_null is greater than the threshold predict null.',
)
parser.add_argument(
'--max_seq_length',
default=384,
type=int,
help=(
'The maximum total input sequence length after WordPiece tokenization. Sequences '
'longer than this will be truncated, and sequences shorter than this will be padded.'
),
)
parser.add_argument(
'--doc_stride',
default=128,
type=int,
help='When splitting up a long document into chunks, how much stride to take between chunks.',
)
parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')
parser.add_argument(
'--n_best_size',
default=20,
type=int,
help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
'--max_answer_length',
default=30,
type=int,
help=(
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
),
)
parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')
parser.add_argument(
'--dataset_name',
type=str,
default=None,
required=True,
help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--dataset_config_name',
type=str,
default=None,
help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
'--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
'--fp16',
action='store_true',
help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
'--int8',
action='store_true',
help='Whether to use INT8',
)
__magic_name__ = parser.parse_args()
if args.tokenizer_name:
__magic_name__ = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
)
logger.info('Training/evaluation parameters %s', args)
__magic_name__ = args.per_device_eval_batch_size
__magic_name__ = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
__magic_name__ = True
__magic_name__ = 'temp_engine/bert-fp32.engine'
if args.fpaa:
__magic_name__ = 'temp_engine/bert-fp16.engine'
if args.inta:
__magic_name__ = 'temp_engine/bert-int8.engine'
# import ONNX file
if not os.path.exists('temp_engine'):
os.makedirs('temp_engine')
__magic_name__ = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
__magic_name__ = [network.get_input(i) for i in range(network.num_inputs)]
__magic_name__ = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
__magic_name__ = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
__magic_name__ = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
__magic_name__ = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, 'wb') as f:
f.write(engine.serialize())
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : str , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : str , lowerCamelCase : List[str]):
A_ : str = np.asarray(inputs["""input_ids"""] , dtype=np.intaa)
A_ : int = np.asarray(inputs["""attention_mask"""] , dtype=np.intaa)
A_ : Optional[int] = np.asarray(inputs["""token_type_ids"""] , dtype=np.intaa)
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCamelCase)
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCamelCase)
# start time
A_ : List[Any] = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCamelCase) for d_inp in d_inputs] + [int(lowerCamelCase), int(lowerCamelCase)] , stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
cuda.memcpy_dtoh_async(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Synchronize the stream and take time
stream.synchronize()
# end time
A_ : str = time.time()
A_ : Tuple = end_time - start_time
A_ : Any = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__magic_name__ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__magic_name__ = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
__magic_name__ = raw_datasets['validation'].column_names
__magic_name__ = 'question' if 'question' in column_names else column_names[0]
__magic_name__ = 'context' if 'context' in column_names else column_names[1]
__magic_name__ = 'answers' if 'answers' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
__magic_name__ = tokenizer.padding_side == 'right'
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
)
__magic_name__ = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCamelCase ( lowerCamelCase : Dict):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
A_ : List[Any] = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
A_ : Optional[int] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="""only_second""" if pad_on_right else """only_first""" , max_length=lowerCamelCase , stride=args.doc_stride , return_overflowing_tokens=lowerCamelCase , return_offsets_mapping=lowerCamelCase , padding="""max_length""" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
A_ : List[str] = tokenized_examples.pop("""overflow_to_sample_mapping""")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
A_ : Union[str, Any] = []
for i in range(len(tokenized_examples["""input_ids"""])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
A_ : Any = tokenized_examples.sequence_ids(lowerCamelCase)
A_ : Tuple = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
A_ : Union[str, Any] = sample_mapping[i]
tokenized_examples["example_id"].append(examples["""id"""][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
A_ : Dict = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
]
return tokenized_examples
__magic_name__ = raw_datasets['validation']
# Validation Feature Creation
__magic_name__ = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='Running tokenizer on validation dataset',
)
__magic_name__ = default_data_collator
__magic_name__ = eval_dataset.remove_columns(['example_id', 'offset_mapping'])
__magic_name__ = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any]="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
A_ : Tuple = postprocess_qa_predictions(
examples=lowerCamelCase , features=lowerCamelCase , predictions=lowerCamelCase , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCamelCase , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
A_ : Dict = [
{"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
]
else:
A_ : Union[str, Any] = [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]
A_ : Any = [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCamelCase , label_ids=lowerCamelCase)
__magic_name__ = load_metric('squad_v2' if args.version_2_with_negative else 'squad')
# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize
# Allocate device memory for inputs and outputs.
__magic_name__ = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
__magic_name__ = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
__magic_name__ = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
__magic_name__ = cuda.Stream()
# Evaluation
logger.info('***** Running Evaluation *****')
logger.info(f""" Num examples = {len(eval_dataset)}""")
logger.info(f""" Batch size = {args.per_device_eval_batch_size}""")
__magic_name__ = 0.0
__magic_name__ = 0
__magic_name__ = timeit.default_timer()
__magic_name__ = None
for step, batch in enumerate(eval_dataloader):
__magic_name__ , __magic_name__ = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
__magic_name__ , __magic_name__ = outputs
__magic_name__ = torch.tensor(start_logits)
__magic_name__ = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
__magic_name__ = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
__magic_name__ = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
__magic_name__ = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
__magic_name__ = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
__magic_name__ = nested_truncate(all_preds, len(eval_dataset))
__magic_name__ = timeit.default_timer() - start_time
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1_000 / niter))
logger.info('Total Inference Time = {:.3f} ms'.format(total_time * 1_000))
logger.info('Total Number of Inference = %d', niter)
__magic_name__ = post_processing_function(eval_examples, eval_dataset, all_preds)
__magic_name__ = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")
| 27 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__magic_name__ = ['gpt2']
__magic_name__ = 'gpt2'
if is_tf_available():
class __lowerCAmelCase ( tf.Module ):
'''simple docstring'''
def __init__( self : Any ,_a : List[Any] ):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = tokenizer
A_ : str = AutoConfig.from_pretrained(_a )
A_ : int = TFGPTaLMHeadModel.from_config(_a )
@tf.function(input_signature=(tf.TensorSpec((None,) ,tf.string ,name="""text""" ),) )
def _a ( self : Tuple ,_a : List[str] ):
'''simple docstring'''
A_ : Union[str, Any] = self.tokenizer(_a )
A_ : str = tokenized["""input_ids"""].to_tensor()
A_ : List[Any] = tf.cast(input_ids_dense > 0 ,tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
A_ : int = self.model(input_ids=_a ,attention_mask=_a )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self : Optional[Any] ):
'''simple docstring'''
super().setUp()
A_ : str = [GPTaTokenizer.from_pretrained(_a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
A_ : str = [TFGPTaTokenizer.from_pretrained(_a ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
A_ : Tuple = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
A_ : Any = list(zip(self.test_sentences ,self.test_sentences[::-1] ) )
def _a ( self : Any ):
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers ,self.tf_tokenizers ):
for test_inputs in self.test_sentences:
A_ : List[str] = tokenizer([test_inputs] ,return_tensors="""tf""" )
A_ : Optional[Any] = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
A_ : str = python_outputs[key].numpy()
A_ : List[Any] = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(_a ,tf.intaa ) == tf_outputs_values ) )
@slow
def _a ( self : Union[str, Any] ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
A_ : Any = tf.function(_a )
for test_inputs in self.test_sentences:
A_ : int = tf.constant(_a )
A_ : Any = compiled_tokenizer(_a )
A_ : Tuple = tf_tokenizer(_a )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def _a ( self : Any ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
A_ : Tuple = ModelToSave(tokenizer=_a )
A_ : List[str] = tf.convert_to_tensor([self.test_sentences[0]] )
A_ : Any = model.serving(_a ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
A_ : Dict = Path(_a ) / """saved.model"""
tf.saved_model.save(_a ,_a ,signatures={"""serving_default""": model.serving} )
A_ : Dict = tf.saved_model.load(_a )
A_ : Union[str, Any] = loaded_model.signatures["""serving_default"""](_a )["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def _a ( self : List[Any] ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
A_ : List[Any] = tf.convert_to_tensor([self.test_sentences[0]] )
A_ : Optional[Any] = tf_tokenizer(_a ) # Build model with some sample inputs
A_ : Tuple = tf_tokenizer.get_config()
A_ : Tuple = TFGPTaTokenizer.from_config(_a )
A_ : Optional[int] = model_from_config(_a )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def _a ( self : Any ):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
A_ : Any = 123123
for max_length in [3, 5, 1024]:
A_ : Any = tf.convert_to_tensor([self.test_sentences[0]] )
A_ : Optional[int] = tf_tokenizer(_a ,max_length=_a )
A_ : int = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['ConvNextFeatureExtractor']
__magic_name__ = ['ConvNextImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvNextForImageClassification',
'ConvNextModel',
'ConvNextPreTrainedModel',
'ConvNextBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TFConvNextForImageClassification',
