code
stringlengths 87
55.2k
| code_codestyle
int64 0
349
| style_context
stringlengths 135
49.1k
| style_context_codestyle
int64 0
349
| label
int64 0
1
|
|---|---|---|---|---|
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class _snake_case ( a__ ):
'''simple docstring'''
A__ : str = field(default="automatic-speech-recognition" , metadata={"include_in_asdict_even_if_is_default": True} )
A__ : ClassVar[Features] = Features({"audio": Audio()} )
A__ : ClassVar[Features] = Features({"transcription": Value("string" )} )
A__ : str = "audio"
A__ : str = "transcription"
def A__ ( self: Union[str, Any] ,lowerCamelCase_: Any ) -> Any:
if self.audio_column not in features:
raise ValueError(F'''Column {self.audio_column} is not present in features.''' )
if not isinstance(features[self.audio_column] ,__UpperCAmelCase ):
raise ValueError(F'''Column {self.audio_column} is not an Audio type.''' )
UpperCAmelCase_ : Tuple = copy.deepcopy(self )
UpperCAmelCase_ : Any = self.input_schema.copy()
UpperCAmelCase_ : List[str] = features[self.audio_column]
UpperCAmelCase_ : int = input_schema
return task_template
@property
def A__ ( self: List[Any] ) -> Dict[str, str]:
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 345 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__snake_case = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__snake_case = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
def remove_articles(_lowerCAmelCase : Optional[int] ):
_a = re.compile(R'''\b(a|an|the)\b''', re.UNICODE )
return re.sub(_lowerCAmelCase, ''' ''', _lowerCAmelCase )
def white_space_fix(_lowerCAmelCase : Tuple ):
return " ".join(text.split() )
def remove_punc(_lowerCAmelCase : Tuple ):
_a = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_lowerCAmelCase : List[Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase ) ) ) )
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return int(normalize_answer(_lowerCAmelCase ) == normalize_answer(_lowerCAmelCase ) )
def A_ ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Any ):
"""simple docstring"""
_a = [any(compute_exact(_lowerCAmelCase, _lowerCAmelCase ) for ref in refs ) for pred, refs in zip(_lowerCAmelCase, _lowerCAmelCase )]
return (sum(_lowerCAmelCase ) / len(_lowerCAmelCase )) * 1_00
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : List[Any], _lowerCAmelCase : str, _lowerCAmelCase : str ):
"""simple docstring"""
_a = [rgram for rgrams in rgramslist for rgram in rgrams]
_a = Counter(_lowerCAmelCase )
_a = Counter(_lowerCAmelCase )
_a = Counter()
for sgram, scount in sgramcounter.items():
_a = scount * numref
_a = Counter(_lowerCAmelCase )
_a = Counter()
for cgram, ccount in cgramcounter.items():
_a = ccount * numref
# KEEP
_a = sgramcounter_rep & cgramcounter_rep
_a = keepgramcounter_rep & rgramcounter
_a = sgramcounter_rep & rgramcounter
_a = 0
_a = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_a = 1
_a = 1
if len(_lowerCAmelCase ) > 0:
_a = keeptmpscorea / len(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_a = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_a = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_a = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_a = sgramcounter_rep - cgramcounter_rep
_a = delgramcounter_rep - rgramcounter
_a = sgramcounter_rep - rgramcounter
_a = 0
_a = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_a = 1
if len(_lowerCAmelCase ) > 0:
_a = deltmpscorea / len(_lowerCAmelCase )
# ADDITION
_a = set(_lowerCAmelCase ) - set(_lowerCAmelCase )
_a = set(_lowerCAmelCase ) & set(_lowerCAmelCase )
_a = set(_lowerCAmelCase ) - set(_lowerCAmelCase )
_a = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_a = 1
_a = 1
if len(_lowerCAmelCase ) > 0:
_a = addtmpscore / len(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
_a = addtmpscore / len(_lowerCAmelCase )
_a = 0
if addscore_precision > 0 or addscore_recall > 0:
_a = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def A_ ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Dict, _lowerCAmelCase : Any ):
"""simple docstring"""
_a = len(_lowerCAmelCase )
_a = ssent.split(''' ''' )
_a = csent.split(''' ''' )
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
for rsent in rsents:
_a = rsent.split(''' ''' )
_a = []
_a = []
_a = []
ragramslist.append(_lowerCAmelCase )
for i in range(0, len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
_a = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
_a = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
_a = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
for i in range(0, len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
_a = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
_a = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
_a = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(_lowerCAmelCase )
for i in range(0, len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
_a = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
_a = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
_a = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(_lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
_a = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_a = sum([delascore, delascore, delascore, delascore] ) / 4
_a = sum([addascore, addascore, addascore, addascore] ) / 4
_a = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : bool = True, _lowerCAmelCase : str = "13a", _lowerCAmelCase : bool = True ):
"""simple docstring"""
if lowercase:
_a = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_a = sacrebleu.metrics.bleu._get_tokenizer(_lowerCAmelCase )()(_lowerCAmelCase )
else:
_a = sacrebleu.TOKENIZERS[tokenizer]()(_lowerCAmelCase )
elif tokenizer == "moses":
_a = sacremoses.MosesTokenizer().tokenize(_lowerCAmelCase, return_str=_lowerCAmelCase, escape=_lowerCAmelCase )
elif tokenizer == "penn":
_a = sacremoses.MosesTokenizer().penn_tokenize(_lowerCAmelCase, return_str=_lowerCAmelCase )
else:
_a = sentence
if not return_str:
_a = normalized_sent.split()
return normalized_sent
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : Dict, _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
if not (len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == len(_lowerCAmelCase )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_a = 0
for src, pred, refs in zip(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase ):
sari_score += SARIsent(normalize(_lowerCAmelCase ), normalize(_lowerCAmelCase ), [normalize(_lowerCAmelCase ) for sent in refs] )
_a = sari_score / len(_lowerCAmelCase )
return 1_00 * sari_score
def A_ ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Tuple, _lowerCAmelCase : Any="exp", _lowerCAmelCase : Tuple=None, _lowerCAmelCase : Union[str, Any]=False, _lowerCAmelCase : Optional[Any]=False, _lowerCAmelCase : List[str]=False, ):
"""simple docstring"""
_a = len(references[0] )
if any(len(_lowerCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_a = [[refs[i] for refs in references] for i in range(_lowerCAmelCase )]
_a = sacrebleu.corpus_bleu(
_lowerCAmelCase, _lowerCAmelCase, smooth_method=_lowerCAmelCase, smooth_value=_lowerCAmelCase, force=_lowerCAmelCase, lowercase=_lowerCAmelCase, use_effective_order=_lowerCAmelCase, )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> str:
_a = {}
result.update({'''sari''': compute_sari(sources=__UpperCAmelCase , predictions=__UpperCAmelCase , references=__UpperCAmelCase )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=__UpperCAmelCase , references=__UpperCAmelCase )} )
result.update({'''exact''': compute_em(predictions=__UpperCAmelCase , references=__UpperCAmelCase )} )
return result | 320 | 0 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
snake_case_ = RobertaPreLayerNormConfig.from_pretrained(
_lowerCAmelCase , architectures=['RobertaPreLayerNormForMaskedLM'] )
# convert state_dict
snake_case_ = torch.load(hf_hub_download(repo_id=_lowerCAmelCase , filename='pytorch_model.bin' ) )
snake_case_ = {}
for tensor_key, tensor_value in original_state_dict.items():
# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
if tensor_key.startswith('roberta.' ):
snake_case_ = 'roberta_prelayernorm.' + tensor_key[len('roberta.' ) :]
# The original implementation contains weights which are not used, remove them from the state_dict
if tensor_key.endswith('.self.LayerNorm.weight' ) or tensor_key.endswith('.self.LayerNorm.bias' ):
continue
snake_case_ = tensor_value
snake_case_ = RobertaPreLayerNormForMaskedLM.from_pretrained(
pretrained_model_name_or_path=_lowerCAmelCase , config=_lowerCAmelCase , state_dict=_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
# convert tokenizer
snake_case_ = AutoTokenizer.from_pretrained(_lowerCAmelCase )
tokenizer.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint-repo''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__UpperCamelCase = parser.parse_args()
convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
| 69 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f'{solution() = }') | 320 | 0 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCamelCase = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCamelCase = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(self._get_feature_types() ), reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
], )
def _UpperCAmelCase ( self ) -> Any:
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def _UpperCAmelCase ( self, lowercase_, lowercase_, lowercase_=None, lowercase_="uniform_average", lowercase_=True ) -> Optional[int]:
"""simple docstring"""
a__ =mean_squared_error(
__UpperCAmelCase, __UpperCAmelCase, sample_weight=__UpperCAmelCase, multioutput=__UpperCAmelCase, squared=__UpperCAmelCase )
return {"mse": mse}
| 188 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__snake_case = logging.get_logger('''transformers.models.speecht5''')
__snake_case = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__snake_case = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__snake_case = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__snake_case = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__snake_case = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__snake_case = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__snake_case = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__snake_case = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__snake_case = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case = []
__snake_case = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Tuple, _lowerCAmelCase : Dict, _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
for attribute in key.split('''.''' ):
_a = getattr(_lowerCAmelCase, _lowerCAmelCase )
if weight_type is not None:
_a = getattr(_lowerCAmelCase, _lowerCAmelCase ).shape
else:
_a = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
_a = value
elif weight_type == "weight_g":
_a = value
elif weight_type == "weight_v":
_a = value
elif weight_type == "bias":
_a = value
elif weight_type == "running_mean":
_a = value
elif weight_type == "running_var":
_a = value
elif weight_type == "num_batches_tracked":
_a = value
else:
_a = value
logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Tuple ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
_a , _a = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def A_ ( _lowerCAmelCase : Any, _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : int ):
"""simple docstring"""
_a = []
if task == "s2t":
_a = hf_model.speechta.encoder.prenet.feature_encoder
_a = MAPPING_S2T
_a = IGNORE_KEYS_S2T
elif task == "t2s":
_a = None
_a = MAPPING_T2S
_a = IGNORE_KEYS_T2S
elif task == "s2s":
_a = hf_model.speechta.encoder.prenet.feature_encoder
_a = MAPPING_S2S
_a = IGNORE_KEYS_S2S
else:
raise ValueError(f'Unsupported task: {task}' )
for name, value in fairseq_dict.items():
if should_ignore(_lowerCAmelCase, _lowerCAmelCase ):
logger.info(f'{name} was ignored' )
continue
_a = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, hf_model.config.feat_extract_norm == '''group''', )
_a = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
_a , _a = key.split('''.*.''' )
if prefix in name and suffix in name:
_a = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
_a = True
if "*" in mapped_key:
_a = name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
_a = mapped_key.replace('''*''', _lowerCAmelCase )
if "weight_g" in name:
_a = '''weight_g'''
elif "weight_v" in name:
_a = '''weight_v'''
elif "bias" in name:
_a = '''bias'''
elif "weight" in name:
_a = '''weight'''
elif "running_mean" in name:
_a = '''running_mean'''
elif "running_var" in name:
_a = '''running_var'''
elif "num_batches_tracked" in name:
_a = '''num_batches_tracked'''
else:
_a = None
set_recursively(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Dict, _lowerCAmelCase : List[Any], _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_a = full_name.split('''conv_layers.''' )[-1]
_a = name.split('''.''' )
_a = int(items[0] )
_a = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_a = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_a = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_a = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_a = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def A_ ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Dict, _lowerCAmelCase : List[Any]=None, _lowerCAmelCase : List[str]=None, _lowerCAmelCase : int=None, ):
"""simple docstring"""
if config_path is not None:
_a = SpeechTaConfig.from_pretrained(_lowerCAmelCase )
else:
_a = SpeechTaConfig()
if task == "s2t":
_a = config.max_text_positions
_a = SpeechTaForSpeechToText(_lowerCAmelCase )
elif task == "t2s":
_a = 18_76
_a = 6_00
_a = config.max_speech_positions
_a = SpeechTaForTextToSpeech(_lowerCAmelCase )
elif task == "s2s":
_a = 18_76
_a = config.max_speech_positions
_a = SpeechTaForSpeechToSpeech(_lowerCAmelCase )
else:
raise ValueError(f'Unknown task name: {task}' )
if vocab_path:
_a = SpeechTaTokenizer(_lowerCAmelCase, model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
_a = AddedToken('''<mask>''', lstrip=_lowerCAmelCase, rstrip=_lowerCAmelCase )
_a = mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
_a = SpeechTaFeatureExtractor()
_a = SpeechTaProcessor(tokenizer=_lowerCAmelCase, feature_extractor=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
_a = torch.load(_lowerCAmelCase )
recursively_load_weights(fairseq_checkpoint['''model'''], _lowerCAmelCase, _lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if repo_id:
print('''Pushing to the hub...''' )
processor.push_to_hub(_lowerCAmelCase )
model.push_to_hub(_lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
) | 320 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : Optional[Any] = WavaVecaForSequenceClassification.from_pretrained(_lowerCAmelCase,config=_lowerCAmelCase )
_A : Dict = downstream_dict["""projector.weight"""]
_A : Any = downstream_dict["""projector.bias"""]
_A : int = downstream_dict["""model.post_net.linear.weight"""]
_A : Any = downstream_dict["""model.post_net.linear.bias"""]
return model
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : List[Any] = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCAmelCase,config=_lowerCAmelCase )
_A : str = downstream_dict["""model.linear.weight"""]
_A : Union[str, Any] = downstream_dict["""model.linear.bias"""]
return model
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : Optional[int] = WavaVecaForXVector.from_pretrained(_lowerCAmelCase,config=_lowerCAmelCase )
_A : str = downstream_dict["""connector.weight"""]
_A : Dict = downstream_dict["""connector.bias"""]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_A : Optional[int] = downstream_dict[
f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_A : int = downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_A : Tuple = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
_A : Any = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
_A : List[str] = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
_A : str = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
_A : Dict = downstream_dict["""objective.W"""]
return model
@torch.no_grad()
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
_A : int = torch.load(_lowerCAmelCase,map_location="""cpu""" )
_A : Dict = checkpoint["""Downstream"""]
_A : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
_A : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
_lowerCAmelCase,return_attention_mask=_lowerCAmelCase,do_normalize=_lowerCAmelCase )
_A : Tuple = hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
_A : Tuple = convert_classification(_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase )
elif arch.endswith("""ForAudioFrameClassification""" ):
_A : str = convert_diarization(_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase )
elif arch.endswith("""ForXVector""" ):
_A : List[str] = convert_xvector(_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase )
else:
raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_A : Optional[int] = checkpoint["""Featurizer"""]["""weights"""]
hf_feature_extractor.save_pretrained(_lowerCAmelCase )
hf_model.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
_snake_case = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 26 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[Any] = 'decision_transformer'
A_ : Union[str, Any] = ['past_key_values']
A_ : str = {
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , __UpperCAmelCase=17 , __UpperCAmelCase=4 , __UpperCAmelCase=128 , __UpperCAmelCase=4096 , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=1024 , __UpperCAmelCase=3 , __UpperCAmelCase=1 , __UpperCAmelCase=None , __UpperCAmelCase="relu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=0.02 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=50256 , __UpperCAmelCase=50256 , __UpperCAmelCase=False , __UpperCAmelCase=False , **__UpperCAmelCase , ) -> Optional[int]:
_a = state_dim
_a = act_dim
_a = hidden_size
_a = max_ep_len
_a = action_tanh
_a = vocab_size
_a = n_positions
_a = n_layer
_a = n_head
_a = n_inner
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = scale_attn_weights
_a = use_cache
_a = scale_attn_by_inverse_layer_idx
_a = reorder_and_upcast_attn
_a = bos_token_id
_a = eos_token_id
super().__init__(bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase ) | 320 | 0 |
'''simple docstring'''
import os
import sys
import unittest
__lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__lowerCAmelCase = os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
__lowerCAmelCase = os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')
class __magic_name__ ( unittest.TestCase ):
def __lowercase ( self : List[str] ):
_a : int = get_test_to_tester_mapping(__UpperCAmelCase )
_a : List[Any] = get_test_to_tester_mapping(__UpperCAmelCase )
_a : Dict = {'BertModelTest': 'BertModelTester'}
_a : List[Any] = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) ,__UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) ,__UpperCAmelCase )
def __lowercase ( self : str ):
_a : List[Any] = get_model_to_test_mapping(__UpperCAmelCase )
_a : Union[str, Any] = get_model_to_test_mapping(__UpperCAmelCase )
_a : List[Any] = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
_a : List[Any] = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) ,__UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) ,__UpperCAmelCase )
def __lowercase ( self : Tuple ):
_a : Tuple = get_model_to_tester_mapping(__UpperCAmelCase )
_a : Union[str, Any] = get_model_to_tester_mapping(__UpperCAmelCase )
_a : str = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
_a : int = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) ,__UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) ,__UpperCAmelCase )
| 89 |
"""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
__snake_case = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[str] = ['pixel_values']
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ) -> None:
super().__init__(**__UpperCAmelCase )
_a = size if size is not None else {'''shortest_edge''': 224}
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
_a = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
_a = do_resize
_a = size
_a = resample
_a = do_center_crop
_a = crop_size
_a = do_rescale
_a = rescale_factor
_a = do_normalize
_a = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_a = image_std if image_std is not None else OPENAI_CLIP_STD
_a = do_convert_rgb
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
_a = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
_a = get_size_dict(__UpperCAmelCase )
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(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> Optional[Any]:
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ) -> PIL.Image.Image:
_a = do_resize if do_resize is not None else self.do_resize
_a = size if size is not None else self.size
_a = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
_a = resample if resample is not None else self.resample
_a = do_center_crop if do_center_crop is not None else self.do_center_crop
_a = crop_size if crop_size is not None else self.crop_size
_a = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
_a = do_rescale if do_rescale is not None else self.do_rescale
_a = rescale_factor if rescale_factor is not None else self.rescale_factor
_a = do_normalize if do_normalize is not None else self.do_normalize
_a = image_mean if image_mean is not None else self.image_mean
_a = image_std if image_std is not None else self.image_std
_a = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_a = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
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 = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
_a = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
_a = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
_a = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
_a = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
_a = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
_a = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
_a = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase ) | 320 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
__lowerCAmelCase : Any =[
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
__lowerCAmelCase : Optional[int] ='UperNetConfig'
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Tuple , lowerCAmelCase__ :Any , lowerCAmelCase__ :str , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tuple = 0 , lowerCAmelCase__ :Union[str, Any] = False , lowerCAmelCase__ :Any = 1 , ) -> None:
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Convad(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=__UpperCAmelCase , padding=__UpperCAmelCase , bias=__UpperCAmelCase , dilation=__UpperCAmelCase , )
__SCREAMING_SNAKE_CASE : Dict = nn.BatchNormad(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.ReLU()
def __magic_name__( self :Tuple , lowerCAmelCase__ :List[str] ) -> torch.Tensor:
__SCREAMING_SNAKE_CASE : List[str] = self.conv(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.batch_norm(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Any = self.activation(__UpperCAmelCase )
return output
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :List[str] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :str ) -> None:
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = [
nn.AdaptiveAvgPoolad(__UpperCAmelCase ),
UperNetConvModule(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(__UpperCAmelCase ) , __UpperCAmelCase )
def __magic_name__( self :Optional[Any] , lowerCAmelCase__ :Dict ) -> torch.Tensor:
__SCREAMING_SNAKE_CASE : Any = input
for layer in self.layers:
__SCREAMING_SNAKE_CASE : Any = layer(__UpperCAmelCase )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str , lowerCAmelCase__ :Any , lowerCAmelCase__ :Optional[Any] ) -> None:
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[int] = pool_scales
__SCREAMING_SNAKE_CASE : str = align_corners
__SCREAMING_SNAKE_CASE : Optional[int] = in_channels
__SCREAMING_SNAKE_CASE : Tuple = channels
__SCREAMING_SNAKE_CASE : str = []
for i, pool_scale in enumerate(__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = UperNetPyramidPoolingBlock(pool_scale=__UpperCAmelCase , in_channels=__UpperCAmelCase , channels=__UpperCAmelCase )
self.blocks.append(__UpperCAmelCase )
self.add_module(str(__UpperCAmelCase ) , __UpperCAmelCase )
def __magic_name__( self :Tuple , lowerCAmelCase__ :Optional[int] ) -> List[torch.Tensor]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = []
for ppm in self.blocks:
__SCREAMING_SNAKE_CASE : int = ppm(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : int = nn.functional.interpolate(
__UpperCAmelCase , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(__UpperCAmelCase )
return ppm_outs
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :int , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any] ) -> Tuple:
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = config
__SCREAMING_SNAKE_CASE : List[str] = config.pool_scales # e.g. (1, 2, 3, 6)
__SCREAMING_SNAKE_CASE : Optional[Any] = in_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = config.hidden_size
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
__SCREAMING_SNAKE_CASE : Union[str, Any] = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
__SCREAMING_SNAKE_CASE : int = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
__SCREAMING_SNAKE_CASE : List[Any] = nn.ModuleList()
__SCREAMING_SNAKE_CASE : List[Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
__SCREAMING_SNAKE_CASE : Optional[Any] = UperNetConvModule(__UpperCAmelCase , self.channels , kernel_size=1 )
__SCREAMING_SNAKE_CASE : Optional[Any] = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(__UpperCAmelCase )
self.fpn_convs.append(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Dict = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def __magic_name__( self :Any ) -> Tuple:
self.apply(self._init_weights )
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :Union[str, Any] ) -> Optional[int]:
if isinstance(__UpperCAmelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def __magic_name__( self :int , lowerCAmelCase__ :Any ) -> Dict:
__SCREAMING_SNAKE_CASE : Optional[int] = inputs[-1]
__SCREAMING_SNAKE_CASE : List[Any] = [x]
psp_outs.extend(self.psp_modules(__UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE : int = torch.cat(__UpperCAmelCase , dim=1 )
__SCREAMING_SNAKE_CASE : List[str] = self.bottleneck(__UpperCAmelCase )
return output
def __magic_name__( self :int , lowerCAmelCase__ :Tuple ) -> torch.Tensor:
# build laterals
__SCREAMING_SNAKE_CASE : Optional[int] = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(__UpperCAmelCase ) )
# build top-down path
__SCREAMING_SNAKE_CASE : List[str] = len(__UpperCAmelCase )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = laterals[i - 1].shape[2:]
__SCREAMING_SNAKE_CASE : Tuple = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=__UpperCAmelCase , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
__SCREAMING_SNAKE_CASE : str = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
__SCREAMING_SNAKE_CASE : str = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
__SCREAMING_SNAKE_CASE : str = torch.cat(__UpperCAmelCase , dim=1 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.fpn_bottleneck(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[Any] = self.classifier(__UpperCAmelCase )
return output
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Dict = 2 , lowerCAmelCase__ :List[str] = 3 , lowerCAmelCase__ :str = 1 ) -> None:
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[int] = config
__SCREAMING_SNAKE_CASE : Dict = config.auxiliary_in_channels
__SCREAMING_SNAKE_CASE : List[Any] = config.auxiliary_channels
__SCREAMING_SNAKE_CASE : List[str] = config.auxiliary_num_convs
__SCREAMING_SNAKE_CASE : Dict = config.auxiliary_concat_input
__SCREAMING_SNAKE_CASE : Any = in_index
__SCREAMING_SNAKE_CASE : Tuple = (kernel_size // 2) * dilation
__SCREAMING_SNAKE_CASE : str = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=__UpperCAmelCase , padding=__UpperCAmelCase , dilation=__UpperCAmelCase ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=__UpperCAmelCase , padding=__UpperCAmelCase , dilation=__UpperCAmelCase ) )
if self.num_convs == 0:
__SCREAMING_SNAKE_CASE : List[str] = nn.Identity()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Sequential(*__UpperCAmelCase )
if self.concat_input:
__SCREAMING_SNAKE_CASE : List[Any] = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=__UpperCAmelCase , padding=kernel_size // 2 )
__SCREAMING_SNAKE_CASE : List[str] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def __magic_name__( self :Optional[Any] ) -> Optional[Any]:
self.apply(self._init_weights )
def __magic_name__( self :List[str] , lowerCAmelCase__ :Tuple ) -> Union[str, Any]:
if isinstance(__UpperCAmelCase , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :Optional[int] ) -> torch.Tensor:
# just take the relevant feature maps
__SCREAMING_SNAKE_CASE : Optional[Any] = encoder_hidden_states[self.in_index]
__SCREAMING_SNAKE_CASE : Any = self.convs(__UpperCAmelCase )
if self.concat_input:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
__SCREAMING_SNAKE_CASE : int = self.classifier(__UpperCAmelCase )
return output
class _lowercase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = UperNetConfig
SCREAMING_SNAKE_CASE__ : Tuple = 'pixel_values'
SCREAMING_SNAKE_CASE__ : int = True
def __magic_name__( self :Union[str, Any] , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def __magic_name__( self :List[str] ) -> List[str]:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def __magic_name__( self :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str=False ) -> List[str]:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : List[str] = value
__lowerCAmelCase : Optional[int] =r'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowerCAmelCase : Optional[int] =r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , a__ , )
class _lowercase ( a__ ):
'''simple docstring'''
def __init__( self :Optional[Any] , lowerCAmelCase__ :str ) -> Optional[int]:
super().__init__(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
__SCREAMING_SNAKE_CASE : Any = UperNetHead(__UpperCAmelCase , in_channels=self.backbone.channels )
__SCREAMING_SNAKE_CASE : List[Any] = UperNetFCNHead(__UpperCAmelCase ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC )
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :Union[str, Any] = None , lowerCAmelCase__ :List[str] = None , lowerCAmelCase__ :List[Any] = None , lowerCAmelCase__ :Optional[Any] = None , lowerCAmelCase__ :Any = None , ) -> Union[tuple, SemanticSegmenterOutput]:
__SCREAMING_SNAKE_CASE : int = return_dict if return_dict is not None else self.config.use_return_dict
__SCREAMING_SNAKE_CASE : Tuple = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__SCREAMING_SNAKE_CASE : List[Any] = output_attentions if output_attentions is not None else self.config.output_attentions
__SCREAMING_SNAKE_CASE : Any = self.backbone.forward_with_filtered_kwargs(
__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , output_attentions=__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[Any] = outputs.feature_maps
__SCREAMING_SNAKE_CASE : str = self.decode_head(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[Any] = nn.functional.interpolate(__UpperCAmelCase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = None
if self.auxiliary_head is not None:
__SCREAMING_SNAKE_CASE : int = self.auxiliary_head(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.functional.interpolate(
__UpperCAmelCase , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
__SCREAMING_SNAKE_CASE : str = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
__SCREAMING_SNAKE_CASE : List[Any] = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Tuple = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[str] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
__SCREAMING_SNAKE_CASE : Dict = (logits,) + outputs[1:]
else:
__SCREAMING_SNAKE_CASE : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=__UpperCAmelCase , logits=__UpperCAmelCase , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 9 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 320 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase__ = 637_8137.0
UpperCAmelCase__ = 635_6752.31_4245
UpperCAmelCase__ = 637_8137
def A ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
_UpperCAmelCase = atan((1 - flattening) * tan(radians(_lowerCAmelCase ) ) )
_UpperCAmelCase = atan((1 - flattening) * tan(radians(_lowerCAmelCase ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
_UpperCAmelCase = haversine_distance(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
_UpperCAmelCase = (b_lata + b_lata) / 2
_UpperCAmelCase = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
_UpperCAmelCase = (sin(_lowerCAmelCase ) ** 2) * (cos(_lowerCAmelCase ) ** 2)
_UpperCAmelCase = cos(sigma / 2 ) ** 2
_UpperCAmelCase = (sigma - sin(_lowerCAmelCase )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
_UpperCAmelCase = (cos(_lowerCAmelCase ) ** 2) * (sin(_lowerCAmelCase ) ** 2)
_UpperCAmelCase = sin(sigma / 2 ) ** 2
_UpperCAmelCase = (sigma + sin(_lowerCAmelCase )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
__snake_case = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def A_ ( ):
"""simple docstring"""
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, bootstrap_aggregation=_lowerCAmelCase, rouge_keys=['''rouge2''', '''rougeL'''] )
assert isinstance(_lowerCAmelCase, _lowerCAmelCase )
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, bootstrap_aggregation=_lowerCAmelCase, rouge_keys=['''rouge2'''] )
assert (
pd.DataFrame(no_aggregation['''rouge2'''] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['''rouge2'''] ).fmeasure.mean()
)
def A_ ( ):
"""simple docstring"""
_a = '''rougeLsum'''
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=[k] )[k]
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=[k] )[k]
assert score > score_no_sep
def A_ ( ):
"""simple docstring"""
_a = ['''rouge1''', '''rouge2''', '''rougeL''']
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=_lowerCAmelCase )
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=_lowerCAmelCase )
assert score_sep == score_no_sep
def A_ ( ):
"""simple docstring"""
_a = [
'''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
]
_a = [
'''Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
''' the final seconds on board Flight 9525.''',
]
assert calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase ) == calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_a = [
'''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
]
_a = [
''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
]
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, rouge_keys=['''rougeLsum'''], newline_sep=_lowerCAmelCase )['''rougeLsum''']
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, rouge_keys=['''rougeLsum'''] )['''rougeLsum''']
assert new_score > prev_score
def A_ ( ):
"""simple docstring"""
_a = Path('''examples/seq2seq/test_data/wmt_en_ro''' )
_a = calculate_rouge_path(data_dir.joinpath('''test.source''' ), data_dir.joinpath('''test.target''' ) )
assert isinstance(_lowerCAmelCase, _lowerCAmelCase )
_a = calculate_rouge_path(
data_dir.joinpath('''test.source''' ), data_dir.joinpath('''test.target''' ), bootstrap_aggregation=_lowerCAmelCase )
assert isinstance(_lowerCAmelCase, _lowerCAmelCase ) | 320 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_lowerCAmelCase :List[str] = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
# See all BART models at https://huggingface.co/models?filter=bart
}
class _UpperCAmelCase ( a__ ):
'''simple docstring'''
a__ ='bart'
a__ =['past_key_values']
a__ ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=5_0_2_6_5 , A=1_0_2_4 , A=1_2 , A=4_0_9_6 , A=1_6 , A=1_2 , A=4_0_9_6 , A=1_6 , A=0.0 , A=0.0 , A="gelu" , A=1_0_2_4 , A=0.1 , A=0.0 , A=0.0 , A=0.02 , A=0.0 , A=False , A=True , A=3 , A=1 , A=0 , A=2 , A=True , A=2 , A=2 , **A , ) -> Tuple:
_UpperCAmelCase : Optional[Any] = vocab_size
_UpperCAmelCase : List[str] = max_position_embeddings
_UpperCAmelCase : Optional[int] = d_model
_UpperCAmelCase : Optional[Any] = encoder_ffn_dim
_UpperCAmelCase : Optional[Any] = encoder_layers
_UpperCAmelCase : List[str] = encoder_attention_heads
_UpperCAmelCase : List[str] = decoder_ffn_dim
_UpperCAmelCase : Optional[int] = decoder_layers
_UpperCAmelCase : Any = decoder_attention_heads
_UpperCAmelCase : List[str] = dropout
_UpperCAmelCase : Tuple = attention_dropout
_UpperCAmelCase : int = activation_dropout
_UpperCAmelCase : Tuple = activation_function
_UpperCAmelCase : Dict = init_std
_UpperCAmelCase : Tuple = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : str = classifier_dropout
_UpperCAmelCase : Optional[int] = use_cache
_UpperCAmelCase : int = encoder_layers
_UpperCAmelCase : str = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , is_encoder_decoder=__UpperCAmelCase , decoder_start_token_id=__UpperCAmelCase , forced_eos_token_id=__UpperCAmelCase , **__UpperCAmelCase , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , __UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'''The config can simply be saved and uploaded again to be fixed.''' )
class _UpperCAmelCase ( a__ ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase : Dict = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_UpperCAmelCase : Any = {0: '''batch'''}
_UpperCAmelCase : Tuple = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_UpperCAmelCase : List[Any] = {0: '''batch''', 1: '''decoder_sequence'''}
_UpperCAmelCase : List[str] = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
_UpperCAmelCase : Tuple = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_UpperCAmelCase , _UpperCAmelCase : str = self.num_layers
for i in range(__UpperCAmelCase ):
_UpperCAmelCase : int = {0: '''batch''', 2: '''past_sequence + sequence'''}
_UpperCAmelCase : Dict = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
_UpperCAmelCase : Any = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase : int = super().outputs
else:
_UpperCAmelCase : int = super(__UpperCAmelCase , self ).outputs
if self.use_past:
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.num_layers
for i in range(__UpperCAmelCase ):
_UpperCAmelCase : List[Any] = {0: '''batch''', 2: '''past_sequence + sequence'''}
_UpperCAmelCase : Optional[int] = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def __lowerCAmelCase ( self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
_UpperCAmelCase : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Generate decoder inputs
_UpperCAmelCase : List[Any] = seq_length if not self.use_past else 1
_UpperCAmelCase : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_UpperCAmelCase : Any = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_UpperCAmelCase : Tuple = dict(**__UpperCAmelCase , **__UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_UpperCAmelCase , _UpperCAmelCase : int = common_inputs['''input_ids'''].shape
_UpperCAmelCase : List[str] = common_inputs['''decoder_input_ids'''].shape[1]
_UpperCAmelCase , _UpperCAmelCase : Dict = self.num_attention_heads
_UpperCAmelCase : Tuple = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_UpperCAmelCase : Dict = decoder_seq_length + 3
_UpperCAmelCase : List[str] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_UpperCAmelCase : Tuple = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase )] , dim=1 )
_UpperCAmelCase : Optional[int] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.num_layers
_UpperCAmelCase : Any = min(__UpperCAmelCase , __UpperCAmelCase )
_UpperCAmelCase : List[str] = max(__UpperCAmelCase , __UpperCAmelCase ) - min_num_layers
_UpperCAmelCase : Tuple = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__UpperCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
) )
# TODO: test this.
_UpperCAmelCase : Optional[int] = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__UpperCAmelCase , __UpperCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) )
return common_inputs
def __lowerCAmelCase ( self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
_UpperCAmelCase : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_UpperCAmelCase , _UpperCAmelCase : Tuple = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_UpperCAmelCase : List[Any] = seqlen + 2
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.num_layers
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.num_attention_heads
_UpperCAmelCase : List[Any] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_UpperCAmelCase : Optional[int] = common_inputs['''attention_mask'''].dtype
_UpperCAmelCase : List[str] = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
_UpperCAmelCase : Tuple = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(__UpperCAmelCase )
]
return common_inputs
def __lowerCAmelCase ( self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase : Tuple = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_UpperCAmelCase : Tuple = tokenizer.num_special_tokens_to_add(__UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__UpperCAmelCase )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase : Any = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
_UpperCAmelCase : Dict = dict(tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase ) )
return common_inputs
def __lowerCAmelCase ( self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase : List[Any] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
elif self.task == "causal-lm":
_UpperCAmelCase : Union[str, Any] = self._generate_dummy_inputs_for_causal_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
else:
_UpperCAmelCase : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
return common_inputs
def __lowerCAmelCase ( self , A , A , A , A ) -> Optional[Any]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase : str = super()._flatten_past_key_values_(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
_UpperCAmelCase : Optional[Any] = super(__UpperCAmelCase , self )._flatten_past_key_values_(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
| 263 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase ) | 320 | 0 |
'''simple docstring'''
import argparse
import json
import subprocess
def __a(SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ):
'''simple docstring'''
_lowerCAmelCase = []
_lowerCAmelCase = (
F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_lowerCAmelCase = subprocess.run(_lowerCAmelCase , shell=_lowerCAmelCase , stdout=subprocess.PIPE )
_lowerCAmelCase = output.stdout.decode("utf-8" )
_lowerCAmelCase = json.loads(_lowerCAmelCase )
_lowerCAmelCase = status["runners"]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(_lowerCAmelCase )
# save the result so we can report them on Slack
with open("offline_runners.txt" , "w" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) )
if len(_lowerCAmelCase ) > 0:
_lowerCAmelCase = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(F'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def __a(SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
return values.split("," )
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--target_runners",
default=None,
type=list_str,
required=True,
help="Comma-separated list of runners to check status.",
)
parser.add_argument(
"--token", default=None, type=str, required=True, help="A token that has actions:read permission."
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 158 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 320 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class a__ :
@staticmethod
def _lowerCamelCase ( *_UpperCamelCase , **_UpperCamelCase ):
"""simple docstring"""
pass
def _A ( snake_case ) -> List[Any]:
_lowercase : Dict = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def _A ( snake_case ) -> int:
_lowercase : int = np.array(_lowerCAmelCase )
_lowercase : str = npimg.shape
return {"hash": hashimage(_lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class a__ ( unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Any = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
_SCREAMING_SNAKE_CASE : str = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Optional[int] = MaskGenerationPipeline(model=__UpperCAmelCase , image_processor=__UpperCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def _lowerCamelCase ( self ):
"""simple docstring"""
pass
@slow
@require_torch
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
_lowercase : Optional[Any] = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
_lowercase : Union[str, Any] = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : str = "facebook/sam-vit-huge"
_lowercase : Optional[int] = pipeline("mask-generation" , model=__UpperCAmelCase )
_lowercase : List[Any] = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
_lowercase : Tuple = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
] , )
| 250 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[9_99] )
* int(constant[99_99] )
* int(constant[9_99_99] )
* int(constant[99_99_99] )
)
if __name__ == "__main__":
print(solution()) | 320 | 0 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
def UpperCAmelCase ( a_ , a_ ) -> List[str]:
"""simple docstring"""
A_ : Optional[int] = nn.functional.normalize(_lowerCAmelCase )
A_ : Tuple = nn.functional.normalize(_lowerCAmelCase )
return torch.mm(_lowerCAmelCase , normalized_text_embeds.t() )
class _lowerCAmelCase ( a__ ):
"""simple docstring"""
lowerCamelCase = CLIPConfig
lowerCamelCase = ['CLIPEncoderLayer']
def __init__( self , _lowerCamelCase ) -> Optional[Any]:
super().__init__(__UpperCAmelCase )
A_ : Optional[Any] = CLIPVisionModel(config.vision_config )
A_ : Tuple = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=__UpperCAmelCase )
A_ : str = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=__UpperCAmelCase )
A_ : List[str] = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=__UpperCAmelCase )
A_ : Optional[int] = nn.Parameter(torch.ones(17 ) , requires_grad=__UpperCAmelCase )
A_ : Optional[Any] = nn.Parameter(torch.ones(3 ) , requires_grad=__UpperCAmelCase )
@torch.no_grad()
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> int:
A_ : List[Any] = self.vision_model(__UpperCAmelCase )[1] # pooled_output
A_ : int = self.visual_projection(__UpperCAmelCase )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A_ : Any = cosine_distance(__UpperCAmelCase , self.special_care_embeds ).cpu().float().numpy()
A_ : Optional[Any] = cosine_distance(__UpperCAmelCase , self.concept_embeds ).cpu().float().numpy()
A_ : Optional[Any] = []
A_ : str = image_embeds.shape[0]
for i in range(__UpperCAmelCase ):
A_ : Optional[int] = {"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
A_ : Tuple = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
A_ : Tuple = special_cos_dist[i][concept_idx]
A_ : int = self.special_care_embeds_weights[concept_idx].item()
A_ : Any = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]} )
A_ : int = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
A_ : int = cos_dist[i][concept_idx]
A_ : List[str] = self.concept_embeds_weights[concept_idx].item()
A_ : List[Any] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(__UpperCAmelCase )
result.append(__UpperCAmelCase )
A_ : Optional[Any] = [len(res["""bad_concepts"""] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> Union[str, Any]:
A_ : Optional[int] = self.vision_model(__UpperCAmelCase )[1] # pooled_output
A_ : Any = self.visual_projection(__UpperCAmelCase )
A_ : int = cosine_distance(__UpperCAmelCase , self.special_care_embeds )
A_ : Dict = cosine_distance(__UpperCAmelCase , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
A_ : Optional[Any] = 0.0
A_ : Dict = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
A_ : Union[str, Any] = torch.any(special_scores > 0 , dim=1 )
A_ : Tuple = special_care * 0.01
A_ : Tuple = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
A_ : List[str] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
A_ : Optional[int] = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 344 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
# See all BART models at https://huggingface.co/models?filter=bart
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[str] = 'bart'
A_ : Optional[Any] = ['past_key_values']
A_ : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , __UpperCAmelCase=50265 , __UpperCAmelCase=1024 , __UpperCAmelCase=12 , __UpperCAmelCase=4096 , __UpperCAmelCase=16 , __UpperCAmelCase=12 , __UpperCAmelCase=4096 , __UpperCAmelCase=16 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=1024 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.02 , __UpperCAmelCase=0.0 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=3 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=2 , __UpperCAmelCase=2 , **__UpperCAmelCase , ) -> Tuple:
_a = vocab_size
_a = max_position_embeddings
_a = d_model
_a = encoder_ffn_dim
_a = encoder_layers
_a = encoder_attention_heads
_a = decoder_ffn_dim
_a = decoder_layers
_a = decoder_attention_heads
_a = dropout
_a = attention_dropout
_a = activation_dropout
_a = activation_function
_a = init_std
_a = encoder_layerdrop
_a = decoder_layerdrop
_a = classifier_dropout
_a = use_cache
_a = encoder_layers
_a = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , is_encoder_decoder=__UpperCAmelCase , decoder_start_token_id=__UpperCAmelCase , forced_eos_token_id=__UpperCAmelCase , **__UpperCAmelCase , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , __UpperCAmelCase ):
_a = self.bos_token_id
warnings.warn(
F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'''The config can simply be saved and uploaded again to be fixed.''' )
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_a = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_a = {0: '''batch'''}
_a = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''decoder_sequence'''}
_a = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
_a = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_a , _a = self.num_layers
for i in range(__UpperCAmelCase ):
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
_a = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_a = super().outputs
else:
_a = super(__UpperCAmelCase , self ).outputs
if self.use_past:
_a , _a = self.num_layers
for i in range(__UpperCAmelCase ):
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Generate decoder inputs
_a = seq_length if not self.use_past else 1
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_a = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_a = dict(**__UpperCAmelCase , **__UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
_a = common_inputs['''decoder_input_ids'''].shape[1]
_a , _a = self.num_attention_heads
_a = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_a = decoder_seq_length + 3
_a = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_a = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase )] , dim=1 )
_a = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_a , _a = self.num_layers
_a = min(__UpperCAmelCase , __UpperCAmelCase )
_a = max(__UpperCAmelCase , __UpperCAmelCase ) - min_num_layers
_a = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__UpperCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
) )
# TODO: test this.
_a = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__UpperCAmelCase , __UpperCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) )
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_a = seqlen + 2
_a , _a = self.num_layers
_a , _a = self.num_attention_heads
_a = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_a = common_inputs['''attention_mask'''].dtype
_a = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
_a = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(__UpperCAmelCase )
]
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_a = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_a = tokenizer.num_special_tokens_to_add(__UpperCAmelCase )
_a = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__UpperCAmelCase )
# Generate dummy inputs according to compute batch and sequence
_a = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
_a = dict(tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase ) )
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_a = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
elif self.task == "causal-lm":
_a = self._generate_dummy_inputs_for_causal_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
else:
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
if self.task in ["default", "seq2seq-lm"]:
_a = super()._flatten_past_key_values_(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
_a = super(__UpperCAmelCase , self )._flatten_past_key_values_(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) | 320 | 0 |
def lowerCamelCase_ ( _a : int = 1000 ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = -1
UpperCAmelCase_ : Optional[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCAmelCase_ : Tuple = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCAmelCase_ : int = n - a - b
if c * c == (a * a + b * b):
UpperCAmelCase_ : Optional[Any] = a * b * c
if candidate >= product:
UpperCAmelCase_ : Optional[Any] = candidate
return product
if __name__ == "__main__":
print(F"{solution() = }")
| 345 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x2_0000 and cp <= 0x2_a6df) #
or (cp >= 0x2_a700 and cp <= 0x2_b73f) #
or (cp >= 0x2_b740 and cp <= 0x2_b81f) #
or (cp >= 0x2_b820 and cp <= 0x2_ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2_f800 and cp <= 0x2_fa1f) #
): #
return True
return False
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
for char in word:
_a = ord(_lowerCAmelCase )
if not _is_chinese_char(_lowerCAmelCase ):
return 0
return 1
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_a = set()
for token in tokens:
_a = len(_lowerCAmelCase ) > 1 and is_chinese(_lowerCAmelCase )
if chinese_word:
word_set.add(_lowerCAmelCase )
_a = list(_lowerCAmelCase )
return word_list
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : set() ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_a = max([len(_lowerCAmelCase ) for w in chinese_word_set] )
_a = bert_tokens
_a , _a = 0, len(_lowerCAmelCase )
while start < end:
_a = True
if is_chinese(bert_word[start] ):
_a = min(end - start, _lowerCAmelCase )
for i in range(_lowerCAmelCase, 1, -1 ):
_a = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1, start + i ):
_a = '''##''' + bert_word[j]
_a = start + i
_a = False
break
if single_word:
start += 1
return bert_word
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : LTP, _lowerCAmelCase : BertTokenizer ):
"""simple docstring"""
_a = []
for i in range(0, len(_lowerCAmelCase ), 1_00 ):
_a = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws
_a = [get_chinese_word(_lowerCAmelCase ) for r in res]
ltp_res.extend(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
_a = []
for i in range(0, len(_lowerCAmelCase ), 1_00 ):
_a = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=_lowerCAmelCase, truncation=_lowerCAmelCase, max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
_a = []
for input_ids, chinese_word in zip(_lowerCAmelCase, _lowerCAmelCase ):
_a = []
for id in input_ids:
_a = bert_tokenizer._convert_id_to_token(_lowerCAmelCase )
input_tokens.append(_lowerCAmelCase )
_a = add_sub_symbol(_lowerCAmelCase, _lowerCAmelCase )
_a = []
# 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 = 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 A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
with open(args.file_name, '''r''', encoding='''utf-8''' ) as f:
_a = f.readlines()
_a = [line.strip() for line in data if len(_lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_a = LTP(args.ltp ) # faster in GPU device
_a = BertTokenizer.from_pretrained(args.bert )
_a = prepare_ref(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
with open(args.save_path, '''w''', encoding='''utf-8''' ) as f:
_a = [json.dumps(_lowerCAmelCase ) + '''\n''' for ref in ref_ids]
f.writelines(_lowerCAmelCase )
if __name__ == "__main__":
__snake_case = 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''',
)
__snake_case = parser.parse_args()
main(args) | 320 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class UpperCamelCase ( a__ ):
def __init__( self, *lowerCAmelCase__, **lowerCAmelCase__) -> None:
warnings.warn(
'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DPTImageProcessor instead.', __UpperCAmelCase, )
super().__init__(*__UpperCAmelCase, **__UpperCAmelCase)
| 69 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[Any] = 'gptj'
A_ : Optional[int] = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , __UpperCAmelCase=50400 , __UpperCAmelCase=2048 , __UpperCAmelCase=4096 , __UpperCAmelCase=28 , __UpperCAmelCase=16 , __UpperCAmelCase=64 , __UpperCAmelCase=None , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=0.02 , __UpperCAmelCase=True , __UpperCAmelCase=50256 , __UpperCAmelCase=50256 , __UpperCAmelCase=False , **__UpperCAmelCase , ) -> Union[str, Any]:
_a = vocab_size
_a = n_positions
_a = n_embd
_a = n_layer
_a = n_head
_a = n_inner
_a = rotary_dim
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = use_cache
_a = bos_token_id
_a = eos_token_id
super().__init__(
bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , tie_word_embeddings=__UpperCAmelCase , **__UpperCAmelCase )
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = "default" , __UpperCAmelCase = None , __UpperCAmelCase = False , ) -> Optional[Any]:
super().__init__(__UpperCAmelCase , task=__UpperCAmelCase , patching_specs=__UpperCAmelCase , use_past=__UpperCAmelCase )
if not getattr(self._config , '''pad_token_id''' , __UpperCAmelCase ):
# TODO: how to do that better?
_a = 0
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
_a = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='''inputs''' )
_a = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def _UpperCAmelCase ( self ) -> int:
return self._config.n_layer
@property
def _UpperCAmelCase ( self ) -> int:
return self._config.n_head
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
_a = super(__UpperCAmelCase , self ).generate_dummy_inputs(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
_a = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_a = seqlen + 2
_a = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_a = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(self.num_layers )
]
_a = common_inputs['''attention_mask''']
if self.use_past:
_a = ordered_inputs['''attention_mask'''].dtype
_a = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def _UpperCAmelCase ( self ) -> int:
return 13 | 320 | 0 |
import re
def UpperCAmelCase__ ( _A : str ):
'''simple docstring'''
a__ =re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(_lowerCAmelCase , _lowerCAmelCase ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator('''+918827897895'''))
| 188 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__snake_case = os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
__snake_case = os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> str:
_a = get_test_to_tester_mapping(__UpperCAmelCase )
_a = get_test_to_tester_mapping(__UpperCAmelCase )
_a = {'''BertModelTest''': '''BertModelTester'''}
_a = {
'''BlipModelTest''': '''BlipModelTester''',
'''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
'''BlipTextModelTest''': '''BlipTextModelTester''',
'''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
'''BlipVQAModelTest''': '''BlipVQAModelTester''',
'''BlipVisionModelTest''': '''BlipVisionModelTester''',
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = get_model_to_test_mapping(__UpperCAmelCase )
_a = get_model_to_test_mapping(__UpperCAmelCase )
_a = {
'''BertForMaskedLM''': ['''BertModelTest'''],
'''BertForMultipleChoice''': ['''BertModelTest'''],
'''BertForNextSentencePrediction''': ['''BertModelTest'''],
'''BertForPreTraining''': ['''BertModelTest'''],
'''BertForQuestionAnswering''': ['''BertModelTest'''],
'''BertForSequenceClassification''': ['''BertModelTest'''],
'''BertForTokenClassification''': ['''BertModelTest'''],
'''BertLMHeadModel''': ['''BertModelTest'''],
'''BertModel''': ['''BertModelTest'''],
}
_a = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
'''BlipModel''': ['''BlipModelTest'''],
'''BlipTextModel''': ['''BlipTextModelTest'''],
'''BlipVisionModel''': ['''BlipVisionModelTest'''],
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = get_model_to_tester_mapping(__UpperCAmelCase )
_a = get_model_to_tester_mapping(__UpperCAmelCase )
_a = {
'''BertForMaskedLM''': ['''BertModelTester'''],
'''BertForMultipleChoice''': ['''BertModelTester'''],
'''BertForNextSentencePrediction''': ['''BertModelTester'''],
'''BertForPreTraining''': ['''BertModelTester'''],
'''BertForQuestionAnswering''': ['''BertModelTester'''],
'''BertForSequenceClassification''': ['''BertModelTester'''],
'''BertForTokenClassification''': ['''BertModelTester'''],
'''BertLMHeadModel''': ['''BertModelTester'''],
'''BertModel''': ['''BertModelTester'''],
}
_a = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
'''BlipModel''': ['''BlipModelTester'''],
'''BlipTextModel''': ['''BlipTextModelTester'''],
'''BlipVisionModel''': ['''BlipVisionModelTester'''],
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase ) | 320 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
if isinstance(_lowerCAmelCase,_lowerCAmelCase ):
raise TypeError("""\'float\' object cannot be interpreted as an integer""" )
if isinstance(_lowerCAmelCase,_lowerCAmelCase ):
raise TypeError("""\'str\' object cannot be interpreted as an integer""" )
if num == 0:
return "0b0"
_A : Optional[Any] = False
if num < 0:
_A : List[str] = True
_A : Optional[Any] = -num
_A : Dict = []
while num > 0:
binary.insert(0,num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(_lowerCAmelCase ) for e in binary )
return "0b" + "".join(str(_lowerCAmelCase ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 26 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __lowerCamelCase :
'''simple docstring'''
@staticmethod
def _UpperCAmelCase ( *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
pass
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_a = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_a = np.array(_lowerCAmelCase )
_a = npimg.shape
return {"hash": hashimage(_lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
A_ : Any = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
A_ : str = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
_a = MaskGenerationPipeline(model=__UpperCAmelCase , image_processor=__UpperCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> int:
pass
@require_tf
@unittest.skip('''Image segmentation not implemented in TF''' )
def _UpperCAmelCase ( self ) -> List[str]:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> int:
_a = pipeline('''mask-generation''' , model='''facebook/sam-vit-huge''' )
_a = image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg''' , points_per_batch=256 )
# Shortening by hashing
_a = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
{'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9967},
{'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
{'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9909},
{'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9879},
{'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9834},
{'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9716},
{'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9612},
{'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9599},
{'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9552},
{'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9532},
{'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9516},
{'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9499},
{'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9483},
{'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9464},
{'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.943},
{'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.943},
{'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9408},
{'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9335},
{'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9326},
{'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9262},
{'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8999},
{'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8986},
{'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8984},
{'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8873},
{'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def _UpperCAmelCase ( self ) -> Any:
_a = '''facebook/sam-vit-huge'''
_a = pipeline('''mask-generation''' , model=__UpperCAmelCase )
_a = image_segmenter(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
_a = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0210},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
] , ) | 320 | 0 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCAmelCase = 16
__lowerCAmelCase = 32
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 16 ) -> Union[str, Any]:
_a : Optional[int] = AutoTokenizer.from_pretrained('bert-base-cased' )
_a : str = DatasetDict(
{
'train': dataset['train'].select(_lowerCAmelCase ),
'validation': dataset['train'].select(_lowerCAmelCase ),
'test': dataset['validation'],
} )
def tokenize_function(lowerCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_a : Optional[int] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_a : List[Any] = datasets.map(
_lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_a : List[str] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_a : Optional[int] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_a : Optional[Any] = 16
elif accelerator.mixed_precision != "no":
_a : Optional[Any] = 8
else:
_a : List[Any] = None
return tokenizer.pad(
_lowerCAmelCase , padding='longest' , max_length=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_tensors='pt' , )