'TFConvNextModel',
'TFConvNextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__magic_name__ = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TapasForMaskedLM',
'TapasForQuestionAnswering',
'TapasForSequenceClassification',
'TapasModel',
'TapasPreTrainedModel',
'load_tf_weights_in_tapas',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFTapasForMaskedLM',
'TFTapasForQuestionAnswering',
'TFTapasForSequenceClassification',
'TFTapasModel',
'TFTapasPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_text_model"""
def __init__( self : Union[str, Any] ,_a : Any=49408 ,_a : Any=512 ,_a : Tuple=2048 ,_a : Dict=12 ,_a : Optional[int]=8 ,_a : Tuple=16 ,_a : Tuple="quick_gelu" ,_a : Optional[Any]=1e-5 ,_a : List[Any]=0.0 ,_a : Optional[int]=0.02 ,_a : Dict=1.0 ,_a : Dict=0 ,_a : Any=49406 ,_a : Tuple=49407 ,**_a : List[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Optional[int] = intermediate_size
A_ : Optional[int] = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : int = max_position_embeddings
A_ : str = hidden_act
A_ : Union[str, Any] = layer_norm_eps
A_ : Tuple = attention_dropout
A_ : Union[str, Any] = initializer_range
A_ : List[Any] = initializer_factor
@classmethod
def _a ( cls : List[str] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : int = cls.get_config_dict(_a ,**_a )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : Union[str, Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit_vision_model"""
def __init__( self : List[Any] ,_a : Optional[Any]=768 ,_a : Tuple=3072 ,_a : Dict=12 ,_a : int=12 ,_a : Dict=3 ,_a : Tuple=768 ,_a : int=32 ,_a : int="quick_gelu" ,_a : List[Any]=1e-5 ,_a : Tuple=0.0 ,_a : List[Any]=0.02 ,_a : str=1.0 ,**_a : int ,):
'''simple docstring'''
super().__init__(**_a )
A_ : List[str] = hidden_size
A_ : Union[str, Any] = intermediate_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Optional[Any] = num_attention_heads
A_ : int = num_channels
A_ : str = image_size
A_ : List[Any] = patch_size
A_ : int = hidden_act
A_ : List[Any] = layer_norm_eps
A_ : List[str] = attention_dropout
A_ : str = initializer_range
A_ : str = initializer_factor
@classmethod
def _a ( cls : List[Any] ,_a : Union[str, os.PathLike] ,**_a : str ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : Optional[int] = cls.get_config_dict(_a ,**_a )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
A_ : List[str] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """owlvit"""
a_ = True
def __init__( self : Union[str, Any] ,_a : List[str]=None ,_a : List[str]=None ,_a : Dict=512 ,_a : List[Any]=2.6592 ,_a : Optional[Any]=True ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(**_a )
if text_config is None:
A_ : List[Any] = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
A_ : Tuple = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
A_ : Dict = OwlViTTextConfig(**_a )
A_ : Dict = OwlViTVisionConfig(**_a )
A_ : Any = projection_dim
A_ : Optional[int] = logit_scale_init_value
A_ : Optional[int] = return_dict
A_ : Dict = 1.0
@classmethod
def _a ( cls : Union[str, Any] ,_a : Union[str, os.PathLike] ,**_a : Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_a )
A_ , A_ : List[Any] = cls.get_config_dict(_a ,**_a )
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(_a ,**_a )
@classmethod
def _a ( cls : int ,_a : Dict ,_a : Dict ,**_a : List[str] ):
'''simple docstring'''
A_ : str = {}
A_ : int = text_config
A_ : Union[str, Any] = vision_config
return cls.from_dict(_a ,**_a )
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = copy.deepcopy(self.__dict__ )
A_ : str = self.text_config.to_dict()
A_ : Optional[int] = self.vision_config.to_dict()
A_ : List[Any] = self.__class__.model_type
return output
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : int ):
'''simple docstring'''
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def _a ( self : str ):
'''simple docstring'''
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
def _a ( self : int ,_a : "ProcessorMixin" ,_a : int = -1 ,_a : int = -1 ,_a : Optional["TensorType"] = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(
processor.tokenizer ,batch_size=_a ,seq_length=_a ,framework=_a )
A_ : Any = super().generate_dummy_inputs(
processor.image_processor ,batch_size=_a ,framework=_a )
return {**text_input_dict, **image_input_dict}
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 14
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
__magic_name__ = list[list[float | int]]
def lowerCamelCase ( lowerCamelCase : Matrix , lowerCamelCase : Matrix):
A_ : int = len(lowerCamelCase)
A_ : Matrix = [[0 for _ in range(size + 1)] for _ in range(lowerCamelCase)]
A_ : int
A_ : int
A_ : int
A_ : int
A_ : int
A_ : float
for row in range(lowerCamelCase):
for col in range(lowerCamelCase):
A_ : List[Any] = matrix[row][col]
A_ : Union[str, Any] = vector[row][0]
A_ : str = 0
A_ : Dict = 0
while row < size and col < size:
# pivoting
A_ : Tuple = max((abs(augmented[rowa][col]), rowa) for rowa in range(lowerCamelCase , lowerCamelCase))[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
A_ , A_ : List[str] = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , lowerCamelCase):
A_ : List[str] = augmented[rowa][col] / augmented[row][col]
A_ : str = 0
for cola in range(col + 1 , size + 1):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , lowerCamelCase):
for row in range(lowerCamelCase):
A_ : Union[str, Any] = augmented[row][col] / augmented[col][col]
for cola in range(lowerCamelCase , size + 1):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10)] for row in range(lowerCamelCase)
]
def lowerCamelCase ( lowerCamelCase : list[int]):
A_ : int = len(lowerCamelCase)
A_ : Matrix = [[0 for _ in range(lowerCamelCase)] for _ in range(lowerCamelCase)]
A_ : Matrix = [[0] for _ in range(lowerCamelCase)]
A_ : Matrix
A_ : int
A_ : int
A_ : int
for x_val, y_val in enumerate(lowerCamelCase):
for col in range(lowerCamelCase):
A_ : str = (x_val + 1) ** (size - col - 1)
A_ : Tuple = y_val
A_ : Tuple = solve(lowerCamelCase , lowerCamelCase)
def interpolated_func(lowerCamelCase : int) -> int:
return sum(
round(coeffs[x_val][0]) * (var ** (size - x_val - 1))
for x_val in range(lowerCamelCase))
return interpolated_func
def lowerCamelCase ( lowerCamelCase : int):
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def lowerCamelCase ( lowerCamelCase : Callable[[int], int] = question_function , lowerCamelCase : int = 10):
A_ : list[int] = [func(lowerCamelCase) for x_val in range(1 , order + 1)]
A_ : list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff]) for max_coeff in range(1 , order + 1)
]
A_ : int = 0
A_ : Callable[[int], int]
A_ : int
for poly in polynomials:
A_ : int = 1
while func(lowerCamelCase) == poly(lowerCamelCase):
x_val += 1
ret += poly(lowerCamelCase)
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""input_features""", """is_longer"""]
def __init__( self : Dict ,_a : Optional[int]=64 ,_a : List[Any]=48000 ,_a : str=480 ,_a : Optional[Any]=10 ,_a : Optional[int]=1024 ,_a : Tuple=0.0 ,_a : str=False ,_a : float = 0 ,_a : float = 14000 ,_a : int = None ,_a : str = "fusion" ,_a : str = "repeatpad" ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(
feature_size=_a ,sampling_rate=_a ,padding_value=_a ,return_attention_mask=_a ,**_a ,)
A_ : Tuple = top_db
A_ : Tuple = truncation
A_ : Optional[Any] = padding
A_ : Optional[int] = fft_window_size
A_ : Dict = (fft_window_size >> 1) + 1
A_ : Any = hop_length
A_ : List[Any] = max_length_s
A_ : Tuple = max_length_s * sampling_rate
A_ : Tuple = sampling_rate
A_ : Optional[int] = frequency_min
A_ : Tuple = frequency_max
A_ : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm=_a ,mel_scale="""htk""" ,)
A_ : Dict = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def _a ( self : int ):
'''simple docstring'''
A_ : int = copy.deepcopy(self.__dict__ )
A_ : Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _a ( self : Dict ,_a : np.array ,_a : Optional[np.array] = None ):
'''simple docstring'''
A_ : List[str] = spectrogram(
_a ,window_function(self.fft_window_size ,"""hann""" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=_a ,log_mel="""dB""" ,)
return log_mel_spectrogram.T
def _a ( self : Optional[int] ,_a : Dict ,_a : Optional[Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Dict = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : int = [0]
# randomly choose index for each part
A_ : List[str] = np.random.choice(ranges[0] )
A_ : int = np.random.choice(ranges[1] )
A_ : Optional[int] = np.random.choice(ranges[2] )
A_ : Tuple = mel[idx_front : idx_front + chunk_frames, :]
A_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
A_ : Dict = mel[idx_back : idx_back + chunk_frames, :]
A_ : Optional[int] = torch.tensor(mel[None, None, :] )
A_ : Dict = torch.nn.functional.interpolate(
_a ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=_a )
A_ : str = mel_shrink[0][0].numpy()
A_ : Tuple = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def _a ( self : Dict ,_a : np.array ,_a : Optional[Any] ,_a : int ,_a : Dict ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
A_ : Dict = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
A_ : Tuple = len(_a ) - max_length
A_ : Optional[int] = np.random.randint(0 ,overflow + 1 )
A_ : List[Any] = waveform[idx : idx + max_length]
A_ : Optional[Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
A_ : Dict = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Tuple = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
A_ : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
A_ : Optional[int] = np.stack([mel, mel, mel, mel] ,axis=0 )
A_ : str = False
else:
A_ : str = self._random_mel_fusion(_a ,_a ,_a )
A_ : Optional[Any] = True
else:
raise NotImplementedError(f'data_truncating {truncation} not implemented' )
else:
A_ : Optional[int] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
A_ : int = int(max_length / len(_a ) )
A_ : Any = np.stack(np.tile(_a ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
A_ : List[str] = int(max_length / len(_a ) )
A_ : Optional[Any] = np.stack(np.tile(_a ,_a ) )
A_ : Any = np.pad(_a ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
A_ : List[Any] = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
A_ : Union[str, Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[Any] ,_a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_a : str = None ,_a : Optional[str] = None ,_a : Optional[int] = None ,_a : Optional[int] = None ,_a : Optional[Union[str, TensorType]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : List[str] = truncation if truncation is not None else self.truncation
A_ : List[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
A_ : Any = isinstance(_a ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A_ : int = is_batched_numpy or (
isinstance(_a ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
A_ : Optional[int] = [np.asarray(_a ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_a ,np.ndarray ):
A_ : str = np.asarray(_a ,dtype=np.floataa )
elif isinstance(_a ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A_ : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ : Any = [np.asarray(_a )]
# convert to mel spectrogram, truncate and pad if needed.
A_ : str = [
self._get_input_mel(_a ,max_length if max_length else self.nb_max_samples ,_a ,_a )
for waveform in raw_speech
]
A_ : int = []
A_ : Any = []
for mel, longer in padded_inputs:
input_mel.append(_a )
is_longer.append(_a )
if truncation == "fusion" and sum(_a ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
A_ : List[Any] = np.random.randint(0 ,len(_a ) )
A_ : List[str] = True
if isinstance(input_mel[0] ,_a ):
A_ : Tuple = [np.asarray(_a ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
A_ : List[str] = [[longer] for longer in is_longer]
A_ : Optional[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
A_ : int = BatchFeature(_a )
if return_tensors is not None:
A_ : int = input_features.convert_to_tensors(_a )
return input_features
| 27 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}
# fmt: off
__magic_name__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__magic_name__ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627,
3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """whisper"""
a_ = ["""past_key_values"""]
a_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : str ,_a : List[str]=51865 ,_a : Optional[Any]=80 ,_a : List[Any]=6 ,_a : int=4 ,_a : Optional[int]=6 ,_a : int=4 ,_a : Optional[Any]=1536 ,_a : Union[str, Any]=1536 ,_a : Union[str, Any]=0.0 ,_a : List[Any]=0.0 ,_a : Tuple=50257 ,_a : Optional[int]=True ,_a : Dict=True ,_a : int="gelu" ,_a : Dict=256 ,_a : Optional[Any]=0.0 ,_a : Optional[Any]=0.0 ,_a : Union[str, Any]=0.0 ,_a : Optional[Any]=0.02 ,_a : Dict=False ,_a : Tuple=1500 ,_a : Dict=448 ,_a : Tuple=50256 ,_a : Tuple=50256 ,_a : Optional[int]=50256 ,_a : Optional[int]=None ,_a : Tuple=[220, 50256] ,_a : Dict=False ,_a : List[str]=256 ,_a : Optional[int]=False ,_a : int=0.05 ,_a : Any=10 ,_a : int=2 ,_a : int=0.0 ,_a : Union[str, Any]=10 ,_a : Optional[int]=0 ,_a : int=7 ,**_a : Optional[int] ,):
'''simple docstring'''
A_ : Union[str, Any] = vocab_size
A_ : List[str] = num_mel_bins
A_ : Tuple = d_model
A_ : List[str] = encoder_layers
A_ : List[Any] = encoder_attention_heads
A_ : Dict = decoder_layers
A_ : Any = decoder_attention_heads
A_ : Union[str, Any] = decoder_ffn_dim
A_ : Optional[Any] = encoder_ffn_dim
A_ : Union[str, Any] = dropout
A_ : Tuple = attention_dropout
A_ : Tuple = activation_dropout
A_ : str = activation_function
A_ : Union[str, Any] = init_std
A_ : Optional[Any] = encoder_layerdrop
A_ : List[Any] = decoder_layerdrop
A_ : Tuple = use_cache
A_ : Optional[Any] = encoder_layers
A_ : List[str] = scale_embedding # scale factor will be sqrt(d_model) if True
A_ : Optional[int] = max_source_positions
A_ : str = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
A_ : Tuple = classifier_proj_size
A_ : Tuple = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
A_ : Any = apply_spec_augment
A_ : Optional[int] = mask_time_prob
A_ : Optional[int] = mask_time_length
A_ : Optional[int] = mask_time_min_masks
A_ : Optional[Any] = mask_feature_prob
A_ : Optional[Any] = mask_feature_length
A_ : Any = mask_feature_min_masks
A_ : List[str] = median_filter_width
super().__init__(
pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,is_encoder_decoder=_a ,decoder_start_token_id=_a ,suppress_tokens=_a ,begin_suppress_tokens=_a ,**_a ,)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
A_ : Optional[int] = {0: """batch"""}
else:
A_ : Optional[Any] = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a ,direction="""inputs""" )
return common_inputs
def _a ( self : Dict ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 22050 ,_a : float = 5.0 ,_a : int = 220 ,):
'''simple docstring'''
A_ : int = OrderedDict()
A_ : Dict = OnnxConfig.generate_dummy_inputs(
self ,preprocessor=preprocessor.feature_extractor ,batch_size=_a ,framework=_a ,sampling_rate=_a ,time_duration=_a ,frequency=_a ,)
A_ : int = encoder_inputs["""input_features"""].shape[2]
A_ : Union[str, Any] = encoder_sequence_length // 2 if self.use_past else seq_length
A_ : int = super().generate_dummy_inputs(
preprocessor.tokenizer ,_a ,_a ,_a ,_a )
A_ : Optional[Any] = encoder_inputs.pop("""input_features""" )
A_ : Dict = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
A_ : List[Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _a ( self : Any ):
'''simple docstring'''
return 1e-3
| 27 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,_a : List[Any] ,_a : Dict=13 ,_a : List[Any]=7 ,_a : Optional[Any]=True ,_a : Any=True ,_a : Optional[int]=True ,_a : Union[str, Any]=99 ,_a : Union[str, Any]=32 ,_a : List[str]=5 ,_a : List[str]=4 ,_a : Dict=37 ,_a : List[Any]="gelu" ,_a : int=0.1 ,_a : Optional[int]=0.1 ,_a : Tuple=512 ,_a : Union[str, Any]=16 ,_a : Optional[Any]=2 ,_a : Optional[Any]=0.02 ,_a : Optional[int]=3 ,_a : str=4 ,_a : Optional[Any]=None ,):
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : str = batch_size
A_ : int = seq_length
A_ : Union[str, Any] = is_training
A_ : Optional[Any] = use_token_type_ids
A_ : int = use_labels
A_ : Dict = vocab_size
A_ : List[Any] = hidden_size
A_ : Tuple = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : int = intermediate_size
A_ : Tuple = hidden_act
A_ : int = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : Any = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : Tuple = type_sequence_label_size
A_ : int = initializer_range
A_ : Optional[Any] = num_labels
A_ : str = num_choices
A_ : Optional[Any] = scope
A_ : List[Any] = self.vocab_size - 1
def _a ( self : Any ):
'''simple docstring'''
A_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
A_ : List[Any] = None
if self.use_token_type_ids:
A_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
A_ : int = None
A_ : str = None
A_ : Union[str, Any] = None
if self.use_labels:
A_ : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
A_ : Any = ids_tensor([self.batch_size] ,self.num_choices )
A_ : List[Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,pad_token_id=self.pad_token_id ,)
A_ : Tuple = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _a ( self : Optional[int] ,_a : List[str] ,_a : str ,_a : int ,_a : int ,*_a : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[Any] = OpenAIGPTModel(config=_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,head_mask=_a )
A_ : str = model(_a ,token_type_ids=_a )
A_ : Dict = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : Dict ,_a : Optional[int] ,_a : Union[str, Any] ,_a : Dict ,_a : List[str] ,*_a : str ):
'''simple docstring'''
A_ : str = OpenAIGPTLMHeadModel(_a )
model.to(_a )
model.eval()
A_ : Any = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : Any ,_a : Dict ,_a : List[Any] ,_a : Dict ,_a : Union[str, Any] ,*_a : str ):
'''simple docstring'''
A_ : Any = OpenAIGPTDoubleHeadsModel(_a )
model.to(_a )
model.eval()
A_ : Optional[int] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self : List[str] ,_a : str ,_a : Tuple ,_a : Dict ,_a : Tuple ,*_a : Dict ):
'''simple docstring'''
A_ : List[str] = self.num_labels
A_ : int = OpenAIGPTForSequenceClassification(_a )
model.to(_a )
model.eval()
A_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
A_ : Optional[Any] = model(_a ,token_type_ids=_a ,labels=_a )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : str = config_and_inputs
A_ : int = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
a_ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
a_ = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _a ( self : Tuple ,_a : Optional[int] ,_a : str ,_a : List[str] ,_a : List[str] ,_a : Any ):
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _a ( self : Optional[int] ,_a : str ,_a : Dict ,_a : Optional[int]=False ):
'''simple docstring'''
A_ : Any = super()._prepare_for_class(_a ,_a ,return_labels=_a )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
A_ : Union[str, Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,dtype=torch.long ,device=_a ,)
A_ : Any = inputs_dict["""labels"""]
A_ : Any = inputs_dict["""labels"""]
A_ : Tuple = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) ,dtype=torch.long ,device=_a ,)
A_ : int = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=_a )
return inputs_dict
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Tuple = OpenAIGPTModelTester(self )
A_ : Optional[int] = ConfigTester(self ,config_class=_a ,n_embd=37 )
def _a ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_a )
def _a ( self : Tuple ):
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_a )
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_a )
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_a )
@slow
def _a ( self : List[Any] ):
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = OpenAIGPTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Dict = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(_a )
A_ : Dict = torch.tensor([[481, 4735, 544]] ,dtype=torch.long ,device=_a ) # the president is
A_ : Dict = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
A_ : int = model.generate(_a ,do_sample=_a )
self.assertListEqual(output_ids[0].tolist() ,_a )
| 27 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""input_features""", """is_longer"""]
def __init__( self : Dict ,_a : Optional[int]=64 ,_a : List[Any]=48000 ,_a : str=480 ,_a : Optional[Any]=10 ,_a : Optional[int]=1024 ,_a : Tuple=0.0 ,_a : str=False ,_a : float = 0 ,_a : float = 14000 ,_a : int = None ,_a : str = "fusion" ,_a : str = "repeatpad" ,**_a : Tuple ,):
'''simple docstring'''
super().__init__(
feature_size=_a ,sampling_rate=_a ,padding_value=_a ,return_attention_mask=_a ,**_a ,)
A_ : Tuple = top_db
A_ : Tuple = truncation
A_ : Optional[Any] = padding
A_ : Optional[int] = fft_window_size
A_ : Dict = (fft_window_size >> 1) + 1
A_ : Any = hop_length
A_ : List[Any] = max_length_s
A_ : Tuple = max_length_s * sampling_rate
A_ : Tuple = sampling_rate
A_ : Optional[int] = frequency_min
A_ : Tuple = frequency_max
A_ : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm=_a ,mel_scale="""htk""" ,)
A_ : Dict = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=_a ,min_frequency=_a ,max_frequency=_a ,sampling_rate=_a ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def _a ( self : int ):
'''simple docstring'''
A_ : int = copy.deepcopy(self.__dict__ )
A_ : Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _a ( self : Dict ,_a : np.array ,_a : Optional[np.array] = None ):
'''simple docstring'''
A_ : List[str] = spectrogram(
_a ,window_function(self.fft_window_size ,"""hann""" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=_a ,log_mel="""dB""" ,)
return log_mel_spectrogram.T
def _a ( self : Optional[int] ,_a : Dict ,_a : Optional[Any] ,_a : Optional[int] ):
'''simple docstring'''
A_ : Dict = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : List[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
A_ : int = [0]
# randomly choose index for each part
A_ : List[str] = np.random.choice(ranges[0] )
A_ : int = np.random.choice(ranges[1] )
A_ : Optional[int] = np.random.choice(ranges[2] )
A_ : Tuple = mel[idx_front : idx_front + chunk_frames, :]
A_ : Dict = mel[idx_middle : idx_middle + chunk_frames, :]
A_ : Dict = mel[idx_back : idx_back + chunk_frames, :]
A_ : Optional[int] = torch.tensor(mel[None, None, :] )
A_ : Dict = torch.nn.functional.interpolate(
_a ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=_a )
A_ : str = mel_shrink[0][0].numpy()
A_ : Tuple = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def _a ( self : Dict ,_a : np.array ,_a : Optional[Any] ,_a : int ,_a : Dict ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
A_ : Dict = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
A_ : Tuple = len(_a ) - max_length
A_ : Optional[int] = np.random.randint(0 ,overflow + 1 )
A_ : List[Any] = waveform[idx : idx + max_length]
A_ : Optional[Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
A_ : Dict = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Tuple = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
A_ : Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
A_ : Optional[int] = np.stack([mel, mel, mel, mel] ,axis=0 )
A_ : str = False
else:
A_ : str = self._random_mel_fusion(_a ,_a ,_a )
A_ : Optional[Any] = True
else:
raise NotImplementedError(f'data_truncating {truncation} not implemented' )
else:
A_ : Optional[int] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
A_ : int = int(max_length / len(_a ) )
A_ : Any = np.stack(np.tile(_a ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
A_ : List[str] = int(max_length / len(_a ) )
A_ : Optional[Any] = np.stack(np.tile(_a ,_a ) )
A_ : Any = np.pad(_a ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
A_ : List[Any] = self._np_extract_fbank_features(_a ,self.mel_filters )
A_ : Optional[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
A_ : Union[str, Any] = self._np_extract_fbank_features(_a ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : List[Any] ,_a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,_a : str = None ,_a : Optional[str] = None ,_a : Optional[int] = None ,_a : Optional[int] = None ,_a : Optional[Union[str, TensorType]] = None ,**_a : Any ,):
'''simple docstring'''
A_ : List[str] = truncation if truncation is not None else self.truncation
A_ : List[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
A_ : Any = isinstance(_a ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
A_ : int = is_batched_numpy or (
isinstance(_a ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
A_ : Optional[int] = [np.asarray(_a ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_a ,np.ndarray ):
A_ : str = np.asarray(_a ,dtype=np.floataa )
elif isinstance(_a ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A_ : Tuple = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A_ : Any = [np.asarray(_a )]