# Instantiate dataloaders.
_a : int = DataLoader(
tokenized_datasets['train'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
_a : int = DataLoader(
tokenized_datasets['validation'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
_a : int = DataLoader(
tokenized_datasets['test'] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase )
return train_dataloader, eval_dataloader, test_dataloader
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Union[str, Any]:
_a : Any = []
# Download the dataset
_a : List[str] = load_dataset('glue' , 'mrpc' )
# Create our splits
_a : Optional[int] = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
_a : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a : Tuple = config['lr']
_a : Dict = int(config['num_epochs'] )
_a : List[str] = int(config['seed'] )
_a : Dict = int(config['batch_size'] )
_a : str = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
_a : List[Any] = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_a : Optional[int] = batch_size // MAX_GPU_BATCH_SIZE
_a : Optional[int] = MAX_GPU_BATCH_SIZE
set_seed(_lowerCAmelCase )
# New Code #
# Create our folds:
_a : Tuple = kfold.split(np.zeros(datasets['train'].num_rows ) , datasets['train']['label'] )
_a : Optional[int] = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(_lowerCAmelCase ):
_a , _a , _a : List[str] = get_fold_dataloaders(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a : List[Any] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=_lowerCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_a : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
_a : int = AdamW(params=model.parameters() , lr=_lowerCAmelCase )
# Instantiate scheduler
_a : List[str] = get_linear_schedule_with_warmup(
optimizer=_lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# 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 : str = accelerator.prepare(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Now we train the model
for epoch in range(_lowerCAmelCase ):
model.train()
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_a : List[Any] = model(**_lowerCAmelCase )
_a : List[Any] = outputs.loss
_a : str = loss / gradient_accumulation_steps
accelerator.backward(_lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_a : Optional[int] = model(**_lowerCAmelCase )
_a : Union[str, Any] = outputs.logits.argmax(dim=-1 )
_a , _a : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=_lowerCAmelCase , references=_lowerCAmelCase , )
_a : Union[str, Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , _lowerCAmelCase )
# New Code #
# We also run predictions on the test set at the very end
_a : List[str] = []
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 : Tuple = model(**_lowerCAmelCase )
_a : Tuple = outputs.logits
_a , _a : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(_lowerCAmelCase , dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
_a : Union[str, Any] = torch.cat(_lowerCAmelCase , dim=0 )
_a : Optional[int] = torch.stack(_lowerCAmelCase , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
_a : List[str] = metric.compute(predictions=_lowerCAmelCase , references=_lowerCAmelCase )
accelerator.print('Average test metrics from all folds:' , _lowerCAmelCase )
def __lowerCamelCase ( ) -> List[Any]:
_a : str = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=_lowerCAmelCase , default=_lowerCAmelCase , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
# New Code #
parser.add_argument('--num_folds' , type=_lowerCAmelCase , default=3 , help='The number of splits to perform across the dataset' )
_a : Optional[Any] = parser.parse_args()
_a : str = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
main()
| 89 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __lowerCamelCase :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=99 , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=9 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase=8 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.002 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=0 , __UpperCAmelCase=None , __UpperCAmelCase=None , ) -> Optional[int]:
_a = parent
_a = batch_size
_a = encoder_seq_length
_a = decoder_seq_length
# For common tests
_a = self.decoder_seq_length
_a = is_training
_a = use_attention_mask
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = d_ff
_a = relative_attention_num_buckets
_a = dropout_rate
_a = initializer_factor
_a = eos_token_id
_a = pad_token_id
_a = decoder_start_token_id
_a = None
_a = decoder_layers
def _UpperCAmelCase ( self ) -> Dict:
return TaConfig.from_pretrained('''google/umt5-base''' )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , ) -> Optional[int]:
if attention_mask is None:
_a = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_a = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_a = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__UpperCAmelCase )
if decoder_head_mask is None:
_a = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__UpperCAmelCase )
if cross_attn_head_mask is None:
_a = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__UpperCAmelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _UpperCAmelCase ( self ) -> Tuple:
_a = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_a = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_a = input_ids.clamp(self.pad_token_id + 1 )
_a = decoder_input_ids.clamp(self.pad_token_id + 1 )
_a = self.get_config()
_a = config.num_attention_heads
_a = self.prepare_inputs_dict(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return config, input_dict
def _UpperCAmelCase ( self ) -> int:
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _UpperCAmelCase ( self ) -> List[str]:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Dict:
_a = UMTaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
_a = model(
input_ids=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase , )
_a = model(input_ids=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase )
_a = result.last_hidden_state
_a = result.past_key_values
_a = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__UpperCAmelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Optional[Any]:
_a = UMTaModel(config=__UpperCAmelCase ).get_decoder().to(__UpperCAmelCase ).eval()
# first forward pass
_a = model(__UpperCAmelCase , use_cache=__UpperCAmelCase )
_a = model(__UpperCAmelCase )
_a = model(__UpperCAmelCase , use_cache=__UpperCAmelCase )
self.parent.assertTrue(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) )
self.parent.assertTrue(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) + 1 )
_a , _a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = model(__UpperCAmelCase )['''last_hidden_state''']
_a = model(__UpperCAmelCase , past_key_values=__UpperCAmelCase )['''last_hidden_state''']
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -1, random_slice_idx].detach()
_a = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1e-3 ) )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , ) -> Union[str, Any]:
_a = UMTaModel(config=__UpperCAmelCase ).to(__UpperCAmelCase ).half().eval()
_a = model(**__UpperCAmelCase )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(__UpperCAmelCase ).any().item() )
@require_torch
class __lowerCamelCase ( a__ , a__ , a__ , unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
A_ : Optional[Any] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
A_ : int = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
A_ : str = True
A_ : List[str] = False
A_ : List[Any] = False
A_ : str = True
A_ : List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
A_ : Optional[Any] = [0.8, 0.9]
def _UpperCAmelCase ( self ) -> Tuple:
_a = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def _UpperCAmelCase ( self ) -> int:
_a = self.model_tester.prepare_config_and_inputs()
_a = UMTaModel(config_and_inputs[0] ).to(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__UpperCAmelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=__UpperCAmelCase , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
_a = self.model_tester.prepare_config_and_inputs()
_a = config_and_inputs[0]
_a = UMTaForConditionalGeneration(__UpperCAmelCase ).eval()
model.to(__UpperCAmelCase )
_a = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=__UpperCAmelCase ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCAmelCase ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCAmelCase ),
}
for attn_name, (name, mask) in zip(__UpperCAmelCase , head_masking.items() ):
_a = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_a = torch.ones(
config.num_decoder_layers , config.num_heads , device=__UpperCAmelCase )
_a = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=__UpperCAmelCase , return_dict_in_generate=__UpperCAmelCase , **__UpperCAmelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_a = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
def _UpperCAmelCase ( self ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
@unittest.skip(
'''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
def _UpperCAmelCase ( self ) -> Optional[int]:
_a = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=__UpperCAmelCase ).to(__UpperCAmelCase )
_a = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=__UpperCAmelCase , legacy=__UpperCAmelCase )
_a = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
_a = tokenizer(__UpperCAmelCase , return_tensors='''pt''' , padding=__UpperCAmelCase ).input_ids
# fmt: off
_a = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(__UpperCAmelCase , __UpperCAmelCase )
_a = model.generate(input_ids.to(__UpperCAmelCase ) )
_a = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
_a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase ) | 320 | 0 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = sorted(numsa + numsa )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = divmod(len(_lowerCAmelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase : Dict =[float(x) for x in input('Enter the elements of first array: ').split()]
__lowerCAmelCase : int =[float(x) for x in input('Enter the elements of second array: ').split()]
print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 9 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1 ) -> int:
if self.graph.get(__UpperCAmelCase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
_a = [[w, v]]
if not self.graph.get(__UpperCAmelCase ):
_a = []
def _UpperCAmelCase ( self ) -> int:
return list(self.graph )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Optional[int]:
if s == d:
return []
_a = []
_a = []
if s == -2:
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase=-1 ) -> Tuple:
if c == -1:
_a = floor(random() * 10000 ) + 10
for i in range(__UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
_a = floor(random() * c ) + 1
if n != i:
self.add_pair(__UpperCAmelCase , __UpperCAmelCase , 1 )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> List[str]:
_a = deque()
_a = []
if s == -2:
_a = list(self.graph )[0]
d.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
while d:
_a = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Tuple:
_a = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Dict:
return len(self.graph[u] )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> Tuple:
_a = []
_a = []
if s == -2:
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = s
_a = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return sorted_nodes
def _UpperCAmelCase ( self ) -> Optional[int]:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return list(__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Any:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return False
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Optional[int]:
_a = time()
self.dfs(__UpperCAmelCase , __UpperCAmelCase )
_a = time()
return end - begin
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> Optional[Any]:
_a = time()
self.bfs(__UpperCAmelCase )
_a = time()
return end - begin
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Optional[int]:
_a = {}
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1 ) -> Dict:
# check if the u exists
if self.graph.get(__UpperCAmelCase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
_a = [[w, v]]
# add the other way
if self.graph.get(__UpperCAmelCase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
_a = [[w, u]]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__UpperCAmelCase )
# the other way round
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Dict:
if s == d:
return []
_a = []
_a = []
if s == -2:
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase=-1 ) -> Tuple:
if c == -1:
_a = floor(random() * 10000 ) + 10
for i in range(__UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
_a = floor(random() * c ) + 1
if n != i:
self.add_pair(__UpperCAmelCase , __UpperCAmelCase , 1 )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> List[Any]:
_a = deque()
_a = []
if s == -2:
_a = list(self.graph )[0]
d.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
while d:
_a = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Dict:
return len(self.graph[u] )
def _UpperCAmelCase ( self ) -> int:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return list(__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Optional[Any]:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return False
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return list(self.graph )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Tuple:
_a = time()
self.dfs(__UpperCAmelCase , __UpperCAmelCase )
_a = time()
return end - begin
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> Tuple:
_a = time()
self.bfs(__UpperCAmelCase )
_a = time()
return end - begin | 320 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
"configuration_efficientnet": [
"EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientNetConfig",
"EfficientNetOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["EfficientNetImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"EfficientNetForImageClassification",
"EfficientNetModel",
"EfficientNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 339 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : Dict = 'unispeech'
def __init__( self , __UpperCAmelCase=32 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-5 , __UpperCAmelCase="group" , __UpperCAmelCase="gelu" , __UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512) , __UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCAmelCase=False , __UpperCAmelCase=128 , __UpperCAmelCase=16 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=0.05 , __UpperCAmelCase=10 , __UpperCAmelCase=2 , __UpperCAmelCase=0.0 , __UpperCAmelCase=10 , __UpperCAmelCase=0 , __UpperCAmelCase=320 , __UpperCAmelCase=2 , __UpperCAmelCase=0.1 , __UpperCAmelCase=100 , __UpperCAmelCase=256 , __UpperCAmelCase=256 , __UpperCAmelCase=0.1 , __UpperCAmelCase="mean" , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=256 , __UpperCAmelCase=80 , __UpperCAmelCase=0 , __UpperCAmelCase=1 , __UpperCAmelCase=2 , __UpperCAmelCase=0.5 , **__UpperCAmelCase , ) -> Union[str, Any]:
super().__init__(**__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase )
_a = hidden_size
_a = feat_extract_norm
_a = feat_extract_activation
_a = list(__UpperCAmelCase )
_a = list(__UpperCAmelCase )
_a = list(__UpperCAmelCase )
_a = conv_bias
_a = num_conv_pos_embeddings
_a = num_conv_pos_embedding_groups
_a = len(self.conv_dim )
_a = num_hidden_layers
_a = intermediate_size
_a = hidden_act
_a = num_attention_heads
_a = hidden_dropout
_a = attention_dropout
_a = activation_dropout
_a = feat_proj_dropout
_a = final_dropout
_a = layerdrop
_a = layer_norm_eps
_a = initializer_range
_a = num_ctc_classes
_a = vocab_size
_a = do_stable_layer_norm
_a = use_weighted_layer_sum
_a = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_a = apply_spec_augment
_a = mask_time_prob
_a = mask_time_length
_a = mask_time_min_masks
_a = mask_feature_prob
_a = mask_feature_length
_a = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_a = num_codevectors_per_group
_a = num_codevector_groups
_a = contrastive_logits_temperature
_a = feat_quantizer_dropout
_a = num_negatives
_a = codevector_dim
_a = proj_codevector_dim
_a = diversity_loss_weight
# ctc loss
_a = ctc_loss_reduction
_a = ctc_zero_infinity
# pretraining loss
_a = replace_prob
@property
def _UpperCAmelCase ( self ) -> Optional[int]:
return functools.reduce(operator.mul , self.conv_stride , 1 ) | 320 | 0 |
"""simple docstring"""
from math import factorial
_lowerCAmelCase :Tuple = {str(d): factorial(d) for d in range(10)}
def lowerCamelCase_ (UpperCamelCase__ : int ):
return sum(DIGIT_FACTORIAL[d] for d in str(_lowerCAmelCase ) )
def lowerCamelCase_ ():
_UpperCAmelCase : Optional[int] = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , _lowerCAmelCase ) if sum_of_digit_factorial(_lowerCAmelCase ) == i )
if __name__ == "__main__":
print(f"{solution() = }")
| 263 |
"""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_rembert import RemBertTokenizer
else:
__snake_case = None
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
},
'''tokenizer_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
},
}
__snake_case = {
'''google/rembert''': 256,
}
__snake_case = '''▁'''
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : Optional[Any] = VOCAB_FILES_NAMES
A_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
A_ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ : List[Any] = RemBertTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase="[CLS]" , __UpperCAmelCase="[SEP]" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="[SEP]" , __UpperCAmelCase="<pad>" , __UpperCAmelCase="[CLS]" , __UpperCAmelCase="[MASK]" , **__UpperCAmelCase , ) -> List[Any]:
# Mask token behave like a normal word, i.e. include the space before it
_a = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , **__UpperCAmelCase , )
_a = do_lower_case
_a = remove_space
_a = keep_accents
_a = vocab_file
_a = False if not self.vocab_file else True
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> List[int]:
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1]
return [1] + ([0] * len(__UpperCAmelCase )) + [1]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> List[int]:
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> Tuple[str]:
if not os.path.isdir(__UpperCAmelCase ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(__UpperCAmelCase ) )
return
_a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ):
copyfile(self.vocab_file , __UpperCAmelCase )
return (out_vocab_file,) | 320 | 0 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=sys.maxsize ) -> Union[str, Any]:
_lowerCAmelCase = "bilinear"
_lowerCAmelCase = max_size
_lowerCAmelCase = short_edge_length
def __call__( self , _lowerCAmelCase ) -> Tuple:
_lowerCAmelCase = []
for img in imgs:
_lowerCAmelCase , _lowerCAmelCase = img.shape[:2]
# later: provide list and randomly choose index for resize
_lowerCAmelCase = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
_lowerCAmelCase = size * 1.0 / min(__UpperCAmelCase , __UpperCAmelCase )
if h < w:
_lowerCAmelCase , _lowerCAmelCase = size, scale * w
else:
_lowerCAmelCase , _lowerCAmelCase = scale * h, size
if max(__UpperCAmelCase , __UpperCAmelCase ) > self.max_size:
_lowerCAmelCase = self.max_size * 1.0 / max(__UpperCAmelCase , __UpperCAmelCase )
_lowerCAmelCase = newh * scale
_lowerCAmelCase = neww * scale
_lowerCAmelCase = int(neww + 0.5 )
_lowerCAmelCase = int(newh + 0.5 )
if img.dtype == np.uinta:
_lowerCAmelCase = Image.fromarray(__UpperCAmelCase )
_lowerCAmelCase = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
_lowerCAmelCase = np.asarray(__UpperCAmelCase )
else:
_lowerCAmelCase = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
_lowerCAmelCase = nn.functional.interpolate(
__UpperCAmelCase , (newh, neww) , mode=self.interp_method , align_corners=__UpperCAmelCase ).squeeze(0 )
img_augs.append(__UpperCAmelCase )
return img_augs
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase ) -> List[Any]:
_lowerCAmelCase = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
_lowerCAmelCase = cfg.INPUT.FORMAT
_lowerCAmelCase = cfg.SIZE_DIVISIBILITY
_lowerCAmelCase = cfg.PAD_VALUE
_lowerCAmelCase = cfg.INPUT.MAX_SIZE_TEST
_lowerCAmelCase = cfg.MODEL.DEVICE
_lowerCAmelCase = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_lowerCAmelCase = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_lowerCAmelCase = lambda _lowerCAmelCase : (x - self.pixel_mean) / self.pixel_std
def _snake_case ( self , _lowerCAmelCase ) -> List[str]:
_lowerCAmelCase = tuple(max(__UpperCAmelCase ) for s in zip(*[img.shape for img in images] ) )
_lowerCAmelCase = [im.shape[-2:] for im in images]
_lowerCAmelCase = [
nn.functional.pad(
__UpperCAmelCase , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(__UpperCAmelCase , __UpperCAmelCase )
]
return torch.stack(__UpperCAmelCase ), torch.tensor(__UpperCAmelCase )
def __call__( self , _lowerCAmelCase , _lowerCAmelCase=False ) -> Dict:
with torch.no_grad():
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_lowerCAmelCase = [images]
if single_image:
assert len(__UpperCAmelCase ) == 1
for i in range(len(__UpperCAmelCase ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(__UpperCAmelCase , images.pop(__UpperCAmelCase ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
__UpperCAmelCase , torch.as_tensor(img_tensorize(images.pop(__UpperCAmelCase ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
_lowerCAmelCase = torch.tensor([im.shape[:2] for im in images] )
_lowerCAmelCase = self.aug(__UpperCAmelCase )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
_lowerCAmelCase = [self.normalizer(__UpperCAmelCase ) for x in images]
# now pad them to do the following operations
_lowerCAmelCase , _lowerCAmelCase = self.pad(__UpperCAmelCase )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
_lowerCAmelCase = torch.true_divide(__UpperCAmelCase , __UpperCAmelCase )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def __a(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def __a(SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple[int, int] ):
'''simple docstring'''
assert torch.isfinite(_lowerCAmelCase ).all(), "Box tensor contains infinite or NaN!"
_lowerCAmelCase , _lowerCAmelCase = box_size
tensor[:, 0].clamp_(min=0 , max=_lowerCAmelCase )
tensor[:, 1].clamp_(min=0 , max=_lowerCAmelCase )
tensor[:, 2].clamp_(min=0 , max=_lowerCAmelCase )
tensor[:, 3].clamp_(min=0 , max=_lowerCAmelCase )
| 158 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''ReformerTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''ReformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ReformerAttention''',
'''ReformerForMaskedLM''',
'''ReformerForQuestionAnswering''',
'''ReformerForSequenceClassification''',
'''ReformerLayer''',
'''ReformerModel''',
'''ReformerModelWithLMHead''',
'''ReformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 320 | 0 |
'''simple docstring'''
import os
import sys
import transformers
_snake_case = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 250 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@require_torch
def _UpperCAmelCase ( self ) -> Union[str, Any]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_a = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
_a = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(__UpperCAmelCase )
BertModel.from_pretrained(__UpperCAmelCase )
BertTokenizer.from_pretrained(__UpperCAmelCase )
pipeline(task='''fill-mask''' , model=__UpperCAmelCase )
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
_a = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_a = '''1'''
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def _UpperCAmelCase ( self ) -> List[Any]:
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_a = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
_a = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(__UpperCAmelCase )
BertModel.from_pretrained(__UpperCAmelCase )
BertTokenizer.from_pretrained(__UpperCAmelCase )
pipeline(task='''fill-mask''' , model=__UpperCAmelCase )
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
_a = self.get_env()
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def _UpperCAmelCase ( self ) -> Optional[Any]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_a = '''
from transformers import BertConfig, BertModel, BertTokenizer
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
_a = self.get_env()
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# next emulate no network
_a = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_a = '''1'''
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def _UpperCAmelCase ( self ) -> Tuple:
_a = '''
from transformers import pipeline
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
_a = self.get_env()
_a = '''1'''
_a = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
'''You cannot infer task automatically within `pipeline` when using offline mode''' , result.stderr.decode().replace('''\n''' , '''''' ) , )
@require_torch
def _UpperCAmelCase ( self ) -> List[Any]:
_a = '''
from transformers import AutoModel
'''
_a = '''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
_a = self.get_env()
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_a = '''1'''
_a = subprocess.run(__UpperCAmelCase , env=__UpperCAmelCase , check=__UpperCAmelCase , capture_output=__UpperCAmelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() ) | 320 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase__ : int = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase__ : Dict = [ord(letter) for letter in string.ascii_lowercase]
UpperCamelCase__ : Any = {ord(char) for char in VALID_CHARS}
UpperCamelCase__ : int = ['the', 'be', 'to', 'of', 'and', 'in', 'that', 'have']
def UpperCAmelCase ( a_ , a_ ) -> Optional[int]:
"""simple docstring"""
A_ : Optional[int] = """"""
A_ : Union[str, Any] = 4_2
A_ : Tuple = 4_2
A_ : List[str] = 4_2
for keychar, cipherchar in zip(cycle(_lowerCAmelCase ) , _lowerCAmelCase ):
A_ : Union[str, Any] = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(_lowerCAmelCase )
return decoded
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
A_ : Any = []
for key in product(_lowerCAmelCase , repeat=3 ):
A_ : int = try_key(_lowerCAmelCase , _lowerCAmelCase )
if encoded is not None:
possibles.append(_lowerCAmelCase )
return possibles
def UpperCAmelCase ( a_ , a_ ) -> List[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def UpperCAmelCase ( a_ = "p059_cipher.txt" ) -> Union[str, Any]:
"""simple docstring"""
A_ : Optional[Any] = 4_2
A_ : Tuple = 4_2
A_ : str = 4_2
A_ : Any = 4_2
A_ : Union[str, Any] = Path(_lowerCAmelCase ).parent.joinpath(_lowerCAmelCase ).read_text(encoding="""utf-8""" )
A_ : Dict = [int(_lowerCAmelCase ) for number in data.strip().split(""",""" )]
A_ : Optional[Any] = filter_valid_chars(_lowerCAmelCase )
for common_word in COMMON_WORDS:
A_ : int = filter_common_word(_lowerCAmelCase , _lowerCAmelCase )
if len(_lowerCAmelCase ) == 1:
break
A_ : Any = possibles[0]
return sum(ord(_lowerCAmelCase ) for char in decoded_text )
if __name__ == "__main__":
print(f'{solution() = }')
| 344 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : str = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Any = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[str]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[str]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Dict = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> int:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Union[str, Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Tuple = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Optional[int]:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Optional[Any] = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
requires_backends(cls , ['''flax'''] )
class __lowerCamelCase ( metaclass=a__ ):
'''simple docstring'''
A_ : Any = ['flax']
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> List[Any]:
requires_backends(self , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Any:
requires_backends(cls , ['''flax'''] )
@classmethod
def _UpperCAmelCase ( cls , *__UpperCAmelCase , **__UpperCAmelCase ) -> Dict:
requires_backends(cls , ['''flax'''] ) | 320 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _snake_case ( a__ ):
'''simple docstring'''
A__ : Dict = 'M-CLIP'
def __init__( self: Optional[int] ,lowerCamelCase_: List[str]=1024 ,lowerCamelCase_: Optional[int]=768 ,**lowerCamelCase_: int ) -> List[str]:
UpperCAmelCase_ : Optional[int] = transformerDimSize
UpperCAmelCase_ : Optional[int] = imageDimSize
super().__init__(**__UpperCAmelCase )
class _snake_case ( a__ ):
'''simple docstring'''
A__ : int = MCLIPConfig
def __init__( self: List[Any] ,lowerCamelCase_: List[Any] ,*lowerCamelCase_: List[Any] ,**lowerCamelCase_: Dict ) -> List[Any]:
super().__init__(__UpperCAmelCase ,*__UpperCAmelCase ,**__UpperCAmelCase )
UpperCAmelCase_ : Any = XLMRobertaModel(__UpperCAmelCase )
UpperCAmelCase_ : Optional[int] = torch.nn.Linear(
in_features=config.transformerDimensions ,out_features=config.numDims )
def A__ ( self: str ,lowerCamelCase_: List[str] ,lowerCamelCase_: Tuple ) -> Optional[Any]:
UpperCAmelCase_ : Tuple = self.transformer(input_ids=__UpperCAmelCase ,attention_mask=__UpperCAmelCase )[0]
UpperCAmelCase_ : Tuple = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(__UpperCAmelCase ), embs
| 345 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__snake_case = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__snake_case = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__snake_case = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
def remove_articles(_lowerCAmelCase : Optional[int] ):
_a = re.compile(R'''\b(a|an|the)\b''', re.UNICODE )
return re.sub(_lowerCAmelCase, ''' ''', _lowerCAmelCase )
def white_space_fix(_lowerCAmelCase : Tuple ):
return " ".join(text.split() )
def remove_punc(_lowerCAmelCase : Tuple ):
_a = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_lowerCAmelCase : List[Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase ) ) ) )
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
return int(normalize_answer(_lowerCAmelCase ) == normalize_answer(_lowerCAmelCase ) )
def A_ ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Any ):
"""simple docstring"""
_a = [any(compute_exact(_lowerCAmelCase, _lowerCAmelCase ) for ref in refs ) for pred, refs in zip(_lowerCAmelCase, _lowerCAmelCase )]
return (sum(_lowerCAmelCase ) / len(_lowerCAmelCase )) * 1_00
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : List[Any], _lowerCAmelCase : str, _lowerCAmelCase : str ):
"""simple docstring"""
_a = [rgram for rgrams in rgramslist for rgram in rgrams]
_a = Counter(_lowerCAmelCase )
_a = Counter(_lowerCAmelCase )
_a = Counter()
for sgram, scount in sgramcounter.items():
_a = scount * numref
_a = Counter(_lowerCAmelCase )
_a = Counter()
for cgram, ccount in cgramcounter.items():
_a = ccount * numref
# KEEP
_a = sgramcounter_rep & cgramcounter_rep
_a = keepgramcounter_rep & rgramcounter
_a = sgramcounter_rep & rgramcounter
_a = 0
_a = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_a = 1
_a = 1
if len(_lowerCAmelCase ) > 0:
_a = keeptmpscorea / len(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_a = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_a = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_a = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_a = sgramcounter_rep - cgramcounter_rep
_a = delgramcounter_rep - rgramcounter
_a = sgramcounter_rep - rgramcounter
_a = 0
_a = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_a = 1
if len(_lowerCAmelCase ) > 0:
_a = deltmpscorea / len(_lowerCAmelCase )
# ADDITION
_a = set(_lowerCAmelCase ) - set(_lowerCAmelCase )
_a = set(_lowerCAmelCase ) & set(_lowerCAmelCase )
_a = set(_lowerCAmelCase ) - set(_lowerCAmelCase )
_a = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_a = 1
_a = 1
if len(_lowerCAmelCase ) > 0:
_a = addtmpscore / len(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
_a = addtmpscore / len(_lowerCAmelCase )
_a = 0
if addscore_precision > 0 or addscore_recall > 0:
_a = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def A_ ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Dict, _lowerCAmelCase : Any ):
"""simple docstring"""
_a = len(_lowerCAmelCase )
_a = ssent.split(''' ''' )
_a = csent.split(''' ''' )
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
_a = []
for rsent in rsents:
_a = rsent.split(''' ''' )
_a = []
_a = []
_a = []
ragramslist.append(_lowerCAmelCase )
for i in range(0, len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
_a = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
_a = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
_a = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
ragramslist.append(_lowerCAmelCase )
for i in range(0, len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
_a = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
_a = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
_a = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(_lowerCAmelCase )
for i in range(0, len(_lowerCAmelCase ) - 1 ):
if i < len(_lowerCAmelCase ) - 1:
_a = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 2:
_a = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(_lowerCAmelCase )
if i < len(_lowerCAmelCase ) - 3:
_a = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(_lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
((_a) , (_a) , (_a)) = SARIngram(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
_a = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_a = sum([delascore, delascore, delascore, delascore] ) / 4
_a = sum([addascore, addascore, addascore, addascore] ) / 4
_a = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : bool = True, _lowerCAmelCase : str = "13a", _lowerCAmelCase : bool = True ):
"""simple docstring"""
if lowercase:
_a = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_a = sacrebleu.metrics.bleu._get_tokenizer(_lowerCAmelCase )()(_lowerCAmelCase )
else:
_a = sacrebleu.TOKENIZERS[tokenizer]()(_lowerCAmelCase )
elif tokenizer == "moses":
_a = sacremoses.MosesTokenizer().tokenize(_lowerCAmelCase, return_str=_lowerCAmelCase, escape=_lowerCAmelCase )
elif tokenizer == "penn":
_a = sacremoses.MosesTokenizer().penn_tokenize(_lowerCAmelCase, return_str=_lowerCAmelCase )
else:
_a = sentence
if not return_str:
_a = normalized_sent.split()
return normalized_sent
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : Dict, _lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
if not (len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == len(_lowerCAmelCase )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_a = 0
for src, pred, refs in zip(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase ):
sari_score += SARIsent(normalize(_lowerCAmelCase ), normalize(_lowerCAmelCase ), [normalize(_lowerCAmelCase ) for sent in refs] )
_a = sari_score / len(_lowerCAmelCase )
return 1_00 * sari_score
def A_ ( _lowerCAmelCase : Tuple, _lowerCAmelCase : Tuple, _lowerCAmelCase : Any="exp", _lowerCAmelCase : Tuple=None, _lowerCAmelCase : Union[str, Any]=False, _lowerCAmelCase : Optional[Any]=False, _lowerCAmelCase : List[str]=False, ):
"""simple docstring"""
_a = len(references[0] )
if any(len(_lowerCAmelCase ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_a = [[refs[i] for refs in references] for i in range(_lowerCAmelCase )]
_a = sacrebleu.corpus_bleu(
_lowerCAmelCase, _lowerCAmelCase, smooth_method=_lowerCAmelCase, smooth_value=_lowerCAmelCase, force=_lowerCAmelCase, lowercase=_lowerCAmelCase, use_effective_order=_lowerCAmelCase, )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> str:
_a = {}
result.update({'''sari''': compute_sari(sources=__UpperCAmelCase , predictions=__UpperCAmelCase , references=__UpperCAmelCase )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=__UpperCAmelCase , references=__UpperCAmelCase )} )
result.update({'''exact''': compute_em(predictions=__UpperCAmelCase , references=__UpperCAmelCase )} )
return result | 320 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( a__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = XGLMTokenizer
SCREAMING_SNAKE_CASE_ = XGLMTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def a_ ( self) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ = XGLMTokenizer(__UpperCAmelCase, keep_accents=__UpperCAmelCase)
tokenizer.save_pretrained(self.tmpdirname)
def a_ ( self) -> str:
snake_case_ = '<pad>'
snake_case_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase), __UpperCAmelCase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase), __UpperCAmelCase)
def a_ ( self) -> List[str]:
snake_case_ = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], '<s>')
self.assertEqual(vocab_keys[1], '<pad>')
self.assertEqual(len(__UpperCAmelCase), 1008)
def a_ ( self) -> Optional[int]:
self.assertEqual(self.get_tokenizer().vocab_size, 1008)
def a_ ( self) -> int:
snake_case_ = XGLMTokenizer(__UpperCAmelCase, keep_accents=__UpperCAmelCase)
snake_case_ = tokenizer.tokenize('This is a test')
self.assertListEqual(__UpperCAmelCase, ['▁This', '▁is', '▁a', '▁t', 'est'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], )
snake_case_ = tokenizer.tokenize('I was born in 92000, and this is falsé.')
self.assertListEqual(
__UpperCAmelCase, [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
], )
snake_case_ = tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
self.assertListEqual(
__UpperCAmelCase, [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
], )
snake_case_ = tokenizer.convert_ids_to_tokens(__UpperCAmelCase)
self.assertListEqual(
__UpperCAmelCase, [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
], )
@cached_property
def a_ ( self) -> int:
return XGLMTokenizer.from_pretrained('facebook/xglm-564M')
def a_ ( self) -> Union[str, Any]:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(__UpperCAmelCase, f.name)
snake_case_ = XGLMTokenizer(f.name, keep_accents=__UpperCAmelCase)
snake_case_ = pickle.dumps(__UpperCAmelCase)
pickle.loads(__UpperCAmelCase)
def a_ ( self) -> Dict:
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer()
snake_case_ = 'I was born in 92000, and this is falsé.'
snake_case_ = tokenizer.tokenize(__UpperCAmelCase)
snake_case_ = rust_tokenizer.tokenize(__UpperCAmelCase)
self.assertListEqual(__UpperCAmelCase, __UpperCAmelCase)
snake_case_ = tokenizer.encode(__UpperCAmelCase, add_special_tokens=__UpperCAmelCase)
snake_case_ = rust_tokenizer.encode(__UpperCAmelCase, add_special_tokens=__UpperCAmelCase)
self.assertListEqual(__UpperCAmelCase, __UpperCAmelCase)
snake_case_ = self.get_rust_tokenizer()
snake_case_ = tokenizer.encode(__UpperCAmelCase)
snake_case_ = rust_tokenizer.encode(__UpperCAmelCase)
self.assertListEqual(__UpperCAmelCase, __UpperCAmelCase)
@slow
def a_ ( self) -> Tuple:
snake_case_ = 'Hello World!'