# convert to mel spectrogram, truncate and pad if needed.
A_ : str = [
self._get_input_mel(_a ,max_length if max_length else self.nb_max_samples ,_a ,_a )
for waveform in raw_speech
]
A_ : int = []
A_ : Any = []
for mel, longer in padded_inputs:
input_mel.append(_a )
is_longer.append(_a )
if truncation == "fusion" and sum(_a ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
A_ : List[Any] = np.random.randint(0 ,len(_a ) )
A_ : List[str] = True
if isinstance(input_mel[0] ,_a ):
A_ : Tuple = [np.asarray(_a ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
A_ : List[str] = [[longer] for longer in is_longer]
A_ : Optional[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
A_ : int = BatchFeature(_a )
if return_tensors is not None:
A_ : int = input_features.convert_to_tensors(_a )
return input_features
| 27 |
'''simple docstring'''
import baseaa
def lowerCamelCase ( lowerCamelCase : str):
return baseaa.aaaencode(string.encode("""utf-8"""))
def lowerCamelCase ( lowerCamelCase : bytes):
return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """ViltImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] ,_a : Optional[Any]=None ,_a : List[str]=None ,**_a : Any ):
'''simple docstring'''
A_ : 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.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[Any] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Any ,_a : Tuple ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
# add pixel_values + pixel_mask
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Any ,**_a : Any ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : int ,*_a : int ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : int ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase ( lowerCamelCase : Optional[Any]):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def lowerCamelCase ( lowerCamelCase : str):
# word like '180' or '身高' or '神'
for char in word:
A_ : Optional[Any] = ord(lowerCamelCase)
if not _is_chinese_char(lowerCamelCase):
return 0
return 1
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : Any = set()
for token in tokens:
A_ : str = len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
if chinese_word:
word_set.add(lowerCamelCase)
A_ : Any = list(lowerCamelCase)
return word_list
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : set()):
if not chinese_word_set:
return bert_tokens
A_ : Any = max([len(lowerCamelCase) for w in chinese_word_set])
A_ : str = bert_tokens
A_ , A_ : Any = 0, len(lowerCamelCase)
while start < end:
A_ : Tuple = True
if is_chinese(bert_word[start]):
A_ : List[str] = min(end - start , lowerCamelCase)
for i in range(lowerCamelCase , 1 , -1):
A_ : Tuple = """""".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
A_ : Dict = """##""" + bert_word[j]
A_ : str = start + i
A_ : Dict = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : LTP , lowerCamelCase : BertTokenizer):
A_ : Union[str, Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : List[Any] = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""]).cws
A_ : int = [get_chinese_word(lowerCamelCase) for r in res]
ltp_res.extend(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : List[Any] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : Dict = bert_tokenizer(lines[i : i + 100] , add_special_tokens=lowerCamelCase , truncation=lowerCamelCase , max_length=512)
bert_res.extend(res["""input_ids"""])
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Union[str, Any] = []
for input_ids, chinese_word in zip(lowerCamelCase , lowerCamelCase):
A_ : List[Any] = []
for id in input_ids:
A_ : List[Any] = bert_tokenizer._convert_id_to_token(lowerCamelCase)
input_tokens.append(lowerCamelCase)
A_ : int = add_sub_symbol(lowerCamelCase , lowerCamelCase)
A_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCamelCase):
if token[:2] == "##":
A_ : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
ref_id.append(lowerCamelCase)
ref_ids.append(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
return ref_ids
def lowerCamelCase ( lowerCamelCase : Tuple):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""") as f:
A_ : Optional[int] = f.readlines()
A_ : Union[str, Any] = [line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ : Optional[Any] = LTP(args.ltp) # faster in GPU device
A_ : Dict = BertTokenizer.from_pretrained(args.bert)
A_ : str = prepare_ref(lowerCamelCase , lowerCamelCase , lowerCamelCase)
with open(args.save_path , """w""" , encoding="""utf-8""") as f:
A_ : Optional[Any] = [json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
f.writelines(lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = 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',
)
__magic_name__ = parser.parse_args()
main(args)
| 27 | 1 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowerCamelCase ( lowerCamelCase : dict):
return (data["data"], data["target"])
def lowerCamelCase ( lowerCamelCase : np.ndarray , lowerCamelCase : np.ndarray , lowerCamelCase : np.ndarray):
A_ : int = XGBRegressor(verbosity=0 , random_state=42)
xgb.fit(lowerCamelCase , lowerCamelCase)
# Predict target for test data
A_ : Union[str, Any] = xgb.predict(lowerCamelCase)
A_ : int = predictions.reshape(len(lowerCamelCase) , 1)
return predictions
def lowerCamelCase ( ):
A_ : Union[str, Any] = fetch_california_housing()
A_ , A_ : List[str] = data_handling(lowerCamelCase)
A_ , A_ , A_ , A_ : List[Any] = train_test_split(
lowerCamelCase , lowerCamelCase , test_size=0.25 , random_state=1)
A_ : Any = xgboost(lowerCamelCase , lowerCamelCase , lowerCamelCase)
# Error printing
print(F'Mean Absolute Error : {mean_absolute_error(lowerCamelCase , lowerCamelCase)}')
print(F'Mean Square Error : {mean_squared_error(lowerCamelCase , lowerCamelCase)}')
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 27 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """ViltImageProcessor"""
a_ = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] ,_a : Optional[Any]=None ,_a : List[str]=None ,**_a : Any ):
'''simple docstring'''
A_ : 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.""" ,_a ,)
A_ : List[str] = kwargs.pop("""feature_extractor""" )
A_ : List[Any] = 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__(_a ,_a )
A_ : Optional[Any] = self.image_processor
def __call__( self : Any ,_a : Tuple ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Tuple ,):
'''simple docstring'''
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_token_type_ids=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
# add pixel_values + pixel_mask
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Any ,**_a : Any ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : int ,*_a : int ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = self.tokenizer.model_input_names
A_ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self : str ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,_a ,)
return self.image_processor_class
@property
def _a ( self : int ):
'''simple docstring'''
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,_a ,)
return self.image_processor
| 27 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
__magic_name__ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
a_ = None
def lowerCamelCase ( lowerCamelCase : "pyspark.sql.DataFrame" , lowerCamelCase : List[int] , ):
import pyspark
def generate_fn():
A_ : Tuple = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id"""))
for partition_id in partition_order:
A_ : List[str] = df_with_partition_id.select("""*""").where(F'part_id = {partition_id}').drop("""part_id""")
A_ : List[Any] = partition_df.collect()
A_ : List[Any] = 0
for row in rows:
yield F'{partition_id}_{row_id}', row.asDict()
row_id += 1
return generate_fn
class __lowerCAmelCase ( _BaseExamplesIterable ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_a : "pyspark.sql.DataFrame" ,_a : Union[str, Any]=None ,):
'''simple docstring'''
A_ : Optional[int] = df
A_ : Any = partition_order or range(self.df.rdd.getNumPartitions() )
A_ : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self : Optional[Any] ):
'''simple docstring'''
yield from self.generate_examples_fn()
def _a ( self : Optional[int] ,_a : np.random.Generator ):
'''simple docstring'''
A_ : Union[str, Any] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(_a )
return SparkExamplesIterable(self.df ,partition_order=_a )
def _a ( self : Optional[Any] ,_a : int ,_a : int ):
'''simple docstring'''
A_ : Optional[Any] = self.split_shard_indices_by_worker(_a ,_a )
return SparkExamplesIterable(self.df ,partition_order=_a )
@property
def _a ( self : Tuple ):
'''simple docstring'''
return len(self.partition_order )
class __lowerCAmelCase ( datasets.DatasetBuilder ):
'''simple docstring'''
a_ = SparkConfig
def __init__( self : Any ,_a : "pyspark.sql.DataFrame" ,_a : str = None ,_a : str = None ,**_a : Optional[int] ,):
'''simple docstring'''
import pyspark
A_ : int = pyspark.sql.SparkSession.builder.getOrCreate()
A_ : Optional[int] = df
A_ : List[Any] = working_dir
super().__init__(
cache_dir=_a ,config_name=str(self.df.semanticHash() ) ,**_a ,)
def _a ( self : int ):
'''simple docstring'''
def create_cache_and_write_probe(_a : Any ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=_a )
A_ : Optional[Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(_a ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
A_ : Any = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(_a ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def _a ( self : Dict ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def _a ( self : List[Any] ,_a : datasets.download.download_manager.DownloadManager ):
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def _a ( self : List[Any] ,_a : List[Any] ):
'''simple docstring'''
import pyspark
def get_arrow_batch_size(_a : int ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
A_ : int = self.df.count()
A_ : str = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
A_ : Optional[Any] = (
self.df.limit(_a )
.repartition(1 )
.mapInArrow(_a ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
A_ : Optional[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
A_ : List[str] = min(_a ,int(approx_total_size / max_shard_size ) )
A_ : Optional[Any] = self.df.repartition(_a )
def _a ( self : int ,_a : str ,_a : str ,_a : int ,):
'''simple docstring'''
import pyspark
A_ : List[Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
A_ : Optional[Any] = os.path.join(self._working_dir ,os.path.basename(_a ) ) if self._working_dir else fpath
A_ : int = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
A_ : Dict = self.config.features
A_ : Optional[Any] = self._writer_batch_size
A_ : Any = self._fs.storage_options
def write_arrow(_a : Union[str, Any] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
A_ : str = pyspark.TaskContext().taskAttemptId()
A_ : Tuple = next(_a ,_a )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
A_ : Optional[int] = 0
A_ : Any = writer_class(
features=_a ,path=working_fpath.replace("""SSSSS""" ,f'{shard_id:05d}' ).replace("""TTTTT""" ,f'{task_id:05d}' ) ,writer_batch_size=_a ,storage_options=_a ,embed_local_files=_a ,)
A_ : Union[str, Any] = pa.Table.from_batches([first_batch] )
writer.write_table(_a )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
A_ , A_ : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
A_ : Dict = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f'{shard_id:05d}' ).replace("""TTTTT""" ,f'{task_id:05d}' ) ,writer_batch_size=_a ,storage_options=_a ,embed_local_files=_a ,)
A_ : List[str] = pa.Table.from_batches([batch] )
writer.write_table(_a )
if writer._num_bytes > 0:
A_ , A_ : Optional[int] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(_a ) ):
A_ : List[str] = os.path.join(os.path.dirname(_a ) ,os.path.basename(_a ) )
shutil.move(_a ,_a )
A_ : Dict = (
self.df.mapInArrow(_a ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def _a ( self : Union[str, Any] ,_a : "datasets.SplitGenerator" ,_a : str = "arrow" ,_a : Optional[Union[str, int]] = None ,_a : Optional[int] = None ,**_a : int ,):
'''simple docstring'''
self._validate_cache_dir()
A_ : Any = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(_a )
A_ : List[str] = not is_remote_filesystem(self._fs )
A_ : List[str] = os.path.join if is_local else posixpath.join
A_ : List[str] = """-TTTTT-SSSSS-of-NNNNN"""
A_ : List[str] = f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
A_ : Tuple = path_join(self._output_dir ,_a )
A_ : Dict = 0
A_ : Optional[Any] = 0
A_ : Optional[Any] = 0
A_ : Any = []
A_ : str = []
for task_id, content in self._prepare_split_single(_a ,_a ,_a ):
(
(
A_
) , (
A_
) , (
A_
) , (
A_
) ,
) : Optional[Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(_a )
A_ : Any = total_num_examples
A_ : Any = total_num_bytes
# should rename everything at the end
logger.debug(f'Renaming {total_shards} shards.' )
if total_shards > 1:
A_ : str = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
A_ : Optional[int] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
_a : int ,_a : int ,_a : int ,):
rename(
_a ,fpath.replace("""SSSSS""" ,f'{shard_id:05d}' ).replace("""TTTTT""" ,f'{task_id:05d}' ) ,fpath.replace("""TTTTT-SSSSS""" ,f'{global_shard_id:05d}' ).replace("""NNNNN""" ,f'{total_shards:05d}' ) ,)
A_ : int = []
A_ : Union[str, Any] = 0
for i in range(len(_a ) ):
A_ , A_ : Any = task_id_and_num_shards[i]
for shard_id in range(_a ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(_a ,len(_a ) ).map(lambda _a : _rename_shard(*_a ) ).collect()
else:
# don't use any pattern
A_ : str = 0
A_ : Union[str, Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f'{shard_id:05d}' ).replace("""TTTTT""" ,f'{task_id:05d}' ) ,fpath.replace(_a ,"""""" ) ,)
def _a ( self : Optional[int] ,_a : "datasets.SplitGenerator" ,):
'''simple docstring'''
return SparkExamplesIterable(self.df )
| 27 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""torch""", """torchsde"""]
def __init__( self : Any ,*_a : Union[str, Any] ,**_a : Optional[int] ):
'''simple docstring'''
requires_backends(self ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : Optional[int] ,*_a : List[Any] ,**_a : Any ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
@classmethod
def _a ( cls : List[Any] ,*_a : Tuple ,**_a : Union[str, Any] ):
'''simple docstring'''
requires_backends(cls ,["""torch""", """torchsde"""] )
| 27 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""image_processor""", """tokenizer"""]
a_ = """BlipImageProcessor"""
a_ = """AutoTokenizer"""
def __init__( self : Optional[Any] ,_a : List[Any] ,_a : Union[str, Any] ,_a : Any ):
'''simple docstring'''
super().__init__(_a ,_a )
# add QFormer tokenizer
A_ : Optional[int] = qformer_tokenizer
def __call__( self : str ,_a : ImageInput = None ,_a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,_a : bool = True ,_a : Union[bool, str, PaddingStrategy] = False ,_a : Union[bool, str, TruncationStrategy] = None ,_a : Optional[int] = None ,_a : int = 0 ,_a : Optional[int] = None ,_a : Optional[bool] = None ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = False ,_a : bool = True ,_a : Optional[Union[str, TensorType]] = None ,**_a : Optional[int] ,):
'''simple docstring'''
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
A_ : Any = BatchFeature()
if text is not None:
A_ : int = self.tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_token_type_ids=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
encoding.update(_a )
A_ : List[Any] = self.qformer_tokenizer(
text=_a ,add_special_tokens=_a ,padding=_a ,truncation=_a ,max_length=_a ,stride=_a ,pad_to_multiple_of=_a ,return_attention_mask=_a ,return_overflowing_tokens=_a ,return_special_tokens_mask=_a ,return_offsets_mapping=_a ,return_token_type_ids=_a ,return_length=_a ,verbose=_a ,return_tensors=_a ,**_a ,)
A_ : List[Any] = qformer_text_encoding.pop("""input_ids""" )
A_ : Union[str, Any] = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
A_ : Optional[int] = self.image_processor(_a ,return_tensors=_a )
encoding.update(_a )
return encoding
def _a ( self : List[Any] ,*_a : Dict ,**_a : Union[str, Any] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_a ,**_a )
def _a ( self : Dict ,*_a : List[Any] ,**_a : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*_a ,**_a )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : int = self.tokenizer.model_input_names
A_ : List[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _a ( self : Any ,_a : Tuple ,**_a : Union[str, Any] ):
'''simple docstring'''
if os.path.isfile(_a ):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(_a ,exist_ok=_a )
A_ : int = os.path.join(_a ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(_a )
return super().save_pretrained(_a ,**_a )
@classmethod
def _a ( cls : Dict ,_a : Dict ,**_a : int ):
'''simple docstring'''
A_ : Tuple = AutoTokenizer.from_pretrained(_a ,subfolder="""qformer_tokenizer""" )
A_ : List[Any] = cls._get_arguments_from_pretrained(_a ,**_a )
args.append(_a )
return cls(*_a )
| 27 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]="pt"):
A_ : Optional[int] = {"""add_prefix_space""": True} if isinstance(lowerCamelCase , lowerCamelCase) and not line.startswith(""" """) else {}
A_ : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowerCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=lowerCamelCase , return_tensors=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
def lowerCamelCase ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[Any]=None , ):
A_ : Dict = input_ids.ne(lowerCamelCase).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,_a : Optional[Any] ,_a : Tuple ,_a : Dict ,_a : Tuple ,_a : Tuple="train" ,_a : Optional[int]=None ,_a : Any=None ,_a : int=None ,_a : Union[str, Any]="" ,):
'''simple docstring'''
super().__init__()
A_ : Union[str, Any] = Path(_a ).joinpath(type_path + """.source""" )
A_ : Any = Path(_a ).joinpath(type_path + """.target""" )
A_ : Dict = self.get_char_lens(self.src_file )
A_ : Optional[int] = max_source_length
A_ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
A_ : List[Any] = tokenizer
A_ : Optional[Any] = prefix
if n_obs is not None:
A_ : Any = self.src_lens[:n_obs]
A_ : Optional[int] = src_lang
A_ : Tuple = tgt_lang
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : List[str] ,_a : Tuple ):
'''simple docstring'''
A_ : int = index + 1 # linecache starts at 1
A_ : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) ,_a ).rstrip("""\n""" )
A_ : Dict = linecache.getline(str(self.tgt_file ) ,_a ).rstrip("""\n""" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,_a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
A_ : List[str] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,_a ) else self.tokenizer
)
A_ : Any = self.tokenizer.generator if isinstance(self.tokenizer ,_a ) else self.tokenizer
A_ : Optional[int] = encode_line(_a ,_a ,self.max_source_length ,"""right""" )
A_ : Optional[int] = encode_line(_a ,_a ,self.max_target_length ,"""right""" )
A_ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
A_ : Dict = target_inputs["""input_ids"""].squeeze()
A_ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def _a ( _a : int ):
'''simple docstring'''
return [len(_a ) for x in Path(_a ).open().readlines()]
def _a ( self : Optional[int] ,_a : Dict ):
'''simple docstring'''
A_ : str = torch.stack([x["""input_ids"""] for x in batch] )
A_ : Optional[Any] = torch.stack([x["""attention_mask"""] for x in batch] )
A_ : str = torch.stack([x["""decoder_input_ids"""] for x in batch] )
A_ : Union[str, Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : str = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,_a )
else self.tokenizer.pad_token_id
)
A_ : List[str] = trim_batch(_a ,_a )
A_ , A_ : Union[str, Any] = trim_batch(_a ,_a ,attention_mask=_a )
A_ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__magic_name__ = getLogger(__name__)
def lowerCamelCase ( lowerCamelCase : List[List]):
return list(itertools.chain.from_iterable(lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : str):
A_ : Union[str, Any] = get_git_info()
save_json(lowerCamelCase , os.path.join(lowerCamelCase , """git_log.json"""))
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[Any] , lowerCamelCase : List[str]=4 , **lowerCamelCase : List[str]):
with open(lowerCamelCase , """w""") as f:
json.dump(lowerCamelCase , lowerCamelCase , indent=lowerCamelCase , **lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase) as f:
return json.load(lowerCamelCase)
def lowerCamelCase ( ):
A_ : List[str] = git.Repo(search_parent_directories=lowerCamelCase)
A_ : Union[str, Any] = {
"""repo_id""": str(lowerCamelCase),
"""repo_sha""": str(repo.head.object.hexsha),
"""repo_branch""": str(repo.active_branch),
"""hostname""": str(socket.gethostname()),
}
return repo_infos
def lowerCamelCase ( lowerCamelCase : Callable , lowerCamelCase : Iterable):
return list(map(lowerCamelCase , lowerCamelCase))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
with open(lowerCamelCase , """wb""") as f:
return pickle.dump(lowerCamelCase , lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str]):
def remove_articles(lowerCamelCase : Any):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , lowerCamelCase)
def white_space_fix(lowerCamelCase : List[Any]):
return " ".join(text.split())
def remove_punc(lowerCamelCase : Union[str, Any]):
A_ : Optional[int] = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(lowerCamelCase : List[str]):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : Tuple = normalize_answer(lowerCamelCase).split()
A_ : Dict = normalize_answer(lowerCamelCase).split()
A_ : int = Counter(lowerCamelCase) & Counter(lowerCamelCase)
A_ : Any = sum(common.values())
if num_same == 0:
return 0
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = 1.0 * num_same / len(lowerCamelCase)
A_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def lowerCamelCase ( lowerCamelCase : Any , lowerCamelCase : Any):
return normalize_answer(lowerCamelCase) == normalize_answer(lowerCamelCase)
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : List[str]):
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Any = 0
for hypo, pred in zip(lowerCamelCase , lowerCamelCase):
em += exact_match_score(lowerCamelCase , lowerCamelCase)
if len(lowerCamelCase) > 0:
em /= len(lowerCamelCase)
return {"em": em}
def lowerCamelCase ( lowerCamelCase : Union[str, Any]):
return model_prefix.startswith("""rag""")
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Union[str, Any]):
A_ : Optional[Any] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
A_ : Tuple = """dropout_rate"""
for p in extra_params:
if getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase):
if not hasattr(lowerCamelCase , lowerCamelCase) and not hasattr(lowerCamelCase , equivalent_param[p]):
logger.info("""config doesn't have a `{}` attribute""".format(lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
continue
A_ : Tuple = p if hasattr(lowerCamelCase , lowerCamelCase) else equivalent_param[p]
setattr(lowerCamelCase , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase))
delattr(lowerCamelCase , lowerCamelCase)
return hparams, config
| 27 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : int):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
A_ : Union[str, Any] = ksize + 1
A_ : Union[str, Any] = np.zeros((ksize, ksize) , dtype=np.floataa)
# each value
for y in range(lowerCamelCase):
for x in range(lowerCamelCase):
# distance from center
A_ : Optional[int] = x - ksize // 2
A_ : Any = y - ksize // 2
# degree to radiant
A_ : Dict = theta / 180 * np.pi