snake_case_ = [2, 3_1227, 4447, 35]
self.assertListEqual(__UpperCAmelCase, self.big_tokenizer.encode(__UpperCAmelCase))
@slow
def a_ ( self) -> Union[str, Any]:
snake_case_ = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'
)
# fmt: off
snake_case_ = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(__UpperCAmelCase, self.big_tokenizer.encode(__UpperCAmelCase))
@slow
def a_ ( self) -> int:
# fmt: off
snake_case_ = {
'input_ids': [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase, model_name='facebook/xglm-564M', padding=__UpperCAmelCase, )
| 69 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 50 ):
"""simple docstring"""
_a = [1] * (length + 1)
for row_length in range(3, length + 1 ):
for block_length in range(3, row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f'{solution() = }') | 320 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = ['''BartphoTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 188 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__snake_case = logging.get_logger('''transformers.models.speecht5''')
__snake_case = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__snake_case = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__snake_case = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__snake_case = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__snake_case = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__snake_case = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__snake_case = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__snake_case = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__snake_case = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case = []
__snake_case = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
__snake_case = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Tuple, _lowerCAmelCase : Dict, _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
for attribute in key.split('''.''' ):
_a = getattr(_lowerCAmelCase, _lowerCAmelCase )
if weight_type is not None:
_a = getattr(_lowerCAmelCase, _lowerCAmelCase ).shape
else:
_a = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
_a = value
elif weight_type == "weight_g":
_a = value
elif weight_type == "weight_v":
_a = value
elif weight_type == "bias":
_a = value
elif weight_type == "running_mean":
_a = value
elif weight_type == "running_var":
_a = value
elif weight_type == "num_batches_tracked":
_a = value
else:
_a = value
logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Tuple ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
_a , _a = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def A_ ( _lowerCAmelCase : Any, _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : int ):
"""simple docstring"""
_a = []
if task == "s2t":
_a = hf_model.speechta.encoder.prenet.feature_encoder
_a = MAPPING_S2T
_a = IGNORE_KEYS_S2T
elif task == "t2s":
_a = None
_a = MAPPING_T2S
_a = IGNORE_KEYS_T2S
elif task == "s2s":
_a = hf_model.speechta.encoder.prenet.feature_encoder
_a = MAPPING_S2S
_a = IGNORE_KEYS_S2S
else:
raise ValueError(f'Unsupported task: {task}' )
for name, value in fairseq_dict.items():
if should_ignore(_lowerCAmelCase, _lowerCAmelCase ):
logger.info(f'{name} was ignored' )
continue
_a = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, hf_model.config.feat_extract_norm == '''group''', )
_a = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
_a , _a = key.split('''.*.''' )
if prefix in name and suffix in name:
_a = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
_a = True
if "*" in mapped_key:
_a = name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
_a = mapped_key.replace('''*''', _lowerCAmelCase )
if "weight_g" in name:
_a = '''weight_g'''
elif "weight_v" in name:
_a = '''weight_v'''
elif "bias" in name:
_a = '''bias'''
elif "weight" in name:
_a = '''weight'''
elif "running_mean" in name:
_a = '''running_mean'''
elif "running_var" in name:
_a = '''running_var'''
elif "num_batches_tracked" in name:
_a = '''num_batches_tracked'''
else:
_a = None
set_recursively(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A_ ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Dict, _lowerCAmelCase : List[Any], _lowerCAmelCase : List[Any] ):
"""simple docstring"""
_a = full_name.split('''conv_layers.''' )[-1]
_a = name.split('''.''' )
_a = int(items[0] )
_a = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_a = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_a = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_a = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_a = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def A_ ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Dict, _lowerCAmelCase : List[Any]=None, _lowerCAmelCase : List[str]=None, _lowerCAmelCase : int=None, ):
"""simple docstring"""
if config_path is not None:
_a = SpeechTaConfig.from_pretrained(_lowerCAmelCase )
else:
_a = SpeechTaConfig()
if task == "s2t":
_a = config.max_text_positions
_a = SpeechTaForSpeechToText(_lowerCAmelCase )
elif task == "t2s":
_a = 18_76
_a = 6_00
_a = config.max_speech_positions
_a = SpeechTaForTextToSpeech(_lowerCAmelCase )
elif task == "s2s":
_a = 18_76
_a = config.max_speech_positions
_a = SpeechTaForSpeechToSpeech(_lowerCAmelCase )
else:
raise ValueError(f'Unknown task name: {task}' )
if vocab_path:
_a = SpeechTaTokenizer(_lowerCAmelCase, model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
_a = AddedToken('''<mask>''', lstrip=_lowerCAmelCase, rstrip=_lowerCAmelCase )
_a = mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
_a = SpeechTaFeatureExtractor()
_a = SpeechTaProcessor(tokenizer=_lowerCAmelCase, feature_extractor=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
_a = torch.load(_lowerCAmelCase )
recursively_load_weights(fairseq_checkpoint['''model'''], _lowerCAmelCase, _lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if repo_id:
print('''Pushing to the hub...''' )
processor.push_to_hub(_lowerCAmelCase )
model.push_to_hub(_lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
__snake_case = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
) | 320 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class lowercase ( a__ ):
_a = 'MCTCTFeatureExtractor'
_a = 'AutoTokenizer'
def __init__( self , _a , _a ) -> List[Any]:
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
_A : int = self.feature_extractor
_A : List[Any] = False
def __call__( self , *_a , **_a ) -> List[str]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__UpperCAmelCase , **__UpperCAmelCase )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
_A : Any = kwargs.pop("""raw_speech""" )
else:
_A : Any = kwargs.pop("""audio""" , __UpperCAmelCase )
_A : List[str] = kwargs.pop("""sampling_rate""" , __UpperCAmelCase )
_A : Optional[Any] = kwargs.pop("""text""" , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
_A : Dict = args[0]
_A : Dict = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
_A : Any = self.feature_extractor(__UpperCAmelCase , *__UpperCAmelCase , sampling_rate=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None:
_A : int = self.tokenizer(__UpperCAmelCase , **__UpperCAmelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_A : Optional[Any] = encodings["""input_ids"""]
return inputs
def a__ ( self , *_a , **_a ) -> List[str]:
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def a__ ( self , *_a , **_a ) -> List[str]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*__UpperCAmelCase , **__UpperCAmelCase )
_A : Optional[int] = kwargs.pop("""input_features""" , __UpperCAmelCase )
_A : Optional[Any] = kwargs.pop("""labels""" , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
_A : Optional[int] = args[0]
_A : Dict = args[1:]
if input_features is not None:
_A : List[str] = self.feature_extractor.pad(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase )
if labels is not None:
_A : Union[str, Any] = self.tokenizer.pad(__UpperCAmelCase , **__UpperCAmelCase )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_A : Tuple = labels["""input_ids"""]
return input_features
def a__ ( self , *_a , **_a ) -> str:
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@contextmanager
def a__ ( self ) -> List[Any]:
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
_A : Dict = True
_A : Optional[int] = self.tokenizer
yield
_A : Any = self.feature_extractor
_A : List[str] = False
| 26 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[Any] = 'decision_transformer'
A_ : Union[str, Any] = ['past_key_values']
A_ : str = {
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , __UpperCAmelCase=17 , __UpperCAmelCase=4 , __UpperCAmelCase=128 , __UpperCAmelCase=4096 , __UpperCAmelCase=True , __UpperCAmelCase=1 , __UpperCAmelCase=1024 , __UpperCAmelCase=3 , __UpperCAmelCase=1 , __UpperCAmelCase=None , __UpperCAmelCase="relu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=0.02 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=50256 , __UpperCAmelCase=50256 , __UpperCAmelCase=False , __UpperCAmelCase=False , **__UpperCAmelCase , ) -> Optional[int]:
_a = state_dim
_a = act_dim
_a = hidden_size
_a = max_ep_len
_a = action_tanh
_a = vocab_size
_a = n_positions
_a = n_layer
_a = n_head
_a = n_inner
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = scale_attn_weights
_a = use_cache
_a = scale_attn_by_inverse_layer_idx
_a = reorder_and_upcast_attn
_a = bos_token_id
_a = eos_token_id
super().__init__(bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase ) | 320 | 0 |
'''simple docstring'''
import numpy as np
def __lowerCamelCase ( lowerCAmelCase_ ) -> Tuple:
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 |
"""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
__snake_case = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[str] = ['pixel_values']
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = True , **__UpperCAmelCase , ) -> None:
super().__init__(**__UpperCAmelCase )
_a = size if size is not None else {'''shortest_edge''': 224}
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
_a = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase , param_name='''crop_size''' )
_a = do_resize
_a = size
_a = resample
_a = do_center_crop
_a = crop_size
_a = do_rescale
_a = rescale_factor
_a = do_normalize
_a = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_a = image_std if image_std is not None else OPENAI_CLIP_STD
_a = do_convert_rgb
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
_a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
_a = get_resize_output_image_size(__UpperCAmelCase , size=size['''shortest_edge'''] , default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
_a = get_size_dict(__UpperCAmelCase )
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(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> Optional[Any]:
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ) -> PIL.Image.Image:
_a = do_resize if do_resize is not None else self.do_resize
_a = size if size is not None else self.size
_a = get_size_dict(__UpperCAmelCase , param_name='''size''' , default_to_square=__UpperCAmelCase )
_a = resample if resample is not None else self.resample
_a = do_center_crop if do_center_crop is not None else self.do_center_crop
_a = crop_size if crop_size is not None else self.crop_size
_a = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' , default_to_square=__UpperCAmelCase )
_a = do_rescale if do_rescale is not None else self.do_rescale
_a = rescale_factor if rescale_factor is not None else self.rescale_factor
_a = do_normalize if do_normalize is not None else self.do_normalize
_a = image_mean if image_mean is not None else self.image_mean
_a = image_std if image_std is not None else self.image_std
_a = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_a = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
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 = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
_a = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
_a = [self.resize(image=__UpperCAmelCase , size=__UpperCAmelCase , resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
_a = [self.center_crop(image=__UpperCAmelCase , size=__UpperCAmelCase ) for image in images]
if do_rescale:
_a = [self.rescale(image=__UpperCAmelCase , scale=__UpperCAmelCase ) for image in images]
if do_normalize:
_a = [self.normalize(image=__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase ) for image in images]
_a = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
_a = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase ) | 320 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] ={
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
),
}
class _lowercase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = 'swinv2'
SCREAMING_SNAKE_CASE__ : Tuple = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self :Union[str, Any] , lowerCAmelCase__ :int=224 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :str=96 , lowerCAmelCase__ :str=[2, 2, 6, 2] , lowerCAmelCase__ :List[Any]=[3, 6, 12, 24] , lowerCAmelCase__ :Optional[int]=7 , lowerCAmelCase__ :str=4.0 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=0.0 , lowerCAmelCase__ :Dict=0.0 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Tuple="gelu" , lowerCAmelCase__ :Dict=False , lowerCAmelCase__ :Tuple=0.02 , lowerCAmelCase__ :Union[str, Any]=1E-5 , lowerCAmelCase__ :int=32 , **lowerCAmelCase__ :List[Any] , ) -> Dict:
super().__init__(**__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[str] = image_size
__SCREAMING_SNAKE_CASE : Optional[int] = patch_size
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : Dict = embed_dim
__SCREAMING_SNAKE_CASE : str = depths
__SCREAMING_SNAKE_CASE : str = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = num_heads
__SCREAMING_SNAKE_CASE : Dict = window_size
__SCREAMING_SNAKE_CASE : str = mlp_ratio
__SCREAMING_SNAKE_CASE : List[str] = qkv_bias
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : int = drop_path_rate
__SCREAMING_SNAKE_CASE : int = hidden_act
__SCREAMING_SNAKE_CASE : Union[str, Any] = use_absolute_embeddings
__SCREAMING_SNAKE_CASE : str = layer_norm_eps
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Optional[Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__SCREAMING_SNAKE_CASE : int = int(embed_dim * 2 ** (len(__UpperCAmelCase ) - 1) )
__SCREAMING_SNAKE_CASE : Optional[int] = (0, 0, 0, 0)
| 9 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 320 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __lowerCAmelCase ( a__ ):
def __init__( self : Optional[Any] , A : int , A : Tuple , A : int) -> int:
"""simple docstring"""
_UpperCAmelCase = dataset
_UpperCAmelCase = process
_UpperCAmelCase = params
def __len__( self : List[str]) -> Any:
"""simple docstring"""
return len(self.dataset)
def __getitem__( self : int , A : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = self.dataset[i]
_UpperCAmelCase = self.process(__UpperCAmelCase , **self.params)
return processed
class __lowerCAmelCase ( a__ ):
def __init__( self : Tuple , A : Optional[Any] , A : int , A : Optional[int] , A : Optional[int]=None) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = loader
_UpperCAmelCase = infer
_UpperCAmelCase = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_UpperCAmelCase = None
_UpperCAmelCase = loader_batch_size
# Internal bookkeeping
_UpperCAmelCase = None
_UpperCAmelCase = None
def __len__( self : str) -> List[Any]:
"""simple docstring"""
return len(self.loader)
def __iter__( self : List[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = iter(self.loader)
return self
def _lowerCamelCase ( self : List[Any]) -> str:
"""simple docstring"""
if isinstance(self._loader_batch_data , torch.Tensor):
# Batch data is simple tensor, just fetch the slice
_UpperCAmelCase = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_UpperCAmelCase = {}
for k, element in self._loader_batch_data.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
# Convert ModelOutput to tuple first
_UpperCAmelCase = element.to_tuple()
if isinstance(element[0] , torch.Tensor):
_UpperCAmelCase = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0] , np.ndarray):
_UpperCAmelCase = tuple(np.expand_dims(el[self._loader_batch_index] , 0) for el in element)
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(__UpperCAmelCase , __UpperCAmelCase):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor):
_UpperCAmelCase = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0] , np.ndarray):
_UpperCAmelCase = tuple(np.expand_dims(el[self._loader_batch_index] , 0) for el in element)
continue
if element is None:
# This can happen for optional data that get passed around
_UpperCAmelCase = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_UpperCAmelCase = element[self._loader_batch_index].unsqueeze(0)
elif isinstance(element[self._loader_batch_index] , np.ndarray):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_UpperCAmelCase = np.expand_dims(element[self._loader_batch_index] , 0)
else:
# This is typically a list, so no need to `unsqueeze`.
_UpperCAmelCase = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_UpperCAmelCase = self._loader_batch_data.__class__(__UpperCAmelCase)
self._loader_batch_index += 1
return result
def _lowerCamelCase ( self : Any) -> Optional[Any]:
"""simple docstring"""
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
_UpperCAmelCase = next(self.iterator)
_UpperCAmelCase = self.infer(__UpperCAmelCase , **self.params)
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(__UpperCAmelCase , torch.Tensor):
_UpperCAmelCase = processed
else:
_UpperCAmelCase = list(processed.keys())[0]
_UpperCAmelCase = processed[key]
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
_UpperCAmelCase = len(__UpperCAmelCase)
else:
_UpperCAmelCase = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_UpperCAmelCase = observed_batch_size
# Setting internal index to unwrap the batch
_UpperCAmelCase = processed
_UpperCAmelCase = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class __lowerCAmelCase ( a__ ):
def __init__( self : Optional[Any] , A : List[str] , A : Dict , A : str , A : Any=None) -> List[Any]:
"""simple docstring"""
super().__init__(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
def __iter__( self : List[str]) -> int:
"""simple docstring"""
_UpperCAmelCase = iter(self.loader)
_UpperCAmelCase = None
return self
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.subiterator is None:
_UpperCAmelCase = self.infer(next(self.iterator) , **self.params)
try:
# Try to return next item
_UpperCAmelCase = next(self.subiterator)
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
_UpperCAmelCase = self.infer(next(self.iterator) , **self.params)
_UpperCAmelCase = next(self.subiterator)
return processed
class __lowerCAmelCase ( a__ ):
def __iter__( self : Any) -> Any:
"""simple docstring"""
_UpperCAmelCase = iter(self.loader)
return self
def _lowerCamelCase ( self : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = False
_UpperCAmelCase = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
_UpperCAmelCase = self.loader_batch_item()
_UpperCAmelCase = item.pop('is_last')
accumulator.append(__UpperCAmelCase)
if is_last:
return accumulator
while not is_last:
_UpperCAmelCase = self.infer(next(self.iterator) , **self.params)
if self.loader_batch_size is not None:
if isinstance(__UpperCAmelCase , torch.Tensor):
_UpperCAmelCase = processed
else:
_UpperCAmelCase = list(processed.keys())[0]
_UpperCAmelCase = processed[key]
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
_UpperCAmelCase = len(__UpperCAmelCase)
else:
_UpperCAmelCase = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_UpperCAmelCase = observed_batch_size
_UpperCAmelCase = processed
_UpperCAmelCase = 0
while self._loader_batch_index < self.loader_batch_size:
_UpperCAmelCase = self.loader_batch_item()
_UpperCAmelCase = item.pop('is_last')
accumulator.append(__UpperCAmelCase)
if is_last:
return accumulator
else:
_UpperCAmelCase = processed
_UpperCAmelCase = item.pop('is_last')
accumulator.append(__UpperCAmelCase)
return accumulator
class __lowerCAmelCase ( a__ ):
def __init__( self : str , A : Tuple , A : Optional[int]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = dataset
_UpperCAmelCase = key
def __len__( self : List[str]) -> Optional[Any]:
"""simple docstring"""
return len(self.dataset)
def __getitem__( self : Optional[Any] , A : Tuple) -> List[str]:
"""simple docstring"""
return self.dataset[i][self.key]
class __lowerCAmelCase ( a__ ):
def __init__( self : Any , A : Optional[Any] , A : Union[str, Any] , A : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = dataset
_UpperCAmelCase = keya
_UpperCAmelCase = keya
def __len__( self : Optional[Any]) -> Optional[Any]:
"""simple docstring"""
return len(self.dataset)
def __getitem__( self : List[str] , A : Tuple) -> List[Any]:
"""simple docstring"""
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 339 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
__snake_case = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def A_ ( ):
"""simple docstring"""
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, bootstrap_aggregation=_lowerCAmelCase, rouge_keys=['''rouge2''', '''rougeL'''] )
assert isinstance(_lowerCAmelCase, _lowerCAmelCase )
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, bootstrap_aggregation=_lowerCAmelCase, rouge_keys=['''rouge2'''] )
assert (
pd.DataFrame(no_aggregation['''rouge2'''] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['''rouge2'''] ).fmeasure.mean()
)
def A_ ( ):
"""simple docstring"""
_a = '''rougeLsum'''
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=[k] )[k]
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=[k] )[k]
assert score > score_no_sep
def A_ ( ):
"""simple docstring"""
_a = ['''rouge1''', '''rouge2''', '''rougeL''']
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=_lowerCAmelCase )
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase, rouge_keys=_lowerCAmelCase )
assert score_sep == score_no_sep
def A_ ( ):
"""simple docstring"""
_a = [
'''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
]
_a = [
'''Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
''' the final seconds on board Flight 9525.''',
]
assert calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase ) == calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, newline_sep=_lowerCAmelCase )
def A_ ( ):
"""simple docstring"""
_a = [
'''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
]
_a = [
''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
]
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, rouge_keys=['''rougeLsum'''], newline_sep=_lowerCAmelCase )['''rougeLsum''']
_a = calculate_rouge(_lowerCAmelCase, _lowerCAmelCase, rouge_keys=['''rougeLsum'''] )['''rougeLsum''']
assert new_score > prev_score
def A_ ( ):
"""simple docstring"""
_a = Path('''examples/seq2seq/test_data/wmt_en_ro''' )
_a = calculate_rouge_path(data_dir.joinpath('''test.source''' ), data_dir.joinpath('''test.target''' ) )
assert isinstance(_lowerCAmelCase, _lowerCAmelCase )
_a = calculate_rouge_path(
data_dir.joinpath('''test.source''' ), data_dir.joinpath('''test.target''' ), bootstrap_aggregation=_lowerCAmelCase )
assert isinstance(_lowerCAmelCase, _lowerCAmelCase ) | 320 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase :Any = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase :Any = ['ReformerTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase :str = ['ReformerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase :Optional[int] = [
'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ReformerAttention',
'ReformerForMaskedLM',
'ReformerForQuestionAnswering',
'ReformerForSequenceClassification',
'ReformerLayer',
'ReformerModel',
'ReformerModelWithLMHead',
'ReformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
_lowerCAmelCase :int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 263 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> None:
warnings.warn(
'''The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ChineseCLIPImageProcessor instead.''' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase ) | 320 | 0 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __a(SCREAMING_SNAKE_CASE_ : str = "laptop" ):
'''simple docstring'''
_lowerCAmelCase = F'''https://www.amazon.in/laptop/s?k={product}'''
_lowerCAmelCase = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
"Accept-Language": "en-US, en;q=0.5",
}
_lowerCAmelCase = BeautifulSoup(requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).text )
# Initialize a Pandas dataframe with the column titles
_lowerCAmelCase = DataFrame(
columns=[
"Product Title",
"Product Link",
"Current Price of the product",
"Product Rating",
"MRP of the product",
"Discount",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"div" , attrs={"class": "s-result-item", "data-component-type": "s-search-result"} , ) , soup.find_all("div" , attrs={"class": "a-row a-size-base a-color-base"} ) , ):
try:
_lowerCAmelCase = item.ha.text
_lowerCAmelCase = "https://www.amazon.in/" + item.ha.a["href"]
_lowerCAmelCase = item.find("span" , attrs={"class": "a-offscreen"} ).text
try:
_lowerCAmelCase = item.find("span" , attrs={"class": "a-icon-alt"} ).text
except AttributeError:
_lowerCAmelCase = "Not available"
try:
_lowerCAmelCase = (
"₹"
+ item.find(
"span" , attrs={"class": "a-price a-text-price"} ).text.split("₹" )[1]
)
except AttributeError:
_lowerCAmelCase = ""
try:
_lowerCAmelCase = float(
(
(
float(product_mrp.strip("₹" ).replace("," , "" ) )
- float(product_price.strip("₹" ).replace("," , "" ) )
)
/ float(product_mrp.strip("₹" ).replace("," , "" ) )
)
* 100 )
except ValueError:
_lowerCAmelCase = float("nan" )
except AttributeError:
pass
_lowerCAmelCase = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_lowerCAmelCase = " "
_lowerCAmelCase = " "
data_frame.index += 1
return data_frame
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = "headphones"
get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')
| 158 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 320 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self , _UpperCamelCase , _UpperCamelCase=13 , _UpperCamelCase=3 , _UpperCamelCase=224 , _UpperCamelCase=30 , _UpperCamelCase=400 , _UpperCamelCase=True , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=[0.5, 0.5, 0.5] , _UpperCamelCase=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
_lowercase : Tuple = size if size is not None else {"height": 18, "width": 18}
_lowercase : Union[str, Any] = parent
_lowercase : Dict = batch_size
_lowercase : Optional[int] = num_channels
_lowercase : Union[str, Any] = image_size
_lowercase : List[str] = min_resolution
_lowercase : Optional[Any] = max_resolution
_lowercase : Any = do_resize
_lowercase : int = size
_lowercase : Union[str, Any] = do_normalize
_lowercase : Any = image_mean
_lowercase : str = image_std
def _lowerCamelCase ( self ):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class a__ ( a__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = ViTImageProcessor if is_vision_available() else None
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Dict = EfficientFormerImageProcessorTester(self )
@property
def _lowerCamelCase ( self ):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "image_std" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(__UpperCAmelCase , "size" ) )
def _lowerCamelCase ( self ):
"""simple docstring"""
pass
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowercase : List[Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
_lowercase : List[str] = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
_lowercase : str = image_processor(__UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowercase : int = prepare_image_inputs(self.image_proc_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
_lowercase : Any = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
_lowercase : List[Any] = image_processor(__UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowercase : Dict = prepare_image_inputs(self.image_proc_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
_lowercase : Tuple = image_processor(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
# Test batched
_lowercase : Optional[int] = image_processor(__UpperCAmelCase , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
) , )
| 250 |
"""simple docstring"""
def A_ ( ):
"""simple docstring"""
_a = []
_a = 1
while len(_lowerCAmelCase ) < 1e6:
constant.append(str(_lowerCAmelCase ) )
i += 1
_a = ''''''.join(_lowerCAmelCase )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[9_99] )
* int(constant[99_99] )
* int(constant[9_99_99] )
* int(constant[99_99_99] )
)
if __name__ == "__main__":
print(solution()) | 320 | 0 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Tuple:
A_ : int = {}
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1 ) -> int:
if self.graph.get(__UpperCAmelCase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
A_ : str = [[w, v]]
if not self.graph.get(__UpperCAmelCase ):
A_ : List[str] = []
def UpperCAmelCase_ ( self ) -> int:
return list(self.graph )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> List[str]:
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__UpperCAmelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 , _lowerCamelCase=-1 ) -> Optional[int]:
if s == d:
return []
A_ : str = []
A_ : Optional[int] = []
if s == -2:
A_ : Union[str, Any] = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : List[Any] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : List[Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A_ : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__UpperCAmelCase ) != 0:
A_ : Optional[int] = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : str = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return visited
def UpperCAmelCase_ ( self , _lowerCamelCase=-1 ) -> Tuple:
if c == -1:
A_ : List[Any] = floor(random() * 1_0000 ) + 10
for i in range(__UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A_ : Any = floor(random() * c ) + 1
if n != i:
self.add_pair(__UpperCAmelCase , __UpperCAmelCase , 1 )
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 ) -> List[str]:
A_ : List[str] = deque()
A_ : Optional[int] = []
if s == -2:
A_ : Union[str, Any] = list(self.graph )[0]
d.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
while d:
A_ : Optional[Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Tuple:
A_ : Union[str, Any] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Dict:
return len(self.graph[u] )
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 ) -> Tuple:
A_ : List[Any] = []
A_ : List[Any] = []
if s == -2:
A_ : Optional[int] = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : List[str] = s
A_ : Optional[int] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(__UpperCAmelCase ) != 0:
A_ : Optional[Any] = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : Optional[Any] = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return sorted_nodes
def UpperCAmelCase_ ( self ) -> Optional[int]:
A_ : Optional[Any] = []
A_ : str = []
A_ : int = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : Tuple = -2
A_ : int = []
A_ : Any = s
A_ : Optional[Any] = False
A_ : Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : str = len(__UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : Tuple = True
if len(__UpperCAmelCase ) != 0:
A_ : Optional[Any] = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : Tuple = False
indirect_parents.append(__UpperCAmelCase )
A_ : List[Any] = s
A_ : Optional[Any] = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return list(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> Any:
A_ : int = []
A_ : int = []
A_ : int = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : List[str] = -2
A_ : Dict = []
A_ : str = s
A_ : Union[str, Any] = False
A_ : str = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Tuple = len(__UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : List[Any] = True
if len(__UpperCAmelCase ) != 0:
A_ : Optional[int] = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : str = False
indirect_parents.append(__UpperCAmelCase )
A_ : Tuple = s
A_ : Optional[Any] = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return False
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 , _lowerCamelCase=-1 ) -> Optional[int]:
A_ : Union[str, Any] = time()
self.dfs(__UpperCAmelCase , __UpperCAmelCase )
A_ : Dict = time()
return end - begin
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 ) -> Optional[Any]:
A_ : str = time()
self.bfs(__UpperCAmelCase )
A_ : Dict = time()
return end - begin
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
A_ : int = {}
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1 ) -> Dict:
# check if the u exists
if self.graph.get(__UpperCAmelCase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
A_ : Tuple = [[w, v]]
# add the other way
if self.graph.get(__UpperCAmelCase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
A_ : Optional[int] = [[w, u]]
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> Tuple:
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__UpperCAmelCase )
# the other way round
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(__UpperCAmelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 , _lowerCamelCase=-1 ) -> Dict:
if s == d:
return []
A_ : int = []
A_ : Optional[Any] = []
if s == -2:
A_ : List[Any] = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : Union[str, Any] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
A_ : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__UpperCAmelCase ) != 0:
A_ : Union[str, Any] = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : List[str] = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return visited
def UpperCAmelCase_ ( self , _lowerCamelCase=-1 ) -> Tuple:
if c == -1:
A_ : Dict = floor(random() * 1_0000 ) + 10
for i in range(__UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
A_ : Optional[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(__UpperCAmelCase , __UpperCAmelCase , 1 )
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 ) -> List[Any]:
A_ : List[str] = deque()
A_ : Union[str, Any] = []
if s == -2:
A_ : int = list(self.graph )[0]
d.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
while d:
A_ : Optional[int] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Dict:
return len(self.graph[u] )
def UpperCAmelCase_ ( self ) -> int:
A_ : List[Any] = []
A_ : Optional[Any] = []
A_ : Dict = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : Any = -2
A_ : Union[str, Any] = []
A_ : Optional[Any] = s
A_ : Tuple = False
A_ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Tuple = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Optional[Any] = len(__UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : str = True
if len(__UpperCAmelCase ) != 0:
A_ : Tuple = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : str = False
indirect_parents.append(__UpperCAmelCase )
A_ : int = s
A_ : Dict = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return list(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ : List[str] = []
A_ : Union[str, Any] = []
A_ : int = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
A_ : int = -2
A_ : Any = []
A_ : int = s
A_ : Tuple = False
A_ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
A_ : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
A_ : Union[str, Any] = len(__UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
A_ : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
A_ : int = True
if len(__UpperCAmelCase ) != 0:
A_ : str = stack[len(__UpperCAmelCase ) - 1]
else:
A_ : Tuple = False
indirect_parents.append(__UpperCAmelCase )
A_ : Any = s
A_ : Any = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return False
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
return list(self.graph )
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 , _lowerCamelCase=-1 ) -> Tuple:
A_ : int = time()
self.dfs(__UpperCAmelCase , __UpperCAmelCase )
A_ : int = time()
return end - begin
def UpperCAmelCase_ ( self , _lowerCamelCase=-2 ) -> Tuple:
A_ : str = time()
self.bfs(__UpperCAmelCase )
A_ : Optional[int] = time()
return end - begin
| 344 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
# See all BART models at https://huggingface.co/models?filter=bart
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[str] = 'bart'
A_ : Optional[Any] = ['past_key_values']
A_ : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , __UpperCAmelCase=50265 , __UpperCAmelCase=1024 , __UpperCAmelCase=12 , __UpperCAmelCase=4096 , __UpperCAmelCase=16 , __UpperCAmelCase=12 , __UpperCAmelCase=4096 , __UpperCAmelCase=16 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase="gelu" , __UpperCAmelCase=1024 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.02 , __UpperCAmelCase=0.0 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=3 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=2 , __UpperCAmelCase=2 , **__UpperCAmelCase , ) -> Tuple:
_a = vocab_size
_a = max_position_embeddings
_a = d_model
_a = encoder_ffn_dim
_a = encoder_layers
_a = encoder_attention_heads
_a = decoder_ffn_dim
_a = decoder_layers
_a = decoder_attention_heads
_a = dropout
_a = attention_dropout
_a = activation_dropout
_a = activation_function
_a = init_std
_a = encoder_layerdrop
_a = decoder_layerdrop
_a = classifier_dropout
_a = use_cache
_a = encoder_layers
_a = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , is_encoder_decoder=__UpperCAmelCase , decoder_start_token_id=__UpperCAmelCase , forced_eos_token_id=__UpperCAmelCase , **__UpperCAmelCase , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' , __UpperCAmelCase ):
_a = self.bos_token_id
warnings.warn(
F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'''The config can simply be saved and uploaded again to be fixed.''' )
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_a = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_a = {0: '''batch'''}
_a = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''decoder_sequence'''}
_a = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
_a = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
_a , _a = self.num_layers
for i in range(__UpperCAmelCase ):
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
_a = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_a = super().outputs
else:
_a = super(__UpperCAmelCase , self ).outputs
if self.use_past:
_a , _a = self.num_layers
for i in range(__UpperCAmelCase ):
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
_a = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Generate decoder inputs
_a = seq_length if not self.use_past else 1
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
_a = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_a = dict(**__UpperCAmelCase , **__UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
_a = common_inputs['''decoder_input_ids'''].shape[1]
_a , _a = self.num_attention_heads
_a = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_a = decoder_seq_length + 3
_a = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_a = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase )] , dim=1 )
_a = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_a , _a = self.num_layers
_a = min(__UpperCAmelCase , __UpperCAmelCase )
_a = max(__UpperCAmelCase , __UpperCAmelCase ) - min_num_layers
_a = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(__UpperCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
torch.zeros(__UpperCAmelCase ),
) )
# TODO: test this.
_a = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(__UpperCAmelCase , __UpperCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) )
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_a = seqlen + 2
_a , _a = self.num_layers
_a , _a = self.num_attention_heads
_a = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_a = common_inputs['''attention_mask'''].dtype
_a = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
_a = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(__UpperCAmelCase )
]
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_a = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_a = tokenizer.num_special_tokens_to_add(__UpperCAmelCase )
_a = compute_effective_axis_dimension(
__UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__UpperCAmelCase )
# Generate dummy inputs according to compute batch and sequence
_a = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
_a = dict(tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase ) )
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_a = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
elif self.task == "causal-lm":
_a = self._generate_dummy_inputs_for_causal_lm(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
else:
_a = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
return common_inputs
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
if self.task in ["default", "seq2seq-lm"]:
_a = super()._flatten_past_key_values_(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
_a = super(__UpperCAmelCase , self )._flatten_past_key_values_(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) | 320 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class _snake_case :
'''simple docstring'''
def __init__( self: Any ,lowerCamelCase_: List[str] ,lowerCamelCase_: int=99 ,lowerCamelCase_: Optional[Any]=13 ,lowerCamelCase_: str=7 ,lowerCamelCase_: Any=9 ,lowerCamelCase_: str=True ,lowerCamelCase_: List[Any]=True ,lowerCamelCase_: Optional[Any]=False ,lowerCamelCase_: Tuple=32 ,lowerCamelCase_: Optional[int]=5 ,lowerCamelCase_: Optional[int]=4 ,lowerCamelCase_: str=37 ,lowerCamelCase_: Tuple=8 ,lowerCamelCase_: Union[str, Any]=0.1 ,lowerCamelCase_: Dict=0.0_0_2 ,lowerCamelCase_: int=1 ,lowerCamelCase_: Union[str, Any]=0 ,lowerCamelCase_: Any=0 ,lowerCamelCase_: List[Any]=None ,lowerCamelCase_: Tuple=None ,) -> Optional[int]:
UpperCAmelCase_ : Optional[int] = parent
UpperCAmelCase_ : Any = batch_size
UpperCAmelCase_ : int = encoder_seq_length
UpperCAmelCase_ : int = decoder_seq_length
# For common tests
UpperCAmelCase_ : Tuple = self.decoder_seq_length
UpperCAmelCase_ : Dict = is_training
UpperCAmelCase_ : Any = use_attention_mask
UpperCAmelCase_ : str = use_labels
UpperCAmelCase_ : Optional[Any] = vocab_size
UpperCAmelCase_ : Any = hidden_size
UpperCAmelCase_ : Tuple = num_hidden_layers
UpperCAmelCase_ : List[Any] = num_attention_heads
UpperCAmelCase_ : Any = d_ff
UpperCAmelCase_ : Tuple = relative_attention_num_buckets
UpperCAmelCase_ : Optional[Any] = dropout_rate
UpperCAmelCase_ : List[str] = initializer_factor
UpperCAmelCase_ : Tuple = eos_token_id
UpperCAmelCase_ : Tuple = pad_token_id
UpperCAmelCase_ : List[Any] = decoder_start_token_id
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : Optional[Any] = decoder_layers
def A__ ( self: Dict ) -> Dict:
return TaConfig.from_pretrained("""google/umt5-base""" )
def A__ ( self: List[Any] ,lowerCamelCase_: Any ,lowerCamelCase_: Tuple ,lowerCamelCase_: Optional[int] ,lowerCamelCase_: Optional[int]=None ,lowerCamelCase_: Any=None ,lowerCamelCase_: Union[str, Any]=None ,lowerCamelCase_: List[str]=None ,lowerCamelCase_: str=None ,) -> Optional[int]:
if attention_mask is None:
UpperCAmelCase_ : str = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
UpperCAmelCase_ : Union[str, Any] = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
UpperCAmelCase_ : Union[str, Any] = torch.ones(config.num_hidden_layers ,config.num_attention_heads ,device=__UpperCAmelCase )
if decoder_head_mask is None:
UpperCAmelCase_ : Optional[Any] = torch.ones(config.num_decoder_layers ,config.num_attention_heads ,device=__UpperCAmelCase )
if cross_attn_head_mask is None:
UpperCAmelCase_ : Dict = torch.ones(
config.num_decoder_layers ,config.num_attention_heads ,device=__UpperCAmelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def A__ ( self: Any ) -> Tuple:
UpperCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.encoder_seq_length] ,self.vocab_size )
UpperCAmelCase_ : int = ids_tensor([self.batch_size, self.decoder_seq_length] ,self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
UpperCAmelCase_ : int = input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase_ : int = decoder_input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase_ : int = self.get_config()
UpperCAmelCase_ : Tuple = config.num_attention_heads
UpperCAmelCase_ : str = self.prepare_inputs_dict(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase )
return config, input_dict
def A__ ( self: Optional[Any] ) -> int:
UpperCAmelCase_ , UpperCAmelCase_ : Dict = self.prepare_config_and_inputs()
return config, inputs_dict
def A__ ( self: List[str] ) -> Tuple:
return TaConfig(
vocab_size=166 ,d_model=self.hidden_size ,d_ff=self.d_ff ,d_kv=self.hidden_size // self.num_attention_heads ,num_layers=self.num_hidden_layers ,num_decoder_layers=self.decoder_layers ,num_heads=self.num_attention_heads ,relative_attention_num_buckets=self.relative_attention_num_buckets ,dropout_rate=self.dropout_rate ,initializer_factor=self.initializer_factor ,eos_token_id=self.eos_token_id ,bos_token_id=self.pad_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.decoder_start_token_id ,)
def A__ ( self: List[Any] ) -> List[str]:
return TaConfig(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,d_ff=self.d_ff ,d_kv=self.hidden_size // self.num_attention_heads ,num_layers=self.num_hidden_layers ,num_decoder_layers=self.decoder_layers ,num_heads=self.num_attention_heads ,relative_attention_num_buckets=self.relative_attention_num_buckets ,dropout_rate=self.dropout_rate ,initializer_factor=self.initializer_factor ,eos_token_id=self.eos_token_id ,bos_token_id=self.pad_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.decoder_start_token_id ,)
def A__ ( self: Optional[int] ,lowerCamelCase_: Any ,lowerCamelCase_: List[Any] ,lowerCamelCase_: Optional[Any] ,lowerCamelCase_: Dict ,lowerCamelCase_: Dict ,lowerCamelCase_: Optional[int] ,) -> Dict:
UpperCAmelCase_ : Any = UMTaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCAmelCase_ : List[str] = model(
input_ids=__UpperCAmelCase ,decoder_input_ids=__UpperCAmelCase ,attention_mask=__UpperCAmelCase ,decoder_attention_mask=__UpperCAmelCase ,)
UpperCAmelCase_ : Tuple = model(input_ids=__UpperCAmelCase ,decoder_input_ids=__UpperCAmelCase )
UpperCAmelCase_ : Any = result.last_hidden_state
UpperCAmelCase_ : Dict = result.past_key_values
UpperCAmelCase_ : Optional[Any] = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() ,(self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() ,(self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__UpperCAmelCase ) ,config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) ,4 )
def A__ ( self: Dict ,lowerCamelCase_: Union[str, Any] ,lowerCamelCase_: str ,lowerCamelCase_: List[str] ,lowerCamelCase_: Dict ,lowerCamelCase_: Optional[int] ,lowerCamelCase_: Optional[int] ,) -> Optional[Any]:
UpperCAmelCase_ : Any = UMTaModel(config=__UpperCAmelCase ).get_decoder().to(__UpperCAmelCase ).eval()
# first forward pass
UpperCAmelCase_ : List[Any] = model(__UpperCAmelCase ,use_cache=__UpperCAmelCase )
UpperCAmelCase_ : Optional[int] = model(__UpperCAmelCase )
UpperCAmelCase_ : List[Any] = model(__UpperCAmelCase ,use_cache=__UpperCAmelCase )
self.parent.assertTrue(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) )
self.parent.assertTrue(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) + 1 )
UpperCAmelCase_ , UpperCAmelCase_ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase_ : Any = ids_tensor((self.batch_size, 1) ,config.vocab_size )
# append to next input_ids and
UpperCAmelCase_ : int = torch.cat([input_ids, next_tokens] ,dim=-1 )
UpperCAmelCase_ : List[str] = model(__UpperCAmelCase )["""last_hidden_state"""]
UpperCAmelCase_ : Optional[int] = model(__UpperCAmelCase ,past_key_values=__UpperCAmelCase )["""last_hidden_state"""]
# select random slice
UpperCAmelCase_ : Any = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
UpperCAmelCase_ : str = output_from_no_past[:, -1, random_slice_idx].detach()
UpperCAmelCase_ : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCAmelCase ,__UpperCAmelCase ,atol=1e-3 ) )
def A__ ( self: Optional[Any] ,lowerCamelCase_: Union[str, Any] ,lowerCamelCase_: Union[str, Any] ,) -> Union[str, Any]:
UpperCAmelCase_ : List[str] = UMTaModel(config=__UpperCAmelCase ).to(__UpperCAmelCase ).half().eval()
UpperCAmelCase_ : int = model(**__UpperCAmelCase )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(__UpperCAmelCase ).any().item() )
@require_torch
class _snake_case ( a__ , a__ , a__ , unittest.TestCase ):
'''simple docstring'''
A__ : Optional[Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
A__ : Optional[Any] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
A__ : int = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
A__ : str = True
A__ : List[str] = False
A__ : List[Any] = False
A__ : str = True
A__ : List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
A__ : Optional[Any] = [0.8, 0.9]
def A__ ( self: str ) -> Tuple:
UpperCAmelCase_ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def A__ ( self: str ) -> int:
UpperCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : Any = UMTaModel(config_and_inputs[0] ).to(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__UpperCAmelCase ,(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,F'''{tmpdirname}/t5_test.onnx''' ,export_params=__UpperCAmelCase ,opset_version=9 ,input_names=["""input_ids""", """decoder_input_ids"""] ,)
@unittest.skipIf(torch_device == """cpu""" ,"""Cant do half precision""" )
def A__ ( self: Tuple ) -> Union[str, Any]:
UpperCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__UpperCAmelCase )
def A__ ( self: Tuple ) -> Union[str, Any]:
UpperCAmelCase_ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ : List[Any] = config_and_inputs[0]
UpperCAmelCase_ : Optional[Any] = UMTaForConditionalGeneration(__UpperCAmelCase ).eval()
model.to(__UpperCAmelCase )
UpperCAmelCase_ : str = {
"""head_mask""": torch.zeros(config.num_layers ,config.num_heads ,device=__UpperCAmelCase ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers ,config.num_heads ,device=__UpperCAmelCase ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers ,config.num_heads ,device=__UpperCAmelCase ),
}
for attn_name, (name, mask) in zip(__UpperCAmelCase ,head_masking.items() ):
UpperCAmelCase_ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
UpperCAmelCase_ : Any = torch.ones(
config.num_decoder_layers ,config.num_heads ,device=__UpperCAmelCase )
UpperCAmelCase_ : str = model.generate(
config_and_inputs[1]["""input_ids"""] ,num_beams=1 ,max_length=3 ,output_attentions=__UpperCAmelCase ,return_dict_in_generate=__UpperCAmelCase ,**__UpperCAmelCase ,)
# We check the state of decoder_attentions and cross_attentions just from the last step
UpperCAmelCase_ : Optional[int] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) ,0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def A__ ( self: List[str] ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _snake_case ( unittest.TestCase ):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def A__ ( self: Union[str, Any] ) -> Optional[int]:
UpperCAmelCase_ : Union[str, Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" ,return_dict=__UpperCAmelCase ).to(__UpperCAmelCase )
UpperCAmelCase_ : Dict = AutoTokenizer.from_pretrained("""google/umt5-small""" ,use_fast=__UpperCAmelCase ,legacy=__UpperCAmelCase )
UpperCAmelCase_ : List[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
UpperCAmelCase_ : Dict = tokenizer(__UpperCAmelCase ,return_tensors="""pt""" ,padding=__UpperCAmelCase ).input_ids
# fmt: off
UpperCAmelCase_ : Tuple = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(__UpperCAmelCase ,__UpperCAmelCase )
UpperCAmelCase_ : Union[str, Any] = model.generate(input_ids.to(__UpperCAmelCase ) )
UpperCAmelCase_ : Tuple = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
UpperCAmelCase_ : str = tokenizer.batch_decode(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase ,__UpperCAmelCase )
| 345 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <= 0x4dbf) #
or (cp >= 0x2_0000 and cp <= 0x2_a6df) #
or (cp >= 0x2_a700 and cp <= 0x2_b73f) #
or (cp >= 0x2_b740 and cp <= 0x2_b81f) #
or (cp >= 0x2_b820 and cp <= 0x2_ceaf) #
or (cp >= 0xf900 and cp <= 0xfaff)
or (cp >= 0x2_f800 and cp <= 0x2_fa1f) #
): #
return True
return False
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
for char in word:
_a = ord(_lowerCAmelCase )
if not _is_chinese_char(_lowerCAmelCase ):
return 0
return 1
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_a = set()
for token in tokens:
_a = len(_lowerCAmelCase ) > 1 and is_chinese(_lowerCAmelCase )
if chinese_word:
word_set.add(_lowerCAmelCase )
_a = list(_lowerCAmelCase )
return word_list
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : set() ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_a = max([len(_lowerCAmelCase ) for w in chinese_word_set] )
_a = bert_tokens
_a , _a = 0, len(_lowerCAmelCase )
while start < end:
_a = True
if is_chinese(bert_word[start] ):
_a = min(end - start, _lowerCAmelCase )
for i in range(_lowerCAmelCase, 1, -1 ):
_a = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1, start + i ):
_a = '''##''' + bert_word[j]
_a = start + i
_a = False
break
if single_word:
start += 1
return bert_word
def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : LTP, _lowerCAmelCase : BertTokenizer ):
"""simple docstring"""
_a = []
for i in range(0, len(_lowerCAmelCase ), 1_00 ):
_a = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws
_a = [get_chinese_word(_lowerCAmelCase ) for r in res]
ltp_res.extend(_lowerCAmelCase )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
_a = []
for i in range(0, len(_lowerCAmelCase ), 1_00 ):
_a = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=_lowerCAmelCase, truncation=_lowerCAmelCase, max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )
_a = []
for input_ids, chinese_word in zip(_lowerCAmelCase, _lowerCAmelCase ):
_a = []
for id in input_ids:
_a = bert_tokenizer._convert_id_to_token(_lowerCAmelCase )
input_tokens.append(_lowerCAmelCase )
_a = add_sub_symbol(_lowerCAmelCase, _lowerCAmelCase )
_a = []
# 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 = 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 A_ ( _lowerCAmelCase : Any ):
"""simple docstring"""
with open(args.file_name, '''r''', encoding='''utf-8''' ) as f:
_a = f.readlines()
_a = [line.strip() for line in data if len(_lowerCAmelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_a = LTP(args.ltp ) # faster in GPU device
_a = BertTokenizer.from_pretrained(args.bert )
_a = prepare_ref(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
with open(args.save_path, '''w''', encoding='''utf-8''' ) as f:
_a = [json.dumps(_lowerCAmelCase ) + '''\n''' for ref in ref_ids]
f.writelines(_lowerCAmelCase )
if __name__ == "__main__":
__snake_case = 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''',
)
__snake_case = parser.parse_args()
main(args) | 320 | 0 |
"""simple docstring"""
class UpperCamelCase : # Public class to implement a graph
def __init__( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__) -> None:
snake_case_ = row
snake_case_ = col
snake_case_ = graph
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__) -> bool:
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__) -> None:
# Checking all 8 elements surrounding nth element
snake_case_ = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
snake_case_ = [-1, 0, 1, -1, 1, -1, 0, 1]
snake_case_ = True # Make those cells visited
for k in range(8):
if self.is_safe(i + row_nbr[k], j + col_nbr[k], __UpperCAmelCase):
self.diffs(i + row_nbr[k], j + col_nbr[k], __UpperCAmelCase)
def a_ ( self) -> int: # And finally, count all islands.
snake_case_ = [[False for j in range(self.COL)] for i in range(self.ROW)]
snake_case_ = 0
for i in range(self.ROW):
for j in range(self.COL):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase)
count += 1
return count
| 69 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[Any] = 'gptj'
A_ : Optional[int] = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , __UpperCAmelCase=50400 , __UpperCAmelCase=2048 , __UpperCAmelCase=4096 , __UpperCAmelCase=28 , __UpperCAmelCase=16 , __UpperCAmelCase=64 , __UpperCAmelCase=None , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=1e-5 , __UpperCAmelCase=0.02 , __UpperCAmelCase=True , __UpperCAmelCase=50256 , __UpperCAmelCase=50256 , __UpperCAmelCase=False , **__UpperCAmelCase , ) -> Union[str, Any]:
_a = vocab_size
_a = n_positions
_a = n_embd
_a = n_layer
_a = n_head
_a = n_inner
_a = rotary_dim
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = use_cache
_a = bos_token_id
_a = eos_token_id
super().__init__(
bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , tie_word_embeddings=__UpperCAmelCase , **__UpperCAmelCase )
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = "default" , __UpperCAmelCase = None , __UpperCAmelCase = False , ) -> Optional[Any]:
super().__init__(__UpperCAmelCase , task=__UpperCAmelCase , patching_specs=__UpperCAmelCase , use_past=__UpperCAmelCase )
if not getattr(self._config , '''pad_token_id''' , __UpperCAmelCase ):
# TODO: how to do that better?
_a = 0
@property
def _UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
_a = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction='''inputs''' )
_a = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def _UpperCAmelCase ( self ) -> int:
return self._config.n_layer
@property
def _UpperCAmelCase ( self ) -> int:
return self._config.n_head
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ) -> Mapping[str, Any]:
_a = super(__UpperCAmelCase , self ).generate_dummy_inputs(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
_a = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_a = seqlen + 2
_a = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_a = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(self.num_layers )
]
_a = common_inputs['''attention_mask''']
if self.use_past:
_a = ordered_inputs['''attention_mask'''].dtype
_a = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def _UpperCAmelCase ( self ) -> int:
return 13 | 320 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
'''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = ['''MaskFormerFeatureExtractor''']
lowerCamelCase = ['''MaskFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MaskFormerForInstanceSegmentation''',
'''MaskFormerModel''',
'''MaskFormerPreTrainedModel''',
]
lowerCamelCase = [
'''MaskFormerSwinBackbone''',
'''MaskFormerSwinModel''',
'''MaskFormerSwinPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 188 |
"""simple docstring"""
import os
import sys
import unittest
__snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__snake_case = os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
__snake_case = os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ) -> str:
_a = get_test_to_tester_mapping(__UpperCAmelCase )
_a = get_test_to_tester_mapping(__UpperCAmelCase )
_a = {'''BertModelTest''': '''BertModelTester'''}
_a = {
'''BlipModelTest''': '''BlipModelTester''',
'''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
'''BlipTextModelTest''': '''BlipTextModelTester''',
'''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
'''BlipVQAModelTest''': '''BlipVQAModelTester''',
'''BlipVisionModelTest''': '''BlipVisionModelTester''',
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = get_model_to_test_mapping(__UpperCAmelCase )
_a = get_model_to_test_mapping(__UpperCAmelCase )
_a = {
'''BertForMaskedLM''': ['''BertModelTest'''],
'''BertForMultipleChoice''': ['''BertModelTest'''],
'''BertForNextSentencePrediction''': ['''BertModelTest'''],
'''BertForPreTraining''': ['''BertModelTest'''],
'''BertForQuestionAnswering''': ['''BertModelTest'''],
'''BertForSequenceClassification''': ['''BertModelTest'''],
'''BertForTokenClassification''': ['''BertModelTest'''],
'''BertLMHeadModel''': ['''BertModelTest'''],
'''BertModel''': ['''BertModelTest'''],
}
_a = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
'''BlipModel''': ['''BlipModelTest'''],
'''BlipTextModel''': ['''BlipTextModelTest'''],
'''BlipVisionModel''': ['''BlipVisionModelTest'''],
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = get_model_to_tester_mapping(__UpperCAmelCase )
_a = get_model_to_tester_mapping(__UpperCAmelCase )
_a = {
'''BertForMaskedLM''': ['''BertModelTester'''],
'''BertForMultipleChoice''': ['''BertModelTester'''],
'''BertForNextSentencePrediction''': ['''BertModelTester'''],
'''BertForPreTraining''': ['''BertModelTester'''],
'''BertForQuestionAnswering''': ['''BertModelTester'''],
'''BertForSequenceClassification''': ['''BertModelTester'''],
'''BertForTokenClassification''': ['''BertModelTester'''],
'''BertLMHeadModel''': ['''BertModelTester'''],
'''BertModel''': ['''BertModelTester'''],
}
_a = {
'''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
'''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
'''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
'''BlipModel''': ['''BlipModelTester'''],
'''BlipTextModel''': ['''BlipTextModelTester'''],
'''BlipVisionModel''': ['''BlipVisionModelTester'''],
}
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(get_test_info.to_json(__UpperCAmelCase ) , __UpperCAmelCase ) | 320 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
_A : Any = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase_ ( snake_case_ = 100 ):
_A : Tuple = 1
_A : str = 2
for i in range(2,max_n + 1 ):
_A : Dict = pre_numerator
_A : List[str] = 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() = }""")
| 26 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __lowerCamelCase :
'''simple docstring'''
@staticmethod
def _UpperCAmelCase ( *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
pass
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_a = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def A_ ( _lowerCAmelCase : Image ):
"""simple docstring"""
_a = np.array(_lowerCAmelCase )
_a = npimg.shape
return {"hash": hashimage(_lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
A_ : Any = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
A_ : str = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
_a = MaskGenerationPipeline(model=__UpperCAmelCase , image_processor=__UpperCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> int:
pass
@require_tf
@unittest.skip('''Image segmentation not implemented in TF''' )
def _UpperCAmelCase ( self ) -> List[str]:
pass
@slow
@require_torch
def _UpperCAmelCase ( self ) -> int:
_a = pipeline('''mask-generation''' , model='''facebook/sam-vit-huge''' )
_a = image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg''' , points_per_batch=256 )
# Shortening by hashing
_a = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.021},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
{'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.9967},
{'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.993},
{'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.9909},
{'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.9879},
{'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.9834},
{'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.9716},
{'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.9612},
{'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.9599},
{'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.9552},
{'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.9532},
{'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.9516},
{'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.9499},
{'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.9483},
{'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.9464},
{'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.943},
{'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.943},
{'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.9408},
{'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.9335},
{'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.9326},
{'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.9262},
{'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.8999},
{'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.8986},
{'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.8984},
{'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.8873},
{'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def _UpperCAmelCase ( self ) -> Any:
_a = '''facebook/sam-vit-huge'''
_a = pipeline('''mask-generation''' , model=__UpperCAmelCase )
_a = image_segmenter(
'''http://images.cocodataset.org/val2017/000000039769.jpg''' , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
_a = []
for i, o in enumerate(outputs['''masks'''] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.0444},
{'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0210},
{'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.0167},
{'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.0132},
{'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.0053},
] , ) | 320 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
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
__lowerCAmelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __magic_name__ ( a__ ):
lowerCAmelCase : List[str] = ['pixel_values']
def __init__( self : Tuple ,_UpperCAmelCase : Optional[int] = True ,_UpperCAmelCase : Tuple = None ,_UpperCAmelCase : Optional[int] = PILImageResampling.BICUBIC ,_UpperCAmelCase : int = True ,_UpperCAmelCase : List[Any] = None ,_UpperCAmelCase : Union[str, Any] = True ,_UpperCAmelCase : Dict = 1 / 255 ,_UpperCAmelCase : Optional[int] = True ,_UpperCAmelCase : int = None ,_UpperCAmelCase : int = None ,_UpperCAmelCase : Any = True ,**_UpperCAmelCase : List[str] ,):
super().__init__(**__UpperCAmelCase )
_a : List[str] = size if size is not None else {'shortest_edge': 224}
_a : Any = get_size_dict(__UpperCAmelCase ,default_to_square=__UpperCAmelCase )
_a : Union[str, Any] = crop_size if crop_size is not None else {'height': 224, 'width': 224}
_a : int = get_size_dict(__UpperCAmelCase ,default_to_square=__UpperCAmelCase ,param_name='crop_size' )
_a : List[Any] = do_resize
_a : int = size
_a : Optional[int] = resample
_a : Tuple = do_center_crop
_a : Any = crop_size
_a : int = do_rescale
_a : Union[str, Any] = rescale_factor
_a : List[str] = do_normalize
_a : Tuple = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_a : Union[str, Any] = image_std if image_std is not None else OPENAI_CLIP_STD
_a : Optional[int] = do_convert_rgb
def __lowercase ( self : Any ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : int ,_UpperCAmelCase : List[Any] = PILImageResampling.BICUBIC ,_UpperCAmelCase : List[str] = None ,**_UpperCAmelCase : str ,):
_a : int = get_size_dict(__UpperCAmelCase ,default_to_square=__UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
_a : int = get_resize_output_image_size(__UpperCAmelCase ,size=size['shortest_edge'] ,default_to_square=__UpperCAmelCase )
return resize(__UpperCAmelCase ,size=__UpperCAmelCase ,resample=__UpperCAmelCase ,data_format=__UpperCAmelCase ,**__UpperCAmelCase )
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : Dict ,_UpperCAmelCase : Dict ,_UpperCAmelCase : str = None ,**_UpperCAmelCase : Dict ,):
_a : Any = get_size_dict(__UpperCAmelCase )
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(__UpperCAmelCase ,size=(size['height'], size['width']) ,data_format=__UpperCAmelCase ,**__UpperCAmelCase )
def __lowercase ( self : Tuple ,_UpperCAmelCase : str ,_UpperCAmelCase : Dict ,_UpperCAmelCase : int = None ,**_UpperCAmelCase : Dict ,):
return rescale(__UpperCAmelCase ,scale=__UpperCAmelCase ,data_format=__UpperCAmelCase ,**__UpperCAmelCase )
def __lowercase ( self : Tuple ,_UpperCAmelCase : Dict ,_UpperCAmelCase : Any ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Any = None ,**_UpperCAmelCase : Union[str, Any] ,):
return normalize(__UpperCAmelCase ,mean=__UpperCAmelCase ,std=__UpperCAmelCase ,data_format=__UpperCAmelCase ,**__UpperCAmelCase )
def __lowercase ( self : Dict ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : Tuple = None ,_UpperCAmelCase : Optional[Any] = None ,_UpperCAmelCase : Any = None ,_UpperCAmelCase : Dict = None ,_UpperCAmelCase : Union[str, Any] = None ,_UpperCAmelCase : Any = None ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : Optional[int] = None ,_UpperCAmelCase : Union[str, Any] = None ,_UpperCAmelCase : str = None ,_UpperCAmelCase : Dict = None ,_UpperCAmelCase : Any = None ,_UpperCAmelCase : str = ChannelDimension.FIRST ,**_UpperCAmelCase : Any ,):
_a : List[Any] = do_resize if do_resize is not None else self.do_resize
_a : int = size if size is not None else self.size
_a : int = get_size_dict(__UpperCAmelCase ,param_name='size' ,default_to_square=__UpperCAmelCase )
_a : Tuple = resample if resample is not None else self.resample
_a : str = 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 : Dict = get_size_dict(__UpperCAmelCase ,param_name='crop_size' ,default_to_square=__UpperCAmelCase )
_a : Union[str, 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 : int = do_normalize if do_normalize is not None else self.do_normalize
_a : Tuple = image_mean if image_mean is not None else self.image_mean
_a : Any = image_std if image_std is not None else self.image_std
_a : Optional[Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_a : Optional[int] = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
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 : Any = [convert_to_rgb(__UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
_a : Any = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
_a : Tuple = [self.resize(image=__UpperCAmelCase ,size=__UpperCAmelCase ,resample=__UpperCAmelCase ) for image in images]
if do_center_crop:
_a : int = [self.center_crop(image=__UpperCAmelCase ,size=__UpperCAmelCase ) for image in images]
if do_rescale:
_a : Union[str, Any] = [self.rescale(image=__UpperCAmelCase ,scale=__UpperCAmelCase ) for image in images]
if do_normalize:
_a : Union[str, Any] = [self.normalize(image=__UpperCAmelCase ,mean=__UpperCAmelCase ,std=__UpperCAmelCase ) for image in images]
_a : str = [to_channel_dimension_format(__UpperCAmelCase ,__UpperCAmelCase ) for image in images]
_a : List[Any] = {'pixel_values': images}
return BatchFeature(data=__UpperCAmelCase ,tensor_type=__UpperCAmelCase )
| 89 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __lowerCamelCase :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=99 , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=9 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase=8 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.002 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=0 , __UpperCAmelCase=None , __UpperCAmelCase=None , ) -> Optional[int]:
_a = parent
_a = batch_size
_a = encoder_seq_length
_a = decoder_seq_length
# For common tests
_a = self.decoder_seq_length
_a = is_training
_a = use_attention_mask
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = d_ff
_a = relative_attention_num_buckets
_a = dropout_rate
_a = initializer_factor
_a = eos_token_id
_a = pad_token_id
_a = decoder_start_token_id
_a = None
_a = decoder_layers
def _UpperCAmelCase ( self ) -> Dict:
return TaConfig.from_pretrained('''google/umt5-base''' )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , ) -> Optional[int]:
if attention_mask is None:
_a = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_a = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_a = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=__UpperCAmelCase )
if decoder_head_mask is None:
_a = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=__UpperCAmelCase )
if cross_attn_head_mask is None:
_a = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=__UpperCAmelCase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _UpperCAmelCase ( self ) -> Tuple:
_a = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_a = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_a = input_ids.clamp(self.pad_token_id + 1 )
_a = decoder_input_ids.clamp(self.pad_token_id + 1 )
_a = self.get_config()
_a = config.num_attention_heads
_a = self.prepare_inputs_dict(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return config, input_dict
def _UpperCAmelCase ( self ) -> int:
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def _UpperCAmelCase ( self ) -> Tuple:
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _UpperCAmelCase ( self ) -> List[str]:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Dict:
_a = UMTaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
_a = model(
input_ids=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase , attention_mask=__UpperCAmelCase , decoder_attention_mask=__UpperCAmelCase , )
_a = model(input_ids=__UpperCAmelCase , decoder_input_ids=__UpperCAmelCase )
_a = result.last_hidden_state
_a = result.past_key_values
_a = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(__UpperCAmelCase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> Optional[Any]:
_a = UMTaModel(config=__UpperCAmelCase ).get_decoder().to(__UpperCAmelCase ).eval()
# first forward pass
_a = model(__UpperCAmelCase , use_cache=__UpperCAmelCase )
_a = model(__UpperCAmelCase )
_a = model(__UpperCAmelCase , use_cache=__UpperCAmelCase )
self.parent.assertTrue(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) )
self.parent.assertTrue(len(__UpperCAmelCase ) == len(__UpperCAmelCase ) + 1 )
_a , _a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = model(__UpperCAmelCase )['''last_hidden_state''']
_a = model(__UpperCAmelCase , past_key_values=__UpperCAmelCase )['''last_hidden_state''']
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -1, random_slice_idx].detach()
_a = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1e-3 ) )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , ) -> Union[str, Any]:
_a = UMTaModel(config=__UpperCAmelCase ).to(__UpperCAmelCase ).half().eval()
_a = model(**__UpperCAmelCase )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(__UpperCAmelCase ).any().item() )
@require_torch
class __lowerCamelCase ( a__ , a__ , a__ , unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
A_ : Optional[Any] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
A_ : int = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
A_ : str = True
A_ : List[str] = False
A_ : List[Any] = False
A_ : str = True
A_ : List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
A_ : Optional[Any] = [0.8, 0.9]
def _UpperCAmelCase ( self ) -> Tuple:
_a = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def _UpperCAmelCase ( self ) -> int:
_a = self.model_tester.prepare_config_and_inputs()
_a = UMTaModel(config_and_inputs[0] ).to(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
__UpperCAmelCase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=__UpperCAmelCase , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
_a = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
_a = self.model_tester.prepare_config_and_inputs()
_a = config_and_inputs[0]
_a = UMTaForConditionalGeneration(__UpperCAmelCase ).eval()
model.to(__UpperCAmelCase )
_a = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=__UpperCAmelCase ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCAmelCase ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=__UpperCAmelCase ),
}
for attn_name, (name, mask) in zip(__UpperCAmelCase , head_masking.items() ):
_a = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_a = torch.ones(
config.num_decoder_layers , config.num_heads , device=__UpperCAmelCase )