A_ : int = np.cos(_theta)
A_ : List[Any] = np.sin(_theta)
# get kernel x
A_ : Dict = cos_theta * px + sin_theta * py
# get kernel y
A_ : Tuple = -sin_theta * px + cos_theta * py
# fill kernel
A_ : List[Any] = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)) * np.cos(2 * np.pi * _x / lambd + psi)
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
__magic_name__ = imread('../image_data/lena.jpg')
# turn image in gray scale value
__magic_name__ = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
__magic_name__ = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
__magic_name__ = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
__magic_name__ = out / out.max() * 255
__magic_name__ = out.astype(np.uinta)
imshow('Original', gray)
imshow('Gabor filter with 20x20 mask and 6 directions', out)
waitKey(0)
| 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list):
A_ : List[Any] = False
while is_sorted is False: # Until all the indices are traversed keep looping
A_ : Optional[Any] = True
for i in range(0 , len(lowerCamelCase) - 1 , 2): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
A_ , A_ : Optional[int] = input_list[i + 1], input_list[i]
# swapping if elements not in order
A_ : Optional[Any] = False
for i in range(1 , len(lowerCamelCase) - 1 , 2): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
A_ , A_ : Union[str, Any] = input_list[i + 1], input_list[i]
# swapping if elements not in order
A_ : Tuple = False
return input_list
if __name__ == "__main__":
print('Enter list to be sorted')
__magic_name__ = [int(x) for x in input().split()]
# inputing elements of the list in one line
__magic_name__ = odd_even_sort(input_list)
print('The sorted list is')
print(sorted_list)
| 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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'''
a_ = KandinskyVaaControlnetPipeline
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
a_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a_ = False
@property
def _a ( self : Any ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return 32
@property
def _a ( self : Tuple ):
'''simple docstring'''
return self.time_input_dim
@property
def _a ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def _a ( self : Optional[Any] ):
'''simple docstring'''
return 100
@property
def _a ( self : List[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[Any] = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
A_ : Tuple = UNetaDConditionModel(**_a )
return model
@property
def _a ( self : List[str] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
A_ : int = VQModel(**self.dummy_movq_kwargs )
return model
def _a ( self : List[str] ):
'''simple docstring'''
A_ : Optional[Any] = self.dummy_unet
A_ : int = self.dummy_movq
A_ : Tuple = DDIMScheduler(
num_train_timesteps=1000 ,beta_schedule="""linear""" ,beta_start=0.00085 ,beta_end=0.012 ,clip_sample=_a ,set_alpha_to_one=_a ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=_a ,)
A_ : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def _a ( self : Dict ,_a : str ,_a : Union[str, Any]=0 ):
'''simple docstring'''
A_ : Dict = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(_a ) ).to(_a )
A_ : int = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
_a )
# create hint
A_ : List[Any] = floats_tensor((1, 3, 64, 64) ,rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
A_ : Optional[Any] = torch.manual_seed(_a )
else:
A_ : str = torch.Generator(device=_a ).manual_seed(_a )
A_ : List[Any] = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def _a ( self : Dict ):
'''simple docstring'''
A_ : List[Any] = """cpu"""
A_ : List[str] = self.get_dummy_components()
A_ : Tuple = self.pipeline_class(**_a )
A_ : Dict = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = pipe(**self.get_dummy_inputs(_a ) )
A_ : Tuple = output.images
A_ : Optional[Any] = pipe(
**self.get_dummy_inputs(_a ) ,return_dict=_a ,)[0]
A_ : Tuple = image[0, -3:, -3:, -1]
A_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : List[Any] = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
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 _a ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Any ):
'''simple docstring'''
A_ : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
A_ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
A_ : Optional[int] = torch.from_numpy(np.array(_a ) ).float() / 255.0
A_ : List[Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
A_ : List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(_a )
A_ : Union[str, Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
A_ : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
A_ : Optional[Any] = """A robot, 4k photo"""
A_ : Any = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ , A_ : List[str] = pipe_prior(
_a ,generator=_a ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
A_ : int = torch.Generator(device="""cuda""" ).manual_seed(0 )
A_ : List[Any] = pipeline(
image_embeds=_a ,negative_image_embeds=_a ,hint=_a ,generator=_a ,num_inference_steps=100 ,output_type="""np""" ,)
A_ : Dict = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(_a ,_a )
| 27 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowerCamelCase ( lowerCamelCase : str):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) #
or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F) #
or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F) #
or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F) #
or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F) #
or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F) #
): #
return True
return False
def lowerCamelCase ( lowerCamelCase : str):
# word like '180' or '身高' or '神'
for char in word:
A_ : List[Any] = ord(lowerCamelCase)
if not _is_chinese_char(lowerCamelCase):
return 0
return 1
def lowerCamelCase ( lowerCamelCase : List[str]):
A_ : int = set()
for token in tokens:
A_ : List[str] = len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
if chinese_word:
word_set.add(lowerCamelCase)
A_ : int = list(lowerCamelCase)
return word_list
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : set()):
if not chinese_word_set:
return bert_tokens
A_ : Optional[int] = max([len(lowerCamelCase) for w in chinese_word_set])
A_ : int = bert_tokens
A_ , A_ : Optional[Any] = 0, len(lowerCamelCase)
while start < end:
A_ : Tuple = True
if is_chinese(bert_word[start]):
A_ : int = min(end - start , lowerCamelCase)
for i in range(lowerCamelCase , 1 , -1):
A_ : Any = """""".join(bert_word[start : start + i])
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i):
A_ : Optional[Any] = """##""" + bert_word[j]
A_ : Dict = start + i
A_ : int = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : LTP , lowerCamelCase : BertTokenizer):
A_ : Optional[int] = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : str = ltp_tokenizer.seg(lines[i : i + 100])[0]
A_ : Optional[int] = [get_chinese_word(lowerCamelCase) for r in res]
ltp_res.extend(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Dict = []
for i in range(0 , len(lowerCamelCase) , 100):
A_ : List[str] = bert_tokenizer(lines[i : i + 100] , add_special_tokens=lowerCamelCase , truncation=lowerCamelCase , max_length=512)
bert_res.extend(res["""input_ids"""])
assert len(lowerCamelCase) == len(lowerCamelCase)
A_ : Optional[Any] = []
for input_ids, chinese_word in zip(lowerCamelCase , lowerCamelCase):
A_ : int = []
for id in input_ids:
A_ : Optional[int] = bert_tokenizer._convert_id_to_token(lowerCamelCase)
input_tokens.append(lowerCamelCase)
A_ : Tuple = add_sub_symbol(lowerCamelCase , lowerCamelCase)
A_ : str = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(lowerCamelCase):
if token[:2] == "##":
A_ : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(lowerCamelCase) == 1 and _is_chinese_char(ord(lowerCamelCase)):
ref_id.append(lowerCamelCase)
ref_ids.append(lowerCamelCase)
assert len(lowerCamelCase) == len(lowerCamelCase)
return ref_ids
def lowerCamelCase ( lowerCamelCase : List[str]):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""") as f:
A_ : Optional[int] = f.readlines()
A_ : Tuple = [line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
A_ : int = LTP(args.ltp) # faster in GPU device
A_ : Optional[Any] = BertTokenizer.from_pretrained(args.bert)
A_ : Union[str, Any] = prepare_ref(lowerCamelCase , lowerCamelCase , lowerCamelCase)
with open(args.save_path , """w""" , encoding="""utf-8""") as f:
A_ : List[Any] = [json.dumps(lowerCamelCase) + """\n""" for ref in ref_ids]
f.writelines(lowerCamelCase)
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser(description='prepare_chinese_ref')
parser.add_argument(
'--file_name',
type=str,
default='./resources/chinese-demo.txt',
help='file need process, same as training data in lm',
)
parser.add_argument(
'--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
)
parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')
__magic_name__ = parser.parse_args()
main(args)
| 27 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """deberta-v2"""
def __init__( self : Optional[Any] ,_a : Union[str, Any]=128100 ,_a : Optional[int]=1536 ,_a : Dict=24 ,_a : int=24 ,_a : Tuple=6144 ,_a : Union[str, Any]="gelu" ,_a : List[Any]=0.1 ,_a : Dict=0.1 ,_a : int=512 ,_a : int=0 ,_a : int=0.02 ,_a : int=1e-7 ,_a : List[str]=False ,_a : Union[str, Any]=-1 ,_a : List[Any]=0 ,_a : Optional[Any]=True ,_a : Tuple=None ,_a : Any=0 ,_a : int="gelu" ,**_a : Any ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : List[Any] = initializer_range
A_ : int = relative_attention
A_ : Tuple = max_relative_positions
A_ : int = pad_token_id
A_ : Tuple = position_biased_input
# Backwards compatibility
if type(_a ) == str:
A_ : str = [x.strip() for x in pos_att_type.lower().split("""|""" )]
A_ : Any = pos_att_type
A_ : Optional[int] = vocab_size
A_ : Tuple = layer_norm_eps
A_ : Any = kwargs.get("""pooler_hidden_size""" ,_a )
A_ : Union[str, Any] = pooler_dropout
A_ : List[Any] = pooler_hidden_act
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : Any = {0: """batch""", 1: """sequence"""}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
else:
return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )
@property
def _a ( self : Optional[int] ):
'''simple docstring'''
return 12
def _a ( self : int ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 3 ,_a : int = 40 ,_a : int = 40 ,_a : "PreTrainedTokenizerBase" = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(preprocessor=_a ,framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 27 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __lowerCAmelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def _a ( self : Optional[int] ):
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"""content""": datasets.Value("""string""" )} ) ,supervised_keys=_a ,)
def _a ( self : List[str] ,_a : Dict ,_a : int ):
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""examples""": get_test_dummy_examples()} )]
def _a ( self : Any ,_a : Union[str, Any] ,_a : Optional[Any] ):
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_a )
class __lowerCAmelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def _a ( self : List[str] ):
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) ,supervised_keys=_a ,)
def _a ( self : Optional[Any] ,_a : Union[str, Any] ,_a : Dict ):
'''simple docstring'''
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""examples""": get_test_nested_examples()} )
]
def _a ( self : Tuple ,_a : Tuple ,_a : Dict ):
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_a )
def lowerCamelCase ( ):
return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""])]
def lowerCamelCase ( ):
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""])]
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@require_beam