_a = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=__UpperCAmelCase , return_dict_in_generate=__UpperCAmelCase , **__UpperCAmelCase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_a = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
def _UpperCAmelCase ( self ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
@unittest.skip(
'''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
def _UpperCAmelCase ( self ) -> Optional[int]:
_a = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=__UpperCAmelCase ).to(__UpperCAmelCase )
_a = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=__UpperCAmelCase , legacy=__UpperCAmelCase )
_a = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
_a = tokenizer(__UpperCAmelCase , return_tensors='''pt''' , padding=__UpperCAmelCase ).input_ids
# fmt: off
_a = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(__UpperCAmelCase , __UpperCAmelCase )
_a = model.generate(input_ids.to(__UpperCAmelCase ) )
_a = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
_a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase ) | 320 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any]=13 , lowerCAmelCase__ :Optional[Any]=3 , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Any=224 , lowerCAmelCase__ :int=1_000 , lowerCAmelCase__ :Tuple=[3, 3, 6, 4] , lowerCAmelCase__ :Optional[Any]=[48, 56, 112, 220] , ) -> str:
__SCREAMING_SNAKE_CASE : Optional[int] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : Dict = num_channels
__SCREAMING_SNAKE_CASE : int = is_training
__SCREAMING_SNAKE_CASE : Dict = use_labels
__SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : List[str] = image_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = layer_depths
__SCREAMING_SNAKE_CASE : str = embed_dims
def __magic_name__( self :Dict ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : Any = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def __magic_name__( self :Optional[int] ) -> List[str]:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=__UpperCAmelCase , layer_scale_init_value=1E-5 , )
def __magic_name__( self :List[Any] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> int:
__SCREAMING_SNAKE_CASE : Any = SwiftFormerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Dict = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __magic_name__( self :List[str] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Any ) -> str:
__SCREAMING_SNAKE_CASE : Dict = self.num_labels
__SCREAMING_SNAKE_CASE : Tuple = SwiftFormerForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Any = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = SwiftFormerForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE : int = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__( self :Any ) -> List[str]:
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) : Any = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( a__ , a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ : Optional[int] = (
{'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ : Dict = False
SCREAMING_SNAKE_CASE__ : List[Any] = False
SCREAMING_SNAKE_CASE__ : Any = False
SCREAMING_SNAKE_CASE__ : Optional[int] = False
SCREAMING_SNAKE_CASE__ : Optional[int] = False
def __magic_name__( self :Dict ) -> List[str]:
__SCREAMING_SNAKE_CASE : Dict = SwiftFormerModelTester(self )
__SCREAMING_SNAKE_CASE : Union[str, Any] = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def __magic_name__( self :str ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __magic_name__( self :Any ) -> Optional[Any]:
pass
def __magic_name__( self :Optional[int] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Optional[Any] = model_class(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def __magic_name__( self :int ) -> List[str]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : Dict = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def __magic_name__( self :int ) -> str:
__SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def __magic_name__( self :Any ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@slow
def __magic_name__( self :Optional[Any] ) -> Tuple:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Dict = SwiftFormerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __magic_name__( self :Optional[int] ) -> List[str]:
pass
def __magic_name__( self :str ) -> Any:
def check_hidden_states_output(lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Optional[int] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE : Dict = outputs.hidden_states
__SCREAMING_SNAKE_CASE : Union[str, Any] = 8
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(__UpperCAmelCase ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE : Optional[int] = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __magic_name__( self :Any ) -> int:
def _config_zero_init(lowerCAmelCase__ :Dict ):
__SCREAMING_SNAKE_CASE : Any = copy.deepcopy(__UpperCAmelCase )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(__UpperCAmelCase , __UpperCAmelCase , 1E-1_0 )
if isinstance(getattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) , __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = _config_zero_init(getattr(__UpperCAmelCase , __UpperCAmelCase ) )
setattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return configs_no_init
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE : int = _config_zero_init(__UpperCAmelCase )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Any = model_class(config=__UpperCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __magic_name__( self :Dict ) -> Union[str, Any]:
pass
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __magic_name__( self :List[str] ) -> List[Any]:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __magic_name__( self :int ) -> Dict:
__SCREAMING_SNAKE_CASE : List[Any] = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processor(images=__UpperCAmelCase , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**__UpperCAmelCase )
# verify the logits
__SCREAMING_SNAKE_CASE : str = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 9 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1 ) -> int:
if self.graph.get(__UpperCAmelCase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
_a = [[w, v]]
if not self.graph.get(__UpperCAmelCase ):
_a = []
def _UpperCAmelCase ( self ) -> int:
return list(self.graph )
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Optional[int]:
if s == d:
return []
_a = []
_a = []
if s == -2:
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase=-1 ) -> Tuple:
if c == -1:
_a = floor(random() * 10000 ) + 10
for i in range(__UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
_a = floor(random() * c ) + 1
if n != i:
self.add_pair(__UpperCAmelCase , __UpperCAmelCase , 1 )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> List[str]:
_a = deque()
_a = []
if s == -2:
_a = list(self.graph )[0]
d.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
while d:
_a = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Tuple:
_a = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Dict:
return len(self.graph[u] )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> Tuple:
_a = []
_a = []
if s == -2:
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = s
_a = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return sorted_nodes
def _UpperCAmelCase ( self ) -> Optional[int]:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return list(__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Any:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return False
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Optional[int]:
_a = time()
self.dfs(__UpperCAmelCase , __UpperCAmelCase )
_a = time()
return end - begin
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> Optional[Any]:
_a = time()
self.bfs(__UpperCAmelCase )
_a = time()
return end - begin
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Optional[int]:
_a = {}
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1 ) -> Dict:
# check if the u exists
if self.graph.get(__UpperCAmelCase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
_a = [[w, v]]
# add the other way
if self.graph.get(__UpperCAmelCase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
_a = [[w, u]]
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(__UpperCAmelCase )
# the other way round
if self.graph.get(__UpperCAmelCase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Dict:
if s == d:
return []
_a = []
_a = []
if s == -2:
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(__UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase=-1 ) -> Tuple:
if c == -1:
_a = floor(random() * 10000 ) + 10
for i in range(__UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
_a = floor(random() * c ) + 1
if n != i:
self.add_pair(__UpperCAmelCase , __UpperCAmelCase , 1 )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> List[Any]:
_a = deque()
_a = []
if s == -2:
_a = list(self.graph )[0]
d.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
while d:
_a = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Dict:
return len(self.graph[u] )
def _UpperCAmelCase ( self ) -> int:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return list(__UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Optional[Any]:
_a = []
_a = []
_a = list(self.graph )[0]
stack.append(__UpperCAmelCase )
visited.append(__UpperCAmelCase )
_a = -2
_a = []
_a = s
_a = False
_a = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_a = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_a = len(__UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_a = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_a = True
if len(__UpperCAmelCase ) != 0:
_a = stack[len(__UpperCAmelCase ) - 1]
else:
_a = False
indirect_parents.append(__UpperCAmelCase )
_a = s
_a = ss
# check if se have reached the starting point
if len(__UpperCAmelCase ) == 0:
return False
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return list(self.graph )
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 , __UpperCAmelCase=-1 ) -> Tuple:
_a = time()
self.dfs(__UpperCAmelCase , __UpperCAmelCase )
_a = time()
return end - begin
def _UpperCAmelCase ( self , __UpperCAmelCase=-2 ) -> Tuple:
_a = time()
self.bfs(__UpperCAmelCase )
_a = time()
return end - begin | 320 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=None , ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = 13
UpperCamelCase = 7
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = 99
UpperCamelCase = 384
UpperCamelCase = 2
UpperCamelCase = 4
UpperCamelCase = 37
UpperCamelCase = """gelu"""
UpperCamelCase = 0.1
UpperCamelCase = 0.1
UpperCamelCase = 512
UpperCamelCase = 16
UpperCamelCase = 2
UpperCamelCase = 0.0_2
UpperCamelCase = 3
UpperCamelCase = 4
UpperCamelCase = 128
UpperCamelCase = 2
UpperCamelCase = 9
UpperCamelCase = 1
UpperCamelCase = None
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = ConvBertConfig(
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 , initializer_range=self.initializer_range , return_dict=_SCREAMING_SNAKE_CASE , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = TFConvBertModel(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCamelCase = [input_ids, input_mask]
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = TFConvBertForMaskedLM(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFConvBertForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = self.num_choices
UpperCamelCase = TFConvBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFConvBertForTokenClassification(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = TFConvBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
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 ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase = (
{
"""feature-extraction""": TFConvBertModel,
"""fill-mask""": TFConvBertForMaskedLM,
"""question-answering""": TFConvBertForQuestionAnswering,
"""text-classification""": TFConvBertForSequenceClassification,
"""token-classification""": TFConvBertForTokenClassification,
"""zero-shot""": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = TFConvBertModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def A__ ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = True
UpperCamelCase = True
if hasattr(_SCREAMING_SNAKE_CASE , """use_cache""" ):
UpperCamelCase = True
UpperCamelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
UpperCamelCase = getattr(self.model_tester , """key_length""" , _SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
UpperCamelCase = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(model(_SCREAMING_SNAKE_CASE ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE , saved_model=_SCREAMING_SNAKE_CASE )
UpperCamelCase = os.path.join(_SCREAMING_SNAKE_CASE , """saved_model""" , """1""" )
UpperCamelCase = tf.keras.models.load_model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
UpperCamelCase = outputs["""encoder_hidden_states"""]
UpperCamelCase = outputs["""encoder_attentions"""]
else:
UpperCamelCase = outputs["""hidden_states"""]
UpperCamelCase = outputs["""attentions"""]
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = True
UpperCamelCase = getattr(self.model_tester , """decoder_seq_length""" , self.model_tester.seq_length )
UpperCamelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
UpperCamelCase = getattr(self.model_tester , """key_length""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = getattr(self.model_tester , """key_length""" , _SCREAMING_SNAKE_CASE )
def check_decoder_attentions_output(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(out_len % 2 , 0 )
UpperCamelCase = outputs.decoder_attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
UpperCamelCase = True
UpperCamelCase = False
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_decoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
UpperCamelCase = True
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_SCREAMING_SNAKE_CASE ) )
self.assertEqual(model.config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
@require_tf
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
UpperCamelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
UpperCamelCase = [1, 6, 768]
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = tf.constant(
[
[
[-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
[0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
[0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 )
| 321 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[Any]:
UpperCamelCase = ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")
UpperCamelCase = (
("""layer.""", """layer_"""),
("""word_embeddings.weight""", """word_embeddings"""),
("""position_embeddings.weight""", """position_embeddings"""),
("""token_type_embeddings.weight""", """token_type_embeddings"""),
(""".""", """/"""),
("""LayerNorm/weight""", """LayerNorm/gamma"""),
("""LayerNorm/bias""", """LayerNorm/beta"""),
("""weight""", """kernel"""),
)
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
UpperCamelCase = model.state_dict()
def to_tf_var_name(__UpperCamelCase ):
for patt, repl in iter(__UpperCamelCase ):
UpperCamelCase = name.replace(__UpperCamelCase , __UpperCamelCase )
return F"bert/{name}"
def create_tf_var(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
UpperCamelCase = tf.dtypes.as_dtype(tensor.dtype )
UpperCamelCase = tf.get_variable(dtype=__UpperCamelCase , shape=tensor.shape , name=__UpperCamelCase , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__UpperCamelCase )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
UpperCamelCase = to_tf_var_name(__UpperCamelCase )
UpperCamelCase = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
UpperCamelCase = torch_tensor.T
UpperCamelCase = create_tf_var(tensor=__UpperCamelCase , name=__UpperCamelCase , session=__UpperCamelCase )
tf.keras.backend.set_value(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = session.run(__UpperCamelCase )
print(F"Successfully created {tf_name}: {np.allclose(__UpperCamelCase , __UpperCamelCase )}" )
UpperCamelCase = tf.train.Saver(tf.trainable_variables() )
saver.save(__UpperCamelCase , os.path.join(__UpperCamelCase , model_name.replace("""-""" , """_""" ) + """.ckpt""" ) )
def lowercase__ ( __UpperCamelCase=None )-> Tuple:
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument("""--model_name""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""model name e.g. bert-base-uncased""" )
parser.add_argument(
"""--cache_dir""" , type=__UpperCamelCase , default=__UpperCamelCase , required=__UpperCamelCase , help="""Directory containing pytorch model""" )
parser.add_argument("""--pytorch_model_path""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""/path/to/<pytorch-model-name>.bin""" )
parser.add_argument("""--tf_cache_dir""" , type=__UpperCamelCase , required=__UpperCamelCase , help="""Directory in which to save tensorflow model""" )
UpperCamelCase = parser.parse_args(__UpperCamelCase )
UpperCamelCase = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=__UpperCamelCase , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import os
def lowercase__ ( __UpperCamelCase = "matrix.txt" )-> int:
with open(os.path.join(os.path.dirname(__UpperCamelCase ) , __UpperCamelCase ) ) as in_file:
UpperCamelCase = in_file.read()
UpperCamelCase = [[int(__UpperCamelCase ) for cell in row.split(""",""" )] for row in data.strip().splitlines()]
UpperCamelCase = [[0 for cell in row] for row in grid]
UpperCamelCase = len(grid[0] )
UpperCamelCase = [[0 for i in range(__UpperCamelCase )] for j in range(__UpperCamelCase )]
UpperCamelCase = grid[0][0]
for i in range(1 , __UpperCamelCase ):
UpperCamelCase = grid[0][i] + dp[0][i - 1]
for i in range(1 , __UpperCamelCase ):
UpperCamelCase = grid[i][0] + dp[i - 1][0]
for i in range(1 , __UpperCamelCase ):
for j in range(1 , __UpperCamelCase ):
UpperCamelCase = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(f'{solution() = }')
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , )-> np.ndarray:
UpperCamelCase = np.shape(__UpperCamelCase )
UpperCamelCase = np.shape(__UpperCamelCase )
UpperCamelCase = np.shape(__UpperCamelCase )
if shape_a[0] != shape_b[0]:
UpperCamelCase = (
"""Expected the same number of rows for A and B. """
F"Instead found A of size {shape_a} and B of size {shape_b}"
)
raise ValueError(__UpperCamelCase )
if shape_b[1] != shape_c[1]:
UpperCamelCase = (
"""Expected the same number of columns for B and C. """
F"Instead found B of size {shape_b} and C of size {shape_c}"
)
raise ValueError(__UpperCamelCase )
UpperCamelCase = pseudo_inv
if a_inv is None:
try:
UpperCamelCase = np.linalg.inv(__UpperCamelCase )
except np.linalg.LinAlgError:
raise ValueError(
"""Input matrix A is not invertible. Cannot compute Schur complement.""" )
return mat_c - mat_b.T @ a_inv @ mat_b
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase = np.array([[2, 1], [6, 3]] )
UpperCamelCase = schur_complement(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = np.block([[a, b], [b.T, c]] )
UpperCamelCase = np.linalg.det(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.linalg.det(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.linalg.det(_SCREAMING_SNAKE_CASE )
self.assertAlmostEqual(_SCREAMING_SNAKE_CASE , det_a * det_s )
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase = np.array([[2, 1], [6, 3]] )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
schur_complement(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
UpperCamelCase = np.array([[0, 3], [3, 0], [2, 3]] )
UpperCamelCase = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
schur_complement(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 321 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 1 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class a_ ( lowerCamelCase ):
def __init__( self , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""bs4"""] )
super().__init__(**_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
UpperCamelCase = parent.find_all(child.name , recursive=_SCREAMING_SNAKE_CASE )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_SCREAMING_SNAKE_CASE ) else next(i for i, s in enumerate(_SCREAMING_SNAKE_CASE , 1 ) if s is child ) )
UpperCamelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = BeautifulSoup(_SCREAMING_SNAKE_CASE , """html.parser""" )
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = []
for element in html_code.descendants:
if type(_SCREAMING_SNAKE_CASE ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCamelCase = html.unescape(_SCREAMING_SNAKE_CASE ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.xpath_soup(_SCREAMING_SNAKE_CASE )
stringaxtag_seq.append(_SCREAMING_SNAKE_CASE )
stringaxsubs_seq.append(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = """"""
for tagname, subs in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
xpath += F"/{tagname}"
if subs != 0:
xpath += F"[{subs}]"
return xpath
def __call__( self , _SCREAMING_SNAKE_CASE ) -> BatchFeature:
"""simple docstring"""
UpperCamelCase = False
# Check that strings has a valid type
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = True
elif isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
if len(_SCREAMING_SNAKE_CASE ) == 0 or isinstance(html_strings[0] , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F"but is of type {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = bool(isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(html_strings[0] , _SCREAMING_SNAKE_CASE )) )
if not is_batched:
UpperCamelCase = [html_strings]
# Get nodes + xpaths
UpperCamelCase = []
UpperCamelCase = []
for html_string in html_strings:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.get_three_from_single(_SCREAMING_SNAKE_CASE )
nodes.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase = []
for node, tag_list, sub_list in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = self.construct_xpath(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
xpath_strings.append(_SCREAMING_SNAKE_CASE )
xpaths.append(_SCREAMING_SNAKE_CASE )
# return as Dict
UpperCamelCase = {"""nodes""": nodes, """xpaths""": xpaths}
UpperCamelCase = BatchFeature(data=_SCREAMING_SNAKE_CASE , tensor_type=_SCREAMING_SNAKE_CASE )
return encoded_inputs
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class a_ ( unittest.TestCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=30 , _SCREAMING_SNAKE_CASE=400 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1 / 255 , _SCREAMING_SNAKE_CASE=True , ) -> List[str]:
"""simple docstring"""
UpperCamelCase = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = do_normalize
UpperCamelCase = image_mean
UpperCamelCase = image_std
UpperCamelCase = do_rescale
UpperCamelCase = rescale_factor
UpperCamelCase = do_pad
def A__ ( self ) -> Dict:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Union[str, Any]:
"""simple docstring"""
if not batched:
UpperCamelCase = image_inputs[0]
if isinstance(_SCREAMING_SNAKE_CASE , Image.Image ):
UpperCamelCase ,UpperCamelCase = image.size
else:
UpperCamelCase ,UpperCamelCase = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase = int(self.size["""shortest_edge"""] * h / w )
UpperCamelCase = self.size["""shortest_edge"""]
elif w > h:
UpperCamelCase = self.size["""shortest_edge"""]
UpperCamelCase = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCamelCase = self.size["""shortest_edge"""]
UpperCamelCase = self.size["""shortest_edge"""]
else:
UpperCamelCase = []
for image in image_inputs:
UpperCamelCase ,UpperCamelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase = max(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : item[0] )[0]
UpperCamelCase = max(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a_ ( lowerCamelCase , unittest.TestCase ):
lowercase = ConditionalDetrImageProcessor if is_vision_available() else None
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = ConditionalDetrImageProcessingTester(self )
@property
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """image_mean""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """image_std""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """do_normalize""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """do_resize""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """size""" ) )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
pass
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase ,UpperCamelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase ,UpperCamelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
UpperCamelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , numpify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase ,UpperCamelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
UpperCamelCase ,UpperCamelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=_SCREAMING_SNAKE_CASE , torchify=_SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(_SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
UpperCamelCase ,UpperCamelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).pixel_values
UpperCamelCase ,UpperCamelCase = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE , batched=_SCREAMING_SNAKE_CASE )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {"""image_id""": 39769, """annotations""": target}
# encode them
UpperCamelCase = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""" )
UpperCamelCase = image_processing(images=_SCREAMING_SNAKE_CASE , annotations=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _SCREAMING_SNAKE_CASE ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _SCREAMING_SNAKE_CASE ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _SCREAMING_SNAKE_CASE ) )
# verify class_labels
UpperCamelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _SCREAMING_SNAKE_CASE ) )
# verify orig_size
UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _SCREAMING_SNAKE_CASE ) )
# verify size
UpperCamelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _SCREAMING_SNAKE_CASE ) )
@slow
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}
UpperCamelCase = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
UpperCamelCase = ConditionalDetrImageProcessor(format="""coco_panoptic""" )
UpperCamelCase = image_processing(images=_SCREAMING_SNAKE_CASE , annotations=_SCREAMING_SNAKE_CASE , masks_path=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _SCREAMING_SNAKE_CASE ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _SCREAMING_SNAKE_CASE ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _SCREAMING_SNAKE_CASE ) )
# verify class_labels
UpperCamelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _SCREAMING_SNAKE_CASE ) )
# verify masks
UpperCamelCase = 822873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , _SCREAMING_SNAKE_CASE )
# verify orig_size
UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _SCREAMING_SNAKE_CASE ) )
# verify size
UpperCamelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _SCREAMING_SNAKE_CASE ) )
| 321 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class a_ ( lowerCamelCase ):
lowercase = ["""image_processor""", """feature_extractor"""]
lowercase = """TvltImageProcessor"""
lowercase = """TvltFeatureExtractor"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
super().__init__(image_processor=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = image_processor
UpperCamelCase = feature_extractor
def __call__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
"""simple docstring"""
if images is None and audio is None:
raise ValueError("""You need to specify either an `images` or `audio` input to process.""" )
UpperCamelCase = None
if images is not None:
UpperCamelCase = self.image_processor(_SCREAMING_SNAKE_CASE , mask_pixel=_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if images_mixed is not None:
UpperCamelCase = self.image_processor(_SCREAMING_SNAKE_CASE , is_mixed=_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if audio is not None:
UpperCamelCase = self.feature_extractor(
_SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , mask_audio=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = {}
if audio is not None:
output_dict.update(_SCREAMING_SNAKE_CASE )
if images is not None:
output_dict.update(_SCREAMING_SNAKE_CASE )
if images_mixed_dict is not None:
output_dict.update(_SCREAMING_SNAKE_CASE )
return output_dict
@property
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = self.image_processor.model_input_names
UpperCamelCase = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 1 |
'''simple docstring'''
import random
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> tuple:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = [], [], []
for element in data:
if element < pivot:
less.append(__UpperCamelCase )
elif element > pivot:
greater.append(__UpperCamelCase )
else:
equal.append(__UpperCamelCase )
return less, equal, greater
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(__UpperCamelCase ) or index < 0:
return None
UpperCamelCase = items[random.randint(0 , len(__UpperCamelCase ) - 1 )]
UpperCamelCase = 0
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = _partition(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = len(__UpperCamelCase )
UpperCamelCase = len(__UpperCamelCase )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(__UpperCamelCase , __UpperCamelCase )
# must be in larger
else:
return quick_select(__UpperCamelCase , index - (m + count) )
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
),
}
class a_ ( lowerCamelCase ):
lowercase = """van"""
def __init__( self , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , _SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , _SCREAMING_SNAKE_CASE=[64, 128, 320, 512] , _SCREAMING_SNAKE_CASE=[3, 3, 12, 3] , _SCREAMING_SNAKE_CASE=[8, 8, 4, 4] , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-6 , _SCREAMING_SNAKE_CASE=1e-2 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , **_SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = image_size
UpperCamelCase = num_channels
UpperCamelCase = patch_sizes
UpperCamelCase = strides
UpperCamelCase = hidden_sizes
UpperCamelCase = depths
UpperCamelCase = mlp_ratios
UpperCamelCase = hidden_act
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = layer_scale_init_value
UpperCamelCase = drop_path_rate
UpperCamelCase = dropout_rate
| 321 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=0.0 , metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Whether to SortishSamler or not."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """whether to use adafactor"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Dropout probability. Goes into model.config."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Attention dropout probability. Goes into model.config."""} )
lowercase = field(
default="""linear""" , metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''} , )
| 321 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class a_ ( lowerCamelCase ):
lowercase = ["""input_values""", """padding_mask"""]
def __init__( self , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 24000 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
super().__init__(feature_size=_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , padding_value=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = chunk_length_s
UpperCamelCase = overlap
@property
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , ) -> BatchFeature:
"""simple docstring"""
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 padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
UpperCamelCase = True
UpperCamelCase = bool(
isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
UpperCamelCase = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
UpperCamelCase = np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
UpperCamelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
UpperCamelCase = [np.asarray(_SCREAMING_SNAKE_CASE ).T]
# verify inputs are valid
for idx, example in enumerate(_SCREAMING_SNAKE_CASE ):
if example.ndim > 2:
raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}" )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels" )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels" )
UpperCamelCase = None
UpperCamelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
UpperCamelCase = min(array.shape[0] for array in raw_audio )
UpperCamelCase = int(np.floor(max_length / self.chunk_stride ) )
UpperCamelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
UpperCamelCase = max(array.shape[0] for array in raw_audio )
UpperCamelCase = int(np.ceil(max_length / self.chunk_stride ) )
UpperCamelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
UpperCamelCase = """max_length"""
else:
UpperCamelCase = input_values
# normal padding on batch
if padded_inputs is None:
UpperCamelCase = self.pad(
_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
if padding:
UpperCamelCase = padded_inputs.pop("""attention_mask""" )
UpperCamelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
UpperCamelCase = example[..., None]
input_values.append(example.T )
UpperCamelCase = input_values
if return_tensors is not None:
UpperCamelCase = padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE )
return padded_inputs
| 321 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, 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 EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=33 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=None , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A__ ( self ) -> Any:
"""simple docstring"""
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , 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 , initializer_range=self.initializer_range , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = EsmModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase = EsmForMaskedLM(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = EsmForTokenClassification(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = False
lowercase = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase = ()
lowercase = (
{
"""feature-extraction""": EsmModel,
"""fill-mask""": EsmForMaskedLM,
"""text-classification""": EsmForSequenceClassification,
"""token-classification""": EsmForTokenClassification,
"""zero-shot""": EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = True
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = EsmModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase = type
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Tuple:
"""simple docstring"""
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = EsmModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()[0]
UpperCamelCase = EsmEmbeddings(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
UpperCamelCase = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
UpperCamelCase = create_position_ids_from_input_ids(_SCREAMING_SNAKE_CASE , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()[0]
UpperCamelCase = EsmEmbeddings(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.empty(2 , 4 , 30 )
UpperCamelCase = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
UpperCamelCase = torch.as_tensor([expected_single_positions, expected_single_positions] )
UpperCamelCase = embeddings.create_position_ids_from_inputs_embeds(_SCREAMING_SNAKE_CASE )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) )
@unittest.skip("""Esm does not support embedding resizing""" )
def A__ ( self ) -> int:
"""simple docstring"""
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def A__ ( self ) -> Dict:
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def A__ ( self ) -> Any:
"""simple docstring"""
pass
@require_torch
class a_ ( lowerCamelCase ):
@slow
def A__ ( self ) -> Dict:
"""simple docstring"""
with torch.no_grad():
UpperCamelCase = EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
UpperCamelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
UpperCamelCase = 33
UpperCamelCase = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor(
[[[8.9_2_1_5, -1_0.5_8_9_8, -6.4_6_7_1], [-6.3_9_6_7, -1_3.9_1_1_4, -1.1_2_1_2], [-7.7_8_1_2, -1_3.9_5_1_6, -3.7_4_0_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def A__ ( self ) -> str:
"""simple docstring"""
with torch.no_grad():