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : Optional[Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
A_ : Union[str, Any] = DummyBeamDataset(cache_dir=_a ,beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_a ,builder.name ,"""default""" ,"""0.0.0""" ,f'{builder.name}-train.arrow' ) ) )
self.assertDictEqual(builder.info.features ,datasets.Features({"""content""": datasets.Value("""string""" )} ) )
A_ : Optional[Any] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows ,_a )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples ,_a )
self.assertDictEqual(dset["""train"""][0] ,get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] ,get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_a ,builder.name ,"""default""" ,"""0.0.0""" ,"""dataset_info.json""" ) ) )
del dset
@require_beam
def _a ( self : Dict ):
'''simple docstring'''
import apache_beam as beam
A_ : Dict = beam.io.parquetio.WriteToParquet
A_ : Optional[int] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
A_ : List[Any] = DummyBeamDataset(cache_dir=_a ,beam_runner="""DirectRunner""" )
with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
A_ : int = partial(_a ,num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_a ,builder.name ,"""default""" ,"""0.0.0""" ,f'{builder.name}-train-00000-of-00002.arrow' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_a ,builder.name ,"""default""" ,"""0.0.0""" ,f'{builder.name}-train-00000-of-00002.arrow' ) ) )
self.assertDictEqual(builder.info.features ,datasets.Features({"""content""": datasets.Value("""string""" )} ) )
A_ : int = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows ,_a )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples ,_a )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["""train"""]["""content"""] ) ,sorted(["""foo""", """bar""", """foobar"""] ) )
self.assertTrue(
os.path.exists(os.path.join(_a ,builder.name ,"""default""" ,"""0.0.0""" ,"""dataset_info.json""" ) ) )
del dset
@require_beam
def _a ( self : Optional[int] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_cache_dir:
A_ : List[Any] = DummyBeamDataset(cache_dir=_a )
self.assertRaises(datasets.builder.MissingBeamOptions ,builder.download_and_prepare )
@require_beam
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Optional[int] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
A_ : str = NestedBeamDataset(cache_dir=_a ,beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_a ,builder.name ,"""default""" ,"""0.0.0""" ,f'{builder.name}-train.arrow' ) ) )
self.assertDictEqual(
builder.info.features ,datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
A_ : Tuple = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows ,_a )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples ,_a )
self.assertDictEqual(dset["""train"""][0] ,get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] ,get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_a ,builder.name ,"""default""" ,"""0.0.0""" ,"""dataset_info.json""" ) ) )
del dset
| 27 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
__magic_name__ = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
__magic_name__ = requests.get(url, headers={'UserAgent': UserAgent().random})
# res.raise_for_status()
with open('project1a.html', 'wb') as out_file: # only for knowing the class
for data in res.iter_content(10_000):
out_file.write(data)
__magic_name__ = BeautifulSoup(res.text, 'html.parser')
__magic_name__ = list(soup.select('.eZt8xd'))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get('href'))
else:
webbrowser.open(f"""https://google.com{link.get('href')}""")
| 27 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
__magic_name__ = [8, 5, 9, 7]
__magic_name__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__magic_name__ = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : List[str] ,_a : list[int] ,_a : list[list[int]] ,_a : list[list[int]] ,):
'''simple docstring'''
A_ : List[Any] = claim_vector
A_ : int = allocated_resources_table
A_ : Dict = maximum_claim_table
def _a ( self : Tuple ):
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self : int ):
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self : Dict ):
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self : Optional[int] ):
'''simple docstring'''
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self : Optional[int] ,**_a : int ):
'''simple docstring'''
A_ : Dict = self.__need()
A_ : List[str] = self.__allocated_resources_table
A_ : Optional[Any] = self.__available_resources()
A_ : Any = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
A_ : List[str] = False
for each_need in need_list:
A_ : Tuple = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
A_ : Optional[Any] = False
break
if execution:
A_ : Optional[Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
A_ : List[Any] = original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
A_ : Optional[Any] = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self : Dict ):
'''simple docstring'''
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(_a ) + 1}'
+ """ """.join(f'{it:>8}' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(_a ) + 1}'
+ """ """.join(f'{it:>8}' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
'''simple docstring'''
from ... import PretrainedConfig
__magic_name__ = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
a_ = """nezha"""
def __init__( self : int ,_a : Union[str, Any]=21128 ,_a : int=768 ,_a : Any=12 ,_a : List[str]=12 ,_a : str=3072 ,_a : int="gelu" ,_a : int=0.1 ,_a : str=0.1 ,_a : Tuple=512 ,_a : List[Any]=64 ,_a : Dict=2 ,_a : List[Any]=0.02 ,_a : Optional[Any]=1e-12 ,_a : List[Any]=0.1 ,_a : Union[str, Any]=0 ,_a : Any=2 ,_a : Union[str, Any]=3 ,_a : int=True ,**_a : int ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,bos_token_id=_a ,eos_token_id=_a ,**_a )
A_ : Tuple = vocab_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : List[Any] = num_attention_heads
A_ : Tuple = hidden_act
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_dropout_prob
A_ : Tuple = attention_probs_dropout_prob
A_ : Dict = max_position_embeddings
A_ : Optional[Any] = max_relative_position
A_ : List[Any] = type_vocab_size
A_ : int = initializer_range
A_ : Tuple = layer_norm_eps
A_ : Dict = classifier_dropout
A_ : int = use_cache
| 27 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
def _a ( self : int ,_a : Optional[int]=0 ):
'''simple docstring'''
A_ : Any = np.random.RandomState(_a )
A_ : List[str] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Union[str, Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_a )
A_ : Union[str, Any] = self.get_dummy_inputs()
A_ : List[Any] = pipe(**_a ).images
A_ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A_ : List[Any] = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Any ):
'''simple docstring'''
A_ : Union[str, Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
A_ : Any = PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=_a )
pipe.set_progress_bar_config(disable=_a )
A_ : Dict = self.get_dummy_inputs()
A_ : Union[str, Any] = pipe(**_a ).images
A_ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A_ : int = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Dict ):
'''simple docstring'''
A_ : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
A_ : Dict = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
A_ : Optional[int] = self.get_dummy_inputs()
A_ : Optional[int] = pipe(**_a ).images
A_ : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A_ : int = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
A_ : List[str] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
A_ : Dict = self.get_dummy_inputs()
A_ : Union[str, Any] = pipe(**_a ).images
A_ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A_ : Dict = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Optional[Any] ):
'''simple docstring'''
A_ : List[str] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
A_ : Tuple = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
A_ : Union[str, Any] = self.get_dummy_inputs()
A_ : Any = pipe(**_a ).images
A_ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A_ : Tuple = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : List[Any] ):
'''simple docstring'''
A_ : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
A_ : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = self.get_dummy_inputs()
A_ : int = pipe(**_a ).images
A_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A_ : Union[str, Any] = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ):
'''simple docstring'''
A_ : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_a )
A_ : str = self.get_dummy_inputs()
A_ : List[str] = 3 * [inputs["""prompt"""]]
# forward
A_ : Optional[Any] = pipe(**_a )
A_ : Any = output.images[0, -3:, -3:, -1]
A_ : str = self.get_dummy_inputs()
A_ : Tuple = 3 * [inputs.pop("""prompt""" )]
A_ : List[Any] = pipe.tokenizer(
_a ,padding="""max_length""" ,max_length=pipe.tokenizer.model_max_length ,truncation=_a ,return_tensors="""np""" ,)
A_ : Union[str, Any] = text_inputs["""input_ids"""]
A_ : Tuple = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
A_ : Optional[int] = prompt_embeds
# forward
A_ : int = pipe(**_a )
A_ : Union[str, Any] = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _a ( self : Dict ):
'''simple docstring'''
A_ : int = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_a )
A_ : Tuple = self.get_dummy_inputs()
A_ : Union[str, Any] = 3 * ["""this is a negative prompt"""]
A_ : Any = negative_prompt
A_ : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
A_ : Tuple = pipe(**_a )
A_ : Any = output.images[0, -3:, -3:, -1]
A_ : List[Any] = self.get_dummy_inputs()
A_ : List[str] = 3 * [inputs.pop("""prompt""" )]
A_ : List[Any] = []
for p in [prompt, negative_prompt]:
A_ : int = pipe.tokenizer(
_a ,padding="""max_length""" ,max_length=pipe.tokenizer.model_max_length ,truncation=_a ,return_tensors="""np""" ,)
A_ : Tuple = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
A_ , A_ : Union[str, Any] = embeds
# forward
A_ : List[Any] = pipe(**_a )
A_ : str = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def _a ( self : Tuple ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Any ):
'''simple docstring'''
A_ : Union[str, Any] = ort.SessionOptions()
A_ : int = False
return options
def _a ( self : Any ):
'''simple docstring'''
A_ : Tuple = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" ,revision="""onnx""" ,safety_checker=_a ,feature_extractor=_a ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
sd_pipe.set_progress_bar_config(disable=_a )
A_ : Optional[int] = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
A_ : Dict = sd_pipe([prompt] ,guidance_scale=6.0 ,num_inference_steps=10 ,output_type="""np""" )
A_ : Tuple = output.images
A_ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : Tuple = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,subfolder="""scheduler""" ,revision="""onnx""" )
A_ : int = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,scheduler=_a ,safety_checker=_a ,feature_extractor=_a ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
sd_pipe.set_progress_bar_config(disable=_a )
A_ : Dict = """open neural network exchange"""
A_ : Optional[int] = np.random.RandomState(0 )
A_ : Union[str, Any] = sd_pipe([prompt] ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=_a ,output_type="""np""" )
A_ : Tuple = output.images
A_ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : str = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Any ):
'''simple docstring'''
A_ : str = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,subfolder="""scheduler""" ,revision="""onnx""" )
A_ : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,scheduler=_a ,safety_checker=_a ,feature_extractor=_a ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
sd_pipe.set_progress_bar_config(disable=_a )
A_ : Optional[int] = """open neural network exchange"""
A_ : Optional[Any] = np.random.RandomState(0 )
A_ : int = sd_pipe([prompt] ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=_a ,output_type="""np""" )
A_ : str = output.images
A_ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
A_ : str = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Any ):
'''simple docstring'''
A_ : int = 0
def test_callback_fn(_a : int ,_a : int ,_a : np.ndarray ) -> None:
A_ : Any = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
A_ : Union[str, Any] = latents[0, -3:, -3:, -1]
A_ : str = np.array(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
A_ : Any = latents[0, -3:, -3:, -1]
A_ : int = np.array(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
A_ : int = False
A_ : int = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,safety_checker=_a ,feature_extractor=_a ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=_a )
A_ : List[str] = """Andromeda galaxy in a bottle"""
A_ : List[Any] = np.random.RandomState(0 )
pipe(
prompt=_a ,num_inference_steps=5 ,guidance_scale=7.5 ,generator=_a ,callback=_a ,callback_steps=1 ,)
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _a ( self : Dict ):
'''simple docstring'''
A_ : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,safety_checker=_a ,feature_extractor=_a ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
assert isinstance(_a ,_a )
assert pipe.safety_checker is None
A_ : List[str] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_a )
A_ : Tuple = OnnxStableDiffusionPipeline.from_pretrained(_a )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
A_ : int = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
| 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] = stack.pop()
explored.add(lowerCamelCase)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(lowerCamelCase)
return explored
__magic_name__ = {
'A': ['B', 'C', 'D'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F'],
'D': ['B', 'D'],
'E': ['B', 'F'],
'F': ['C', 'E', 'G'],
'G': ['F'],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, 'A'))
| 27 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : Tuple ,_a : bool = True ,_a : int = 32 ,_a : List[Any]=PILImageResampling.BILINEAR ,_a : bool = True ,**_a : Optional[Any] ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize
A_ : List[Any] = do_rescale
A_ : int = size_divisor
A_ : Optional[Any] = resample
super().__init__(**_a )
def _a ( self : Optional[int] ,_a : np.ndarray ,_a : int ,_a : Any ,_a : Optional[ChannelDimension] = None ,**_a : List[str] ):
'''simple docstring'''
A_ , A_ : List[Any] = get_image_size(_a )
# Rounds the height and width down to the closest multiple of size_divisor
A_ : str = height // size_divisor * size_divisor
A_ : Optional[Any] = width // size_divisor * size_divisor
A_ : List[str] = resize(_a ,(new_h, new_w) ,resample=_a ,data_format=_a ,**_a )
return image
def _a ( self : List[Any] ,_a : np.ndarray ,_a : float ,_a : Optional[ChannelDimension] = None ,**_a : int ):
'''simple docstring'''
return rescale(image=_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Union[str, Any] ,_a : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,_a : Optional[bool] = None ,_a : Optional[int] = None ,_a : Optional[Any]=None ,_a : Optional[bool] = None ,_a : Optional[Union[TensorType, str]] = None ,_a : ChannelDimension = ChannelDimension.FIRST ,**_a : List[str] ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : Optional[Any] = size_divisor if size_divisor is not None else self.size_divisor
A_ : Union[str, Any] = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
A_ : Optional[Any] = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
A_ : Optional[Any] = [to_numpy_array(_a ) for img in images]
if do_resize:
A_ : Any = [self.resize(_a ,size_divisor=_a ,resample=_a ) for image in images]
if do_rescale:
A_ : Dict = [self.rescale(_a ,scale=1 / 255 ) for image in images]
A_ : Optional[Any] = [to_channel_dimension_format(_a ,_a ) for image in images]
A_ : Tuple = {"""pixel_values""": images}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : List[str] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
A_ : Union[str, Any] = [144, 192, 240]
A_ : int = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
A_ : List[str] = [96, 120, 144]
A_ : Any = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
A_ : Any = [64, 80, 96]
A_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
A_ : Any = 0.05
A_ : List[Any] = 2.0
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : int = 512
A_ : Optional[int] = 16
A_ : List[Any] = 21
A_ : List[str] = """pascal-voc-id2label.json"""
else:
A_ : str = 1000
A_ : Any = """imagenet-1k-id2label.json"""
A_ : Any = """huggingface/label-files"""
A_ : List[str] = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""") , """r"""))
A_ : str = {int(lowerCamelCase): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : List[str] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int=False):
for i in range(1 , 6):
if F'layer_{i}.' in name:
A_ : Tuple = name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.')
if "conv_1." in name:
A_ : Union[str, Any] = name.replace("""conv_1.""" , """conv_stem.""")
if ".block." in name:
A_ : Optional[Any] = name.replace(""".block.""" , """.""")
if "exp_1x1" in name:
A_ : Union[str, Any] = name.replace("""exp_1x1""" , """expand_1x1""")
if "red_1x1" in name:
A_ : int = name.replace("""red_1x1""" , """reduce_1x1""")
if ".local_rep.conv_3x3." in name:
A_ : List[str] = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""")
if ".local_rep.conv_1x1." in name:
A_ : Optional[int] = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""")
if ".norm." in name:
A_ : Tuple = name.replace(""".norm.""" , """.normalization.""")
if ".conv." in name:
A_ : List[Any] = name.replace(""".conv.""" , """.convolution.""")
if ".conv_proj." in name:
A_ : str = name.replace(""".conv_proj.""" , """.conv_projection.""")
for i in range(0 , 2):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Tuple = name.replace(F'.{i}.{j}.' , F'.{i}.layer.{j}.')
for i in range(2 , 6):
for j in range(0 , 4):
if F'.{i}.{j}.' in name:
A_ : Dict = name.replace(F'.{i}.{j}.' , F'.{i}.')
if "expand_1x1" in name:
A_ : Union[str, Any] = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""")
if "conv_3x3" in name:
A_ : str = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""")
if "reduce_1x1" in name:
A_ : Union[str, Any] = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""")
for i in range(2 , 5):
if F'.global_rep.{i}.weight' in name:
A_ : List[Any] = name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight""")
if F'.global_rep.{i}.bias' in name:
A_ : Optional[int] = name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias""")
if ".global_rep." in name:
A_ : Optional[Any] = name.replace(""".global_rep.""" , """.transformer.""")
if ".pre_norm_mha.0." in name:
A_ : int = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""")
if ".pre_norm_mha.1.out_proj." in name:
A_ : Dict = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""")
if ".pre_norm_ffn.0." in name:
A_ : Dict = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""")
if ".pre_norm_ffn.1." in name:
A_ : Any = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""")
if ".pre_norm_ffn.4." in name:
A_ : Union[str, Any] = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""")
if ".transformer." in name:
A_ : Any = name.replace(""".transformer.""" , """.transformer.layer.""")
if ".aspp_layer." in name:
A_ : int = name.replace(""".aspp_layer.""" , """.""")
if ".aspp_pool." in name:
A_ : Tuple = name.replace(""".aspp_pool.""" , """.""")
if "seg_head." in name:
A_ : Optional[int] = name.replace("""seg_head.""" , """segmentation_head.""")
if "segmentation_head.classifier.classifier." in name:
A_ : List[str] = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""")
if "classifier.fc." in name:
A_ : str = name.replace("""classifier.fc.""" , """classifier.""")
elif (not base_model) and ("segmentation_head." not in name):
A_ : str = """mobilevit.""" + name
return name
def lowerCamelCase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=False):
if base_model:
A_ : Dict = """"""
else:
A_ : Any = """mobilevit."""
for key in orig_state_dict.copy().keys():
A_ : List[Any] = orig_state_dict.pop(lowerCamelCase)
if key[:8] == "encoder.":
A_ : int = key[8:]
if "qkv" in key:
A_ : Any = key.split(""".""")
A_ : str = int(key_split[0][6:]) - 1
A_ : int = int(key_split[3])
A_ : Optional[Any] = model.get_submodule(F'{model_prefix}encoder.layer.{layer_num}')
A_ : Tuple = layer.transformer.layer[transformer_num].attention.attention.all_head_size
A_ : Optional[Any] = (
F'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
)
if "weight" in key:
A_ : Dict = val[:dim, :]
A_ : Optional[int] = val[dim : dim * 2, :]
A_ : List[Any] = val[-dim:, :]
else:
A_ : Optional[Any] = val[:dim]
A_ : List[Any] = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : List[str] = val
return orig_state_dict
def lowerCamelCase ( ):
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase).raw)
return im
@torch.no_grad()
def lowerCamelCase ( lowerCamelCase : List[str] , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : int=False):
A_ : Optional[Any] = get_mobilevit_config(lowerCamelCase)
# load original state_dict
A_ : List[Any] = torch.load(lowerCamelCase , map_location="""cpu""")
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_"""):
A_ : List[str] = MobileViTForSemanticSegmentation(lowerCamelCase).eval()
else:
A_ : str = MobileViTForImageClassification(lowerCamelCase).eval()
A_ : str = convert_state_dict(lowerCamelCase , lowerCamelCase)
model.load_state_dict(lowerCamelCase)
# Check outputs on an image, prepared by MobileViTImageProcessor
A_ : Optional[Any] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32)
A_ : Any = image_processor(images=prepare_img() , return_tensors="""pt""")
A_ : List[Any] = model(**lowerCamelCase)
A_ : Dict = outputs.logits
if mobilevit_name.startswith("""deeplabv3_"""):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
A_ : int = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xs":
A_ : Tuple = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
])
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
A_ : Tuple = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase , atol=1E-4)
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
A_ : Tuple = torch.tensor([-0.9866, 0.2392, -1.1241])
elif mobilevit_name == "mobilevit_xs":
A_ : Any = torch.tensor([-2.4761, -0.9399, -1.9587])
elif mobilevit_name == "mobilevit_xxs":
A_ : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653])
else:
raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}')
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1E-4)
Path(lowerCamelCase).mkdir(exist_ok=lowerCamelCase)
print(F'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowerCamelCase)
print(F'Saving image processor to {pytorch_dump_folder_path}')
image_processor.save_pretrained(lowerCamelCase)
if push_to_hub:
A_ : str = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""")
A_ : Union[str, Any] = model_mapping[mobilevit_name]
image_processor.push_to_hub(lowerCamelCase , organization="""apple""")
model.push_to_hub(lowerCamelCase , organization="""apple""")
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--mobilevit_name',
default='mobilevit_s',
type=str,
help=(
'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
),
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, 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.'
)
__magic_name__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 27 | 1 |
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