UpperCamelCase = EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
UpperCamelCase = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
# compare the actual values for a slice.
UpperCamelCase = torch.tensor(
[[[0.1_4_4_4, 0.5_4_1_3, 0.3_2_4_8], [0.3_0_3_4, 0.0_0_5_3, 0.3_1_0_8], [0.3_2_2_8, -0.2_4_9_9, 0.3_4_1_5]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 321 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 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 a_ ( unittest.TestCase ):
def A__ ( self ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=_SCREAMING_SNAKE_CASE , dtype=jnp.bfloataa )
UpperCamelCase ,UpperCamelCase = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE , dtype=jnp.bfloataa )
UpperCamelCase = controlnet_params
UpperCamelCase = """bird"""
UpperCamelCase = jax.device_count()
UpperCamelCase = pipe.prepare_text_inputs([prompts] * num_samples )
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
UpperCamelCase = pipe.prepare_image_inputs([canny_image] * num_samples )
UpperCamelCase = jax.random.PRNGKey(0 )
UpperCamelCase = jax.random.split(_SCREAMING_SNAKE_CASE , jax.device_count() )
UpperCamelCase = replicate(_SCREAMING_SNAKE_CASE )
UpperCamelCase = shard(_SCREAMING_SNAKE_CASE )
UpperCamelCase = shard(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pipe(
prompt_ids=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , prng_seed=_SCREAMING_SNAKE_CASE , num_inference_steps=50 , jit=_SCREAMING_SNAKE_CASE , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
UpperCamelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
UpperCamelCase = images[0, 253:256, 253:256, -1]
UpperCamelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase = jnp.array(
[0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8] )
print(F"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=_SCREAMING_SNAKE_CASE , dtype=jnp.bfloataa )
UpperCamelCase ,UpperCamelCase = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE , dtype=jnp.bfloataa )
UpperCamelCase = controlnet_params
UpperCamelCase = """Chef in the kitchen"""
UpperCamelCase = jax.device_count()
UpperCamelCase = pipe.prepare_text_inputs([prompts] * num_samples )
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
UpperCamelCase = pipe.prepare_image_inputs([pose_image] * num_samples )
UpperCamelCase = jax.random.PRNGKey(0 )
UpperCamelCase = jax.random.split(_SCREAMING_SNAKE_CASE , jax.device_count() )
UpperCamelCase = replicate(_SCREAMING_SNAKE_CASE )
UpperCamelCase = shard(_SCREAMING_SNAKE_CASE )
UpperCamelCase = shard(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pipe(
prompt_ids=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , params=_SCREAMING_SNAKE_CASE , prng_seed=_SCREAMING_SNAKE_CASE , num_inference_steps=50 , jit=_SCREAMING_SNAKE_CASE , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
UpperCamelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
UpperCamelCase = images[0, 253:256, 253:256, -1]
UpperCamelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase = jnp.array(
[[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]] )
print(F"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class a_ ( lowerCamelCase ):
lowercase = """falcon"""
lowercase = ["""past_key_values"""]
def __init__( self , _SCREAMING_SNAKE_CASE=65024 , _SCREAMING_SNAKE_CASE=4544 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=71 , _SCREAMING_SNAKE_CASE=1e-5 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=11 , _SCREAMING_SNAKE_CASE=11 , **_SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = vocab_size
# Backward compatibility with n_embed kwarg
UpperCamelCase = kwargs.pop("""n_embed""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = hidden_size if n_embed is None else n_embed
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = layer_norm_epsilon
UpperCamelCase = initializer_range
UpperCamelCase = use_cache
UpperCamelCase = hidden_dropout
UpperCamelCase = attention_dropout
UpperCamelCase = bos_token_id
UpperCamelCase = eos_token_id
UpperCamelCase = num_attention_heads if num_kv_heads is None else num_kv_heads
UpperCamelCase = alibi
UpperCamelCase = new_decoder_architecture
UpperCamelCase = multi_query # Ignored when new_decoder_architecture is True
UpperCamelCase = parallel_attn
UpperCamelCase = bias
super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> Dict:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def A__ ( self ) -> Dict:
"""simple docstring"""
return not self.alibi
| 321 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import math
class a_ :
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = 0.0
UpperCamelCase = 0.0
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> list[list[int | float]]:
"""simple docstring"""
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def lowercase__ ( )-> None:
# Training Examples ( m, n )
UpperCamelCase = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
UpperCamelCase = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
UpperCamelCase = SelfOrganizingMap()
UpperCamelCase = 3
UpperCamelCase = 0.5
for _ in range(__UpperCamelCase ):
for j in range(len(__UpperCamelCase ) ):
# training sample
UpperCamelCase = training_samples[j]
# Compute the winning vector
UpperCamelCase = self_organizing_map.get_winner(__UpperCamelCase , __UpperCamelCase )
# Update the winning vector
UpperCamelCase = self_organizing_map.update(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# classify test sample
UpperCamelCase = [0, 0, 0, 1]
UpperCamelCase = self_organizing_map.get_winner(__UpperCamelCase , __UpperCamelCase )
# results
print(F"Clusters that the test sample belongs to : {winner}" )
print(F"Weights that have been trained : {weights}" )
# running the main() function
if __name__ == "__main__":
main()
| 321 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
SCREAMING_SNAKE_CASE__ = {
'facebook/bart-base': 1_0_2_4,
'facebook/bart-large': 1_0_2_4,
'facebook/bart-large-mnli': 1_0_2_4,
'facebook/bart-large-cnn': 1_0_2_4,
'facebook/bart-large-xsum': 1_0_2_4,
'yjernite/bart_eli5': 1_0_2_4,
}
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ["""input_ids""", """attention_mask"""]
lowercase = BartTokenizer
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="replace" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
super().__init__(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , errors=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , trim_offsets=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , _SCREAMING_SNAKE_CASE ) != add_prefix_space:
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , pre_tok_state.pop("""type""" ) )
UpperCamelCase = add_prefix_space
UpperCamelCase = pre_tok_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCamelCase = """post_processor"""
UpperCamelCase = getattr(self.backend_tokenizer , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if tokenizer_component_instance:
UpperCamelCase = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
UpperCamelCase = tuple(state["""sep"""] )
if "cls" in state:
UpperCamelCase = tuple(state["""cls"""] )
UpperCamelCase = False
if state.get("""add_prefix_space""" , _SCREAMING_SNAKE_CASE ) != add_prefix_space:
UpperCamelCase = add_prefix_space
UpperCamelCase = True
if state.get("""trim_offsets""" , _SCREAMING_SNAKE_CASE ) != trim_offsets:
UpperCamelCase = trim_offsets
UpperCamelCase = True
if changes_to_apply:
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , state.pop("""type""" ) )
UpperCamelCase = component_class(**_SCREAMING_SNAKE_CASE )
setattr(self.backend_tokenizer , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else value
UpperCamelCase = value
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> BatchEncoding:
"""simple docstring"""
UpperCamelCase = kwargs.get("""is_split_into_words""" , _SCREAMING_SNAKE_CASE )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> BatchEncoding:
"""simple docstring"""
UpperCamelCase = kwargs.get("""is_split_into_words""" , _SCREAMING_SNAKE_CASE )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
UpperCamelCase = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> str:
"""simple docstring"""
UpperCamelCase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [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]
| 321 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class a_ ( lowerCamelCase ):
lowercase = """luke"""
def __init__( self , _SCREAMING_SNAKE_CASE=50267 , _SCREAMING_SNAKE_CASE=500000 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=256 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-12 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , **_SCREAMING_SNAKE_CASE , ) -> Any:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = vocab_size
UpperCamelCase = entity_vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = entity_emb_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = use_entity_aware_attention
UpperCamelCase = classifier_dropout
| 321 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowercase__ ( __UpperCamelCase )-> int:
# 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 >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3400 and cp <= 0X4DBF) #
or (cp >= 0X2_0000 and cp <= 0X2_A6DF) #
or (cp >= 0X2_A700 and cp <= 0X2_B73F) #
or (cp >= 0X2_B740 and cp <= 0X2_B81F) #
or (cp >= 0X2_B820 and cp <= 0X2_CEAF) #
or (cp >= 0XF900 and cp <= 0XFAFF)
or (cp >= 0X2_F800 and cp <= 0X2_FA1F) #
): #
return True
return False
def lowercase__ ( __UpperCamelCase )-> int:
# word like '180' or '身高' or '神'
for char in word:
UpperCamelCase = ord(__UpperCamelCase )
if not _is_chinese_char(__UpperCamelCase ):
return 0
return 1
def lowercase__ ( __UpperCamelCase )-> str:
UpperCamelCase = set()
for token in tokens:
UpperCamelCase = len(__UpperCamelCase ) > 1 and is_chinese(__UpperCamelCase )
if chinese_word:
word_set.add(__UpperCamelCase )
UpperCamelCase = list(__UpperCamelCase )
return word_list
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Any:
if not chinese_word_set:
return bert_tokens
UpperCamelCase = max([len(__UpperCamelCase ) for w in chinese_word_set] )
UpperCamelCase = bert_tokens
UpperCamelCase ,UpperCamelCase = 0, len(__UpperCamelCase )
while start < end:
UpperCamelCase = True
if is_chinese(bert_word[start] ):
UpperCamelCase = min(end - start , __UpperCamelCase )
for i in range(__UpperCamelCase , 1 , -1 ):
UpperCamelCase = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
UpperCamelCase = """##""" + bert_word[j]
UpperCamelCase = start + i
UpperCamelCase = False
break
if single_word:
start += 1
return bert_word
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = []
for i in range(0 , len(__UpperCamelCase ) , 100 ):
UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 100] , tasks=["""cws"""] ).cws
UpperCamelCase = [get_chinese_word(__UpperCamelCase ) for r in res]
ltp_res.extend(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
UpperCamelCase = []
for i in range(0 , len(__UpperCamelCase ) , 100 ):
UpperCamelCase = bert_tokenizer(lines[i : i + 100] , add_special_tokens=__UpperCamelCase , truncation=__UpperCamelCase , max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
UpperCamelCase = []
for input_ids, chinese_word in zip(__UpperCamelCase , __UpperCamelCase ):
UpperCamelCase = []
for id in input_ids:
UpperCamelCase = bert_tokenizer._convert_id_to_token(__UpperCamelCase )
input_tokens.append(__UpperCamelCase )
UpperCamelCase = add_sub_symbol(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__UpperCamelCase ):
if token[:2] == "##":
UpperCamelCase = token[2:]
# save chinese tokens' pos
if len(__UpperCamelCase ) == 1 and _is_chinese_char(ord(__UpperCamelCase ) ):
ref_id.append(__UpperCamelCase )
ref_ids.append(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
return ref_ids
def lowercase__ ( __UpperCamelCase )-> Optional[int]:
# 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:
UpperCamelCase = f.readlines()
UpperCamelCase = [line.strip() for line in data if len(__UpperCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
UpperCamelCase = LTP(args.ltp ) # faster in GPU device
UpperCamelCase = BertTokenizer.from_pretrained(args.bert )
UpperCamelCase = prepare_ref(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
UpperCamelCase = [json.dumps(__UpperCamelCase ) + """\n""" for ref in ref_ids]
f.writelines(__UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = 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',
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
main(args)
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 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 a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = 13
UpperCamelCase = 7
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = 99
UpperCamelCase = 32
UpperCamelCase = 2
UpperCamelCase = 4
UpperCamelCase = 37
UpperCamelCase = """gelu"""
UpperCamelCase = 0.1
UpperCamelCase = 0.1
UpperCamelCase = 512
UpperCamelCase = 16
UpperCamelCase = 2
UpperCamelCase = 0.0_2
UpperCamelCase = 3
UpperCamelCase = 4
UpperCamelCase = None
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = 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 ) -> Optional[Any]:
"""simple docstring"""
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = self.prepare_config_and_inputs()
UpperCamelCase = True
UpperCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase = 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 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
UpperCamelCase = TFEsmModel(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = [input_ids, input_mask]
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = TFEsmModel(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""encoder_hidden_states""": encoder_hidden_states,
"""encoder_attention_mask""": encoder_attention_mask,
}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
UpperCamelCase = [input_ids, input_mask]
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , encoder_hidden_states=_SCREAMING_SNAKE_CASE )
# Also check the case where encoder outputs are not passed
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = TFEsmForMaskedLM(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFEsmForTokenClassification(config=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase = (
{
"""feature-extraction""": TFEsmModel,
"""fill-mask""": TFEsmForMaskedLM,
"""text-classification""": TFEsmForSequenceClassification,
"""token-classification""": TFEsmForTokenClassification,
"""zero-shot""": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase = False
lowercase = False
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = TFEsmModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = TFEsmModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@unittest.skip("""Protein models do not support embedding resizing.""" )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip("""Protein models do not support embedding resizing.""" )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
pass
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
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
UpperCamelCase = model.get_bias()
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for k, v in name.items():
assert isinstance(_SCREAMING_SNAKE_CASE , tf.Variable )
else:
UpperCamelCase = model.get_output_embeddings()
assert x is None
UpperCamelCase = model.get_bias()
assert name is None
@require_tf
class a_ ( unittest.TestCase ):
@slow
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
UpperCamelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
UpperCamelCase = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice.
UpperCamelCase = tf.constant(
[
[
[8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
[-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
[-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2 ) )
@slow
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
UpperCamelCase = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )[0]
# compare the actual values for a slice.
UpperCamelCase = tf.constant(
[
[
[0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
[0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
[0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 321 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 1 |
'''simple docstring'''
import datasets
SCREAMING_SNAKE_CASE__ = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and Stoyanov, Veselin",\n title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n in Natural Language Processing",\n year = "2018",\n publisher = "Association for Computational Linguistics",\n location = "Brussels, Belgium",\n}\n'
SCREAMING_SNAKE_CASE__ = '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'
SCREAMING_SNAKE_CASE__ = '\nComputes XNLI score which is just simple accuracy.\nArgs:\n predictions: Predicted labels.\n references: Ground truth labels.\nReturns:\n \'accuracy\': accuracy\nExamples:\n\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> xnli_metric = datasets.load_metric("xnli")\n >>> results = xnli_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n'
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> List[Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
"""references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
} ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}
| 321 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = 1.5
UpperCamelCase = int(factor * num_class_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 )
os.makedirs(F"{class_data_dir}/images" , exist_ok=__UpperCamelCase )
if len(list(Path(F"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCamelCase = client.query(text=__UpperCamelCase )
if len(__UpperCamelCase ) >= factor * num_class_images or num_images > 1E4:
break
else:
UpperCamelCase = int(factor * num_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 , )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(desc="""downloading real regularization images""" , total=__UpperCamelCase )
with open(F"{class_data_dir}/caption.txt" , """w""" ) as fa, open(F"{class_data_dir}/urls.txt" , """w""" ) as fa, open(
F"{class_data_dir}/images.txt" , """w""" ) as fa:
while total < num_class_images:
UpperCamelCase = class_images[count]
count += 1
try:
UpperCamelCase = requests.get(images["""url"""] )
if img.status_code == 200:
UpperCamelCase = Image.open(BytesIO(img.content ) )
with open(F"{class_data_dir}/images/{total}.jpg" , """wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(F"{class_data_dir}/images/{total}.jpg" + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowercase__ ( )-> str:
UpperCamelCase = argparse.ArgumentParser("""""" , add_help=__UpperCamelCase )
parser.add_argument("""--class_prompt""" , help="""text prompt to retrieve images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--class_data_dir""" , help="""path to save images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--num_class_images""" , help="""number of images to download""" , default=200 , type=__UpperCamelCase )
return parser.parse_args()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 321 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, 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
if is_torch_available():
import torch
from torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=[10, 20, 30, 40] , _SCREAMING_SNAKE_CASE=[1, 1, 2, 1] , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = num_channels
UpperCamelCase = embeddings_size
UpperCamelCase = hidden_sizes
UpperCamelCase = depths
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_act
UpperCamelCase = num_labels
UpperCamelCase = scope
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def A__ ( self ) -> str:
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = RegNetModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = RegNetForImageClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = config_and_inputs
UpperCamelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
lowercase = (
{"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = RegNetModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A__ ( self ) -> str:
"""simple docstring"""
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
pass
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(config=_SCREAMING_SNAKE_CASE )
for name, module in model.named_modules():
if isinstance(_SCREAMING_SNAKE_CASE , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
def check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
UpperCamelCase = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCamelCase = self.model_tester.num_stages
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
UpperCamelCase ,UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCamelCase = layer_type
UpperCamelCase = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )
@slow
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def lowercase__ ( )-> Tuple:
UpperCamelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def A__ ( self ) -> int:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).to(_SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
UpperCamelCase = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
UpperCamelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] ).to(_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 321 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class a_ :
lowercase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
lowercase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = {}
if self.train_dir is not None:
UpperCamelCase = self.train_dir
if self.validation_dir is not None:
UpperCamelCase = self.validation_dir
UpperCamelCase = data_files if data_files else None
@dataclass
class a_ :
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowercase__ ( )-> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
UpperCamelCase = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase = split["""train"""]
UpperCamelCase = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase = ViTMAEForPreTraining(__UpperCamelCase )
if training_args.do_train:
UpperCamelCase = ds["""train"""].column_names
else:
UpperCamelCase = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase = """image"""
elif "img" in column_names:
UpperCamelCase = """img"""
else:
UpperCamelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__UpperCamelCase ):
UpperCamelCase = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Compute absolute learning rate
UpperCamelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
UpperCamelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase = last_checkpoint
UpperCamelCase = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCamelCase )
trainer.save_metrics("""eval""" , __UpperCamelCase )
# Write model card and (optionally) push to hub
UpperCamelCase = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
from PIL import Image
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Image:
def brightness(__UpperCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(__UpperCamelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
SCREAMING_SNAKE_CASE__ = change_brightness(img, 1_0_0)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 321 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = 0
while number > 0:
UpperCamelCase = number % 10
sum_of_digits += last_digit
UpperCamelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowercase__ ( __UpperCamelCase = 100 )-> int:
UpperCamelCase = factorial(__UpperCamelCase )
UpperCamelCase = split_and_add(__UpperCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Any:
# Initialise PyTorch model
UpperCamelCase = RemBertConfig.from_json_file(__UpperCamelCase )
print("""Building PyTorch model from configuration: {}""".format(str(__UpperCamelCase ) ) )
UpperCamelCase = RemBertModel(__UpperCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Save pytorch-model
print("""Save PyTorch model to {}""".format(__UpperCamelCase ) )
torch.save(model.state_dict() , __UpperCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--rembert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained RemBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=() , __UpperCamelCase=None , __UpperCamelCase="no" , __UpperCamelCase="29500" )-> List[Any]:
UpperCamelCase = False
UpperCamelCase = False
if any(key.startswith("""KAGGLE""" ) for key in os.environ.keys() ):
UpperCamelCase = True
elif "IPython" in sys.modules:
UpperCamelCase = """google.colab""" in str(sys.modules["""IPython"""].get_ipython() )
try:
UpperCamelCase = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
F"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}." )
if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME""" , __UpperCamelCase ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"""To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
"""your training function. Restart your notebook and make sure no cells initializes an """
"""`Accelerator`.""" )
if num_processes is None:
UpperCamelCase = 8
UpperCamelCase = PrepareForLaunch(__UpperCamelCase , distributed_type="""TPU""" )
print(F"Launching a training on {num_processes} TPU cores." )
xmp.spawn(__UpperCamelCase , args=__UpperCamelCase , nprocs=__UpperCamelCase , start_method="""fork""" )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("""Launching training on one GPU.""" )
else:
print("""Launching training on one CPU.""" )
function(*__UpperCamelCase )
else:
if num_processes is None:
raise ValueError(
"""You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.""" )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"""To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
"""inside your training function. Restart your notebook and make sure no cells initializes an """
"""`Accelerator`.""" )
if torch.cuda.is_initialized():
raise ValueError(
"""To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
"""using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
"""function.""" )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__UpperCamelCase , master_addr="""127.0.01""" , master_port=__UpperCamelCase , mixed_precision=__UpperCamelCase ):
UpperCamelCase = PrepareForLaunch(__UpperCamelCase , distributed_type="""MULTI_GPU""" )
print(F"Launching training on {num_processes} GPUs." )
try:
start_processes(__UpperCamelCase , args=__UpperCamelCase , nprocs=__UpperCamelCase , start_method="""fork""" )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"""CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
"""This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
"""Please review your imports and test them when running the `notebook_launcher()` to identify """
"""which one is problematic.""" ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
UpperCamelCase = """1"""
print("""Launching training on MPS.""" )
elif torch.cuda.is_available():
print("""Launching training on one GPU.""" )
else:
print("""Launching training on CPU.""" )
function(*__UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase=() , __UpperCamelCase=2 )-> List[str]:
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__UpperCamelCase , master_addr="""127.0.01""" , master_port="""29500""" , accelerate_mixed_precision="""no""" , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu="""yes""" , ):
UpperCamelCase = PrepareForLaunch(__UpperCamelCase , debug=__UpperCamelCase )
start_processes(__UpperCamelCase , args=__UpperCamelCase , nprocs=__UpperCamelCase , start_method="""fork""" )
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
UpperCamelCase = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(__UpperCamelCase ) )
return round(__UpperCamelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class a_ ( lowerCamelCase ):
lowercase = """instructblip_vision_model"""
def __init__( self , _SCREAMING_SNAKE_CASE=1408 , _SCREAMING_SNAKE_CASE=6144 , _SCREAMING_SNAKE_CASE=39 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=14 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=1e-6 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=1e-10 , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , ) -> Dict:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = hidden_size
UpperCamelCase = intermediate_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = patch_size
UpperCamelCase = image_size
UpperCamelCase = initializer_range
UpperCamelCase = attention_dropout
UpperCamelCase = layer_norm_eps
UpperCamelCase = hidden_act
UpperCamelCase = qkv_bias
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = cls.get_config_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
UpperCamelCase = 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(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
class a_ ( lowerCamelCase ):
lowercase = """instructblip_qformer"""
def __init__( self , _SCREAMING_SNAKE_CASE=30522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-12 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1408 , **_SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = position_embedding_type
UpperCamelCase = cross_attention_frequency
UpperCamelCase = encoder_hidden_size
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = cls.get_config_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
UpperCamelCase = config_dict["""qformer_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(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
class a_ ( lowerCamelCase ):
lowercase = """instructblip"""
lowercase = True
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=32 , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
if vision_config is None:
UpperCamelCase = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
UpperCamelCase = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
UpperCamelCase = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
UpperCamelCase = InstructBlipVisionConfig(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = InstructBlipQFormerConfig(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
UpperCamelCase = CONFIG_MAPPING[text_model_type](**_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.text_config.tie_word_embeddings
UpperCamelCase = self.text_config.is_encoder_decoder
UpperCamelCase = num_query_tokens
UpperCamelCase = self.vision_config.hidden_size
UpperCamelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCamelCase = 1.0
UpperCamelCase = 0.0_2
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.vision_config.to_dict()
UpperCamelCase = self.qformer_config.to_dict()
UpperCamelCase = self.text_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def lowercase__ ( __UpperCamelCase )-> str: # picklable for multiprocessing
return x.sum()
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]: # picklable for multiprocessing
return i + 1
@dataclass
class a_ :
lowercase = 42
lowercase = 42
class a_ ( lowerCamelCase ):
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = {}
UpperCamelCase = []
UpperCamelCase = 1
UpperCamelCase = [1, 2]
UpperCamelCase = {"""a""": 1, """b""": 2}
UpperCamelCase = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase = {}
UpperCamelCase = []
UpperCamelCase = 2
UpperCamelCase = [2, 3]
UpperCamelCase = {"""a""": 2, """b""": 3}
UpperCamelCase = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase = 2
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
UpperCamelCase = {"""a""": np.eye(2 ), """b""": np.zeros(3 ), """c""": np.ones(2 )}
UpperCamelCase = {"""a""": 2, """b""": 0, """c""": 2}
UpperCamelCase = {
"""a""": np.eye(2 ).astype(_SCREAMING_SNAKE_CASE ),
"""b""": np.zeros(3 ).astype(_SCREAMING_SNAKE_CASE ),
"""c""": np.ones(2 ).astype(_SCREAMING_SNAKE_CASE ),
}
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , map_numpy=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , map_numpy=_SCREAMING_SNAKE_CASE ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , map_numpy=_SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , map_numpy=_SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(_SCREAMING_SNAKE_CASE ): # can't pickle a local lambda
map_nested(lambda _SCREAMING_SNAKE_CASE : x + 1 , _SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = {"""a""": 1, """b""": 2}
UpperCamelCase = {"""a""": 3, """b""": 4}
UpperCamelCase = {"""a""": 5, """b""": 6}
UpperCamelCase = sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ) , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
class a_ :
lowercase = """bar"""
UpperCamelCase = Foo()
self.assertEqual(foo.my_attr , """bar""" )
with temporary_assignment(_SCREAMING_SNAKE_CASE , """my_attr""" , """BAR""" ):
self.assertEqual(foo.my_attr , """BAR""" )
self.assertEqual(foo.my_attr , """bar""" )
@pytest.mark.parametrize(
"""iterable_length, num_proc, expected_num_proc""" , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[Any]:
with patch("""datasets.utils.py_utils._single_map_nested""" ) as mock_single_map_nested, patch(
"""datasets.parallel.parallel.Pool""" ) as mock_multiprocessing_pool:
UpperCamelCase = {F"{i}": i for i in range(__UpperCamelCase )}
UpperCamelCase = map_nested(lambda __UpperCamelCase : x + 10 , __UpperCamelCase , num_proc=__UpperCamelCase , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class a_ ( lowerCamelCase ):
@require_tf
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
import tensorflow as tf
from tensorflow.keras import layers
UpperCamelCase = layers.Dense(2 )
def gen_random_output():
UpperCamelCase = tf.random.uniform((1, 3) )
return model(_SCREAMING_SNAKE_CASE ).numpy()
with temp_seed(42 , set_tensorflow=_SCREAMING_SNAKE_CASE ):
UpperCamelCase = gen_random_output()
with temp_seed(42 , set_tensorflow=_SCREAMING_SNAKE_CASE ):
UpperCamelCase = gen_random_output()
UpperCamelCase = gen_random_output()
np.testing.assert_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def A__ ( self ) -> List[Any]:
"""simple docstring"""
import torch
def gen_random_output():
UpperCamelCase = torch.nn.Linear(3 , 2 )
UpperCamelCase = torch.rand(1 , 3 )
return model(_SCREAMING_SNAKE_CASE ).detach().numpy()
with temp_seed(42 , set_pytorch=_SCREAMING_SNAKE_CASE ):
UpperCamelCase = gen_random_output()
with temp_seed(42 , set_pytorch=_SCREAMING_SNAKE_CASE ):
UpperCamelCase = gen_random_output()
UpperCamelCase = gen_random_output()
np.testing.assert_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def A__ ( self ) -> List[str]:
"""simple docstring"""
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
UpperCamelCase = gen_random_output()
with temp_seed(42 ):
UpperCamelCase = gen_random_output()
UpperCamelCase = gen_random_output()
np.testing.assert_equal(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("""input_data""" , [{}] )
def lowercase__ ( __UpperCamelCase )-> List[Any]:
UpperCamelCase = NestedDataStructure(__UpperCamelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
"""data, expected_output""" , [
({}, []),
([], []),
("""foo""", ["""foo"""]),
(["""foo""", """bar"""], ["""foo""", """bar"""]),
([["""foo""", """bar"""]], ["""foo""", """bar"""]),
([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
({"""a""": 1, """b""": 2}, [1, 2]),
({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
] , )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Any:
UpperCamelCase = NestedDataStructure(__UpperCamelCase ).flatten()
assert output == expected_output
def lowercase__ ( )-> Tuple:
UpperCamelCase = A(x=1 , y="""foobar""" )
UpperCamelCase = {"""x""": 1, """y""": """foobar"""}
assert asdict(__UpperCamelCase ) == expected_output
UpperCamelCase = {"""a""": {"""b""": A(x=10 , y="""foo""" )}, """c""": [A(x=20 , y="""bar""" )]}
UpperCamelCase = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
assert asdict(__UpperCamelCase ) == expected_output
with pytest.raises(__UpperCamelCase ):
asdict([1, A(x=10 , y="""foo""" )] )
def lowercase__ ( __UpperCamelCase )-> str:
return text.split()
def lowercase__ ( __UpperCamelCase )-> List[Any]:
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def lowercase__ ( )-> Tuple:
with Pool(2 ) as pool:
UpperCamelCase = list(iflatmap_unordered(__UpperCamelCase , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(__UpperCamelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
UpperCamelCase = list(iflatmap_unordered(__UpperCamelCase , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(__UpperCamelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
UpperCamelCase = []
for yield_time, content in iflatmap_unordered(
__UpperCamelCase , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__UpperCamelCase )
assert out.count("""a""" ) == 2
assert out.count("""b""" ) == 2
assert len(__UpperCamelCase ) == 4
| 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 1 |
'''simple docstring'''
from math import ceil
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Tuple:
UpperCamelCase = list(range(0 , __UpperCamelCase ) )
UpperCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCamelCase = []
for i in device_map_blocks:
if device_map_blocks.count(__UpperCamelCase ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__UpperCamelCase )
# Missing blocks
UpperCamelCase = [i for i in blocks if i not in device_map_blocks]
UpperCamelCase = [i for i in device_map_blocks if i not in blocks]
if len(__UpperCamelCase ) != 0:
raise ValueError(
"""Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
""" These attention blocks were specified more than once: """ + str(__UpperCamelCase ) )
if len(__UpperCamelCase ) != 0:
raise ValueError(
"""There are attention blocks for this model that are not specified in the device_map. Add these attention """
"""blocks to a device on the device_map: """ + str(__UpperCamelCase ) )
if len(__UpperCamelCase ) != 0:
raise ValueError(
"""The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
+ str(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> List[Any]:
UpperCamelCase = list(range(__UpperCamelCase ) )
UpperCamelCase = int(ceil(n_layers / len(__UpperCamelCase ) ) )
UpperCamelCase = [layers[i : i + n_blocks] for i in range(0 , __UpperCamelCase , __UpperCamelCase )]
return dict(zip(__UpperCamelCase , __UpperCamelCase ) )
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[str]:
if nth_term == "":
return [""]
UpperCamelCase = int(__UpperCamelCase )
UpperCamelCase = int(__UpperCamelCase )
UpperCamelCase = []
for temp in range(int(__UpperCamelCase ) ):
series.append(F"1 / {pow(temp + 1 , int(__UpperCamelCase ) )}" if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE__ = int(input('Enter the last number (nth term) of the P-Series'))
SCREAMING_SNAKE_CASE__ = int(input('Enter the power for P-Series'))
print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
print(p_series(nth_term, power))
| 321 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE__ = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
# Segments (not really needed)
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 3
SCREAMING_SNAKE_CASE__ = 4
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = """left"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<sep>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<cls>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE=["<eop>", "<eod>"] , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
UpperCamelCase = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_SCREAMING_SNAKE_CASE , remove_space=_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase = 3
UpperCamelCase = do_lower_case
UpperCamelCase = remove_space
UpperCamelCase = keep_accents
UpperCamelCase = vocab_file
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
return state
def __setstate__( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase = {}
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
if self.remove_space:
UpperCamelCase = """ """.join(inputs.strip().split() )
else:
UpperCamelCase = inputs
UpperCamelCase = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
UpperCamelCase = unicodedata.normalize("""NFKD""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = """""".join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE )] )
if self.do_lower_case:
UpperCamelCase = outputs.lower()
return outputs
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.preprocess_text(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
UpperCamelCase = []
for piece in pieces:
if len(_SCREAMING_SNAKE_CASE ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
UpperCamelCase = self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
UpperCamelCase = cur_pieces[1:]
else:
UpperCamelCase = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_SCREAMING_SNAKE_CASE )
else:
new_pieces.append(_SCREAMING_SNAKE_CASE )
return new_pieces
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = """""".join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE , """ """ ).strip()
return out_string
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , **_SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
UpperCamelCase = kwargs.pop("""use_source_tokenizer""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
UpperCamelCase = []
UpperCamelCase = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = []
sub_texts.append(_SCREAMING_SNAKE_CASE )
else:
current_sub_text.append(_SCREAMING_SNAKE_CASE )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
UpperCamelCase = """""".join(_SCREAMING_SNAKE_CASE )
UpperCamelCase = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
UpperCamelCase = self.clean_up_tokenization(_SCREAMING_SNAKE_CASE )
return clean_text
else:
return text
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is not None:
return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1]
return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
UpperCamelCase = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , """wb""" ) as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 321 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE__ = parse(importlib.metadata.version('torch'))
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Any:
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(F"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}" )
UpperCamelCase = STR_OPERATION_TO_FUNC[operation]
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCamelCase = parse(importlib.metadata.version(__UpperCamelCase ) )
return operation(__UpperCamelCase , parse(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Dict:
return compare_versions(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
| 321 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
SCREAMING_SNAKE_CASE__ = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class a_ ( unittest.TestCase ):
lowercase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowercase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
lowercase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
lowercase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
UpperCamelCase = ZeroShotClassificationPipeline(
model=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , candidate_labels=["""polics""", """health"""] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
UpperCamelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels="""politics""" )
self.assertEqual(_SCREAMING_SNAKE_CASE , {"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE )]} )
# No kwarg
UpperCamelCase = classifier("""Who are you voting for in 2020?""" , ["""politics"""] )
self.assertEqual(_SCREAMING_SNAKE_CASE , {"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE )]} )
UpperCamelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels=["""politics"""] )
self.assertEqual(_SCREAMING_SNAKE_CASE , {"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE )]} )
UpperCamelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels="""politics, public health""" )
self.assertEqual(
_SCREAMING_SNAKE_CASE , {"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""] ) ) , 1.0 )
UpperCamelCase = classifier("""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health"""] )
self.assertEqual(
_SCREAMING_SNAKE_CASE , {"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""] ) ) , 1.0 )
UpperCamelCase = classifier(
"""Who are you voting for in 2020?""" , candidate_labels="""politics""" , hypothesis_template="""This text is about {}""" )
self.assertEqual(_SCREAMING_SNAKE_CASE , {"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE )]} )
# https://github.com/huggingface/transformers/issues/13846
UpperCamelCase = classifier(["""I am happy"""] , ["""positive""", """negative"""] )
self.assertEqual(
_SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )]}
for i in range(1 )
] , )
UpperCamelCase = classifier(["""I am happy""", """I am sad"""] , ["""positive""", """negative"""] )
self.assertEqual(
_SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(_SCREAMING_SNAKE_CASE ), """labels""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )], """scores""": [ANY(_SCREAMING_SNAKE_CASE ), ANY(_SCREAMING_SNAKE_CASE )]}
for i in range(2 )
] , )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
classifier("""""" , candidate_labels="""politics""" )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
classifier(_SCREAMING_SNAKE_CASE , candidate_labels="""politics""" )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
classifier("""Who are you voting for in 2020?""" , candidate_labels="""""" )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
classifier("""Who are you voting for in 2020?""" , candidate_labels=_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
classifier(
"""Who are you voting for in 2020?""" , candidate_labels="""politics""" , hypothesis_template="""Not formatting template""" , )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
classifier(
"""Who are you voting for in 2020?""" , candidate_labels="""politics""" , hypothesis_template=_SCREAMING_SNAKE_CASE , )
self.run_entailment_id(_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = zero_shot_classifier.model.config
UpperCamelCase = config.labelaid
UpperCamelCase = zero_shot_classifier.entailment_id
UpperCamelCase = {"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
UpperCamelCase = {"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
UpperCamelCase = {"""ENTAIL""": 0, """NON-ENTAIL""": 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
UpperCamelCase = {"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
UpperCamelCase = original_labelaid
self.assertEqual(_SCREAMING_SNAKE_CASE , zero_shot_classifier.entailment_id )
@require_torch
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = pipeline(
"""zero-shot-classification""" , model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" , framework="""pt""" , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"""Who are you voting for in 2020?""" * 100 , candidate_labels=["""politics""", """public health""", """science"""] )
@require_torch
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = pipeline(
"""zero-shot-classification""" , model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" , framework="""pt""" , )
UpperCamelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""science""", """public health""", """politics"""],
"""scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@require_tf
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = pipeline(
"""zero-shot-classification""" , model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" , framework="""tf""" , )
UpperCamelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""science""", """public health""", """politics"""],
"""scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@slow
@require_torch
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = pipeline("""zero-shot-classification""" , model="""roberta-large-mnli""" , framework="""pt""" )
UpperCamelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""politics""", """public health""", """science"""],
"""scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
UpperCamelCase = zero_shot_classifier(
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
""" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
""" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
""" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
""" machine translation tasks show these models to be superior in quality while being more parallelizable"""
""" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
""" English-to-German translation task, improving over the existing best results, including ensembles by"""
""" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
""" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
""" fraction of the training costs of the best models from the literature. We show that the Transformer"""
""" generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
""" large and limited training data.""" , candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] , multi_label=_SCREAMING_SNAKE_CASE , )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ) , {
"""sequence""": (
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
""" networks in an encoder-decoder configuration. The best performing models also connect the"""
""" encoder and decoder through an attention mechanism. We propose a new simple network"""
""" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
""" and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
""" superior in quality while being more parallelizable and requiring significantly less time to"""
""" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
""" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
""" English-to-French translation task, our model establishes a new single-model state-of-the-art"""
""" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
""" costs of the best models from the literature. We show that the Transformer generalizes well to"""
""" other tasks by applying it successfully to English constituency parsing both with large and"""
""" limited training data."""
),
"""labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
"""scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
@slow
@require_tf
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = pipeline("""zero-shot-classification""" , model="""roberta-large-mnli""" , framework="""tf""" )
UpperCamelCase = zero_shot_classifier(
"""Who are you voting for in 2020?""" , candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ) , {
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""politics""", """public health""", """science"""],
"""scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
UpperCamelCase = zero_shot_classifier(
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
""" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
""" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
""" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
""" machine translation tasks show these models to be superior in quality while being more parallelizable"""
""" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
""" English-to-German translation task, improving over the existing best results, including ensembles by"""
""" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
""" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
""" fraction of the training costs of the best models from the literature. We show that the Transformer"""
""" generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
""" large and limited training data.""" , candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] , multi_label=_SCREAMING_SNAKE_CASE , )
self.assertEqual(
nested_simplify(_SCREAMING_SNAKE_CASE ) , {
"""sequence""": (
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
""" networks in an encoder-decoder configuration. The best performing models also connect the"""
""" encoder and decoder through an attention mechanism. We propose a new simple network"""
""" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
""" and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
""" superior in quality while being more parallelizable and requiring significantly less time to"""
""" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
""" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
""" English-to-French translation task, our model establishes a new single-model state-of-the-art"""
""" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
""" costs of the best models from the literature. We show that the Transformer generalizes well to"""
""" other tasks by applying it successfully to English constituency parsing both with large and"""
""" limited training data."""
),
"""labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
"""scores""": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'vocab.txt'}
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
SCREAMING_SNAKE_CASE__ = {
'YituTech/conv-bert-base': 5_1_2,
'YituTech/conv-bert-medium-small': 5_1_2,
'YituTech/conv-bert-small': 5_1_2,
}
SCREAMING_SNAKE_CASE__ = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_INIT_CONFIGURATION
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ConvBertTokenizer
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="[UNK]" , _SCREAMING_SNAKE_CASE="[SEP]" , _SCREAMING_SNAKE_CASE="[PAD]" , _SCREAMING_SNAKE_CASE="[CLS]" , _SCREAMING_SNAKE_CASE="[MASK]" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , tokenize_chinese_chars=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , _SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get("""strip_accents""" , _SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , _SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , normalizer_state.pop("""type""" ) )
UpperCamelCase = do_lower_case
UpperCamelCase = strip_accents
UpperCamelCase = tokenize_chinese_chars
UpperCamelCase = normalizer_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = do_lower_case
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
UpperCamelCase = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
# Construct model
if openai_config_file == "":
UpperCamelCase = OpenAIGPTConfig()
else:
UpperCamelCase = OpenAIGPTConfig.from_json_file(__UpperCamelCase )
UpperCamelCase = OpenAIGPTModel(__UpperCamelCase )
# Load weights from numpy
load_tf_weights_in_openai_gpt(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Save pytorch-model
UpperCamelCase = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
UpperCamelCase = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , __UpperCamelCase )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--openai_checkpoint_folder_path',
default=None,
type=str,
required=True,
help='Path to the TensorFlow checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--openai_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained OpenAI model. \n'
'This specifies the model architecture.'
),
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 321 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 1 |
'''simple docstring'''
import math
import random
def lowercase__ ( __UpperCamelCase , __UpperCamelCase = False )-> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
SCREAMING_SNAKE_CASE__ = 0.02
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
UpperCamelCase = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__UpperCamelCase ):
# Forward propagation
UpperCamelCase = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
UpperCamelCase = (expected / 100) - layer_a
# Error delta
UpperCamelCase = layer_1_error * sigmoid_function(__UpperCamelCase , __UpperCamelCase )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE__ = int(input('Expected value: '))
SCREAMING_SNAKE_CASE__ = int(input('Number of propagations: '))
print(forward_propagation(expected, number_propagations))
| 321 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class a_ ( lowerCamelCase ):
lowercase = """xglm"""
lowercase = ["""past_key_values"""]
lowercase = {
"""num_attention_heads""": """attention_heads""",
"""hidden_size""": """d_model""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , _SCREAMING_SNAKE_CASE=256008 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE=24 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , **_SCREAMING_SNAKE_CASE , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = vocab_size
UpperCamelCase = max_position_embeddings
UpperCamelCase = d_model
UpperCamelCase = ffn_dim
UpperCamelCase = num_layers
UpperCamelCase = attention_heads
UpperCamelCase = activation_function
UpperCamelCase = dropout
UpperCamelCase = attention_dropout
UpperCamelCase = activation_dropout
UpperCamelCase = layerdrop
UpperCamelCase = init_std
UpperCamelCase = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase = use_cache
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , decoder_start_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
| 321 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE__ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __UpperCamelCase )-> str:
if "://" in dataset_path:
UpperCamelCase = dataset_path.split("""://""" )[1]
return dataset_path
def lowercase__ ( __UpperCamelCase )-> bool:
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> int:
UpperCamelCase = not is_remote_filesystem(__UpperCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__UpperCamelCase ) , fs._strip_protocol(__UpperCamelCase ) )
else:
fs.mv(__UpperCamelCase , __UpperCamelCase , recursive=__UpperCamelCase )
def lowercase__ ( )-> None:
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = threading.Lock()
| 321 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
SCREAMING_SNAKE_CASE__ = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}
class a_ ( lowerCamelCase ):
lowercase = """albert"""
def __init__( self , _SCREAMING_SNAKE_CASE=30000 , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=16384 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE="gelu_new" , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1e-12 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=3 , **_SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = vocab_size
UpperCamelCase = embedding_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_hidden_groups
UpperCamelCase = num_attention_heads
UpperCamelCase = inner_group_num
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = classifier_dropout_prob
UpperCamelCase = position_embedding_type
class a_ ( lowerCamelCase ):
@property
def A__ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCamelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCamelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaXLForCausalLM',
'XLMRobertaXLForMaskedLM',
'XLMRobertaXLForMultipleChoice',
'XLMRobertaXLForQuestionAnswering',
'XLMRobertaXLForSequenceClassification',
'XLMRobertaXLForTokenClassification',
'XLMRobertaXLModel',
'XLMRobertaXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 321 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10 ):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(__UpperCamelCase )
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
UpperCamelCase = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
UpperCamelCase = min(__UpperCamelCase , __UpperCamelCase )
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(__UpperCamelCase )
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
UpperCamelCase = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
UpperCamelCase = max(__UpperCamelCase , __UpperCamelCase )
# after all swaps are performed, send the values back to main
result_pipe[1].send(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> Tuple:
UpperCamelCase = []
UpperCamelCase = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe() )
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
UpperCamelCase = Pipe()
UpperCamelCase = Pipe()
process_array_.append(
Process(
target=__UpperCamelCase , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ) )
UpperCamelCase = temp_rs
UpperCamelCase = temp_rr
for i in range(1 , len(__UpperCamelCase ) - 1 ):
UpperCamelCase = Pipe()
UpperCamelCase = Pipe()
process_array_.append(
Process(
target=__UpperCamelCase , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ) )
UpperCamelCase = temp_rs
UpperCamelCase = temp_rr
process_array_.append(
Process(
target=__UpperCamelCase , args=(
len(__UpperCamelCase ) - 1,
arr[len(__UpperCamelCase ) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(__UpperCamelCase ) - 1],
) , ) )
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(__UpperCamelCase ) ):
UpperCamelCase = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def lowercase__ ( )-> Any:
UpperCamelCase = list(range(10 , 0 , -1 ) )
print("""Initial List""" )
print(*__UpperCamelCase )
UpperCamelCase = odd_even_transposition(__UpperCamelCase )
print("""Sorted List\n""" )
print(*__UpperCamelCase )
if __name__ == "__main__":
main()
| 321 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = 'docs/source/en/_toctree.yml'
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
UpperCamelCase = defaultdict(__UpperCamelCase )
UpperCamelCase = []
UpperCamelCase = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(__UpperCamelCase )
UpperCamelCase = new_doc_list
UpperCamelCase = [key for key, value in counts.items() if value > 1]
UpperCamelCase = []
for duplicate_key in duplicates:
UpperCamelCase = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__UpperCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
UpperCamelCase = sorted(__UpperCamelCase , key=lambda __UpperCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCamelCase ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__UpperCamelCase )
# Sort
return overview_doc
def lowercase__ ( __UpperCamelCase=False )-> List[str]:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
UpperCamelCase = api_doc[scheduler_idx]["""sections"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
UpperCamelCase = False
if new_scheduler_doc != scheduler_doc:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_scheduler_doc
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase__ ( __UpperCamelCase=False )-> Tuple:
with open(__UpperCamelCase , encoding="""utf-8""" ) as f:
UpperCamelCase = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
UpperCamelCase = False
UpperCamelCase = api_doc[pipeline_idx]["""sections"""]
UpperCamelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
UpperCamelCase = pipeline_doc["""section"""]
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if overwrite:
UpperCamelCase = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCamelCase )
# sort overall pipeline doc
UpperCamelCase = clean_doc_toc(__UpperCamelCase )
if new_pipeline_docs != pipeline_docs:
UpperCamelCase = True
if overwrite:
UpperCamelCase = new_pipeline_docs
if diff:
if overwrite:
UpperCamelCase = api_doc
with open(__UpperCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCamelCase , allow_unicode=__UpperCamelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 321 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]:
UpperCamelCase = 1.5
UpperCamelCase = int(factor * num_class_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 )
os.makedirs(F"{class_data_dir}/images" , exist_ok=__UpperCamelCase )
if len(list(Path(F"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCamelCase = client.query(text=__UpperCamelCase )
if len(__UpperCamelCase ) >= factor * num_class_images or num_images > 1E4:
break
else:
UpperCamelCase = int(factor * num_images )
UpperCamelCase = ClipClient(
url="""https://knn.laion.ai/knn-service""" , indice_name="""laion_400m""" , num_images=__UpperCamelCase , aesthetic_weight=0.1 , )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = tqdm(desc="""downloading real regularization images""" , total=__UpperCamelCase )
with open(F"{class_data_dir}/caption.txt" , """w""" ) as fa, open(F"{class_data_dir}/urls.txt" , """w""" ) as fa, open(
F"{class_data_dir}/images.txt" , """w""" ) as fa:
while total < num_class_images:
UpperCamelCase = class_images[count]
count += 1
try:
UpperCamelCase = requests.get(images["""url"""] )
if img.status_code == 200:
UpperCamelCase = Image.open(BytesIO(img.content ) )
with open(F"{class_data_dir}/images/{total}.jpg" , """wb""" ) as f:
f.write(img.content )
fa.write(images["""caption"""] + """\n""" )
fa.write(images["""url"""] + """\n""" )
fa.write(F"{class_data_dir}/images/{total}.jpg" + """\n""" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def lowercase__ ( )-> str:
UpperCamelCase = argparse.ArgumentParser("""""" , add_help=__UpperCamelCase )
parser.add_argument("""--class_prompt""" , help="""text prompt to retrieve images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--class_data_dir""" , help="""path to save images""" , required=__UpperCamelCase , type=__UpperCamelCase )
parser.add_argument("""--num_class_images""" , help="""number of images to download""" , default=200 , type=__UpperCamelCase )
return parser.parse_args()
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 321 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class a_ ( lowerCamelCase ):
lowercase = """detr"""
lowercase = ["""past_key_values"""]
lowercase = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=100 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=256 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="sine" , _SCREAMING_SNAKE_CASE="resnet50" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.1 , **_SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
UpperCamelCase = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = backbone_config.get("""model_type""" )
UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase = config_class.from_dict(_SCREAMING_SNAKE_CASE )
# set timm attributes to None
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None, None, None
UpperCamelCase = use_timm_backbone
UpperCamelCase = backbone_config
UpperCamelCase = num_channels
UpperCamelCase = num_queries
UpperCamelCase = d_model
UpperCamelCase = encoder_ffn_dim
UpperCamelCase = encoder_layers
UpperCamelCase = encoder_attention_heads
UpperCamelCase = decoder_ffn_dim
UpperCamelCase = decoder_layers
UpperCamelCase = decoder_attention_heads
UpperCamelCase = dropout
UpperCamelCase = attention_dropout
UpperCamelCase = activation_dropout
UpperCamelCase = activation_function
UpperCamelCase = init_std
UpperCamelCase = init_xavier_std
UpperCamelCase = encoder_layerdrop
UpperCamelCase = decoder_layerdrop
UpperCamelCase = encoder_layers
UpperCamelCase = auxiliary_loss
UpperCamelCase = position_embedding_type
UpperCamelCase = backbone
UpperCamelCase = use_pretrained_backbone
UpperCamelCase = dilation
# Hungarian matcher
UpperCamelCase = class_cost
UpperCamelCase = bbox_cost
UpperCamelCase = giou_cost
# Loss coefficients
UpperCamelCase = mask_loss_coefficient
UpperCamelCase = dice_loss_coefficient
UpperCamelCase = bbox_loss_coefficient
UpperCamelCase = giou_loss_coefficient
UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def A__ ( self ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def A__ ( self ) -> int:
"""simple docstring"""
return self.d_model
@classmethod
def A__ ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
return cls(backbone_config=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Dict[str, any]:
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
UpperCamelCase = self.backbone_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
class a_ ( lowerCamelCase ):
lowercase = version.parse("""1.11""" )
@property
def A__ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def A__ ( self ) -> float:
"""simple docstring"""
return 1e-5
@property
def A__ ( self ) -> int:
"""simple docstring"""
return 12
| 321 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class a_ :
lowercase = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """The column name of the images in the files."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
lowercase = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = {}
if self.train_dir is not None:
UpperCamelCase = self.train_dir
if self.validation_dir is not None:
UpperCamelCase = self.validation_dir
UpperCamelCase = data_files if data_files else None
@dataclass
class a_ :
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowercase = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowercase = field(default=lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowercase = field(
default=lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowercase = field(
default=0.75 , metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowercase = field(
default=lowerCamelCase , metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class a_ ( lowerCamelCase ):
lowercase = field(
default=1E-3 , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def lowercase__ ( )-> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
UpperCamelCase = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase = split["""train"""]
UpperCamelCase = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(F"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(F"New config: {config}" )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
UpperCamelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
UpperCamelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase = ViTMAEForPreTraining(__UpperCamelCase )
if training_args.do_train:
UpperCamelCase = ds["""train"""].column_names
else:
UpperCamelCase = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase = """image"""
elif "img" in column_names:
UpperCamelCase = """img"""
else:
UpperCamelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__UpperCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__UpperCamelCase ):
UpperCamelCase = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Compute absolute learning rate
UpperCamelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
UpperCamelCase = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["""train"""] if training_args.do_train else None , eval_dataset=ds["""validation"""] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
UpperCamelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase = last_checkpoint
UpperCamelCase = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("""train""" , train_result.metrics )
trainer.save_metrics("""train""" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCamelCase )
trainer.save_metrics("""eval""" , __UpperCamelCase )
# Write model card and (optionally) push to hub
UpperCamelCase = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> List[str]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = multiprocessing.Manager()
UpperCamelCase = manager.list()
UpperCamelCase = multiprocessing.Process(target=__UpperCamelCase , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Union[str, Any]:
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
UpperCamelCase = shutil.rmtree
UpperCamelCase = os.rmdir
UpperCamelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
UpperCamelCase = {}
with swallow_io():
with time_limit(__UpperCamelCase ):
exec(__UpperCamelCase , __UpperCamelCase )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
UpperCamelCase = rmtree
UpperCamelCase = rmdir
UpperCamelCase = chdir
@contextlib.contextmanager
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
def signal_handler(__UpperCamelCase , __UpperCamelCase ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL , __UpperCamelCase )
signal.signal(signal.SIGALRM , __UpperCamelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def lowercase__ ( )-> Optional[int]:
UpperCamelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(__UpperCamelCase ):
with contextlib.redirect_stderr(__UpperCamelCase ):
with redirect_stdin(__UpperCamelCase ):
yield
@contextlib.contextmanager
def lowercase__ ( )-> List[str]:
with tempfile.TemporaryDirectory() as dirname:
with chdir(__UpperCamelCase ):
yield dirname
class a_ ( lowerCamelCase ):
pass
class a_ ( io.StringIO ):
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
raise OSError
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
raise OSError
def A__ ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
return False
class a_ ( contextlib._RedirectStream ): # type: ignore
lowercase = """stdin"""
@contextlib.contextmanager
def lowercase__ ( __UpperCamelCase )-> Tuple:
if root == ".":
yield
return
UpperCamelCase = os.getcwd()
os.chdir(__UpperCamelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(__UpperCamelCase )
def lowercase__ ( __UpperCamelCase=None )-> int:
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
UpperCamelCase = None
UpperCamelCase = None
import os
UpperCamelCase = """1"""
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
import shutil
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
import subprocess
UpperCamelCase = None # type: ignore
UpperCamelCase = None
import sys
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
| 321 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ = 2_5_6
class a_ ( lowerCamelCase ):
lowercase = ["""melgan"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
super().__init__()
# From MELGAN
UpperCamelCase = math.log(1e-5 ) # Matches MelGAN training.
UpperCamelCase = 4.0 # Largest value for most examples
UpperCamelCase = 128
self.register_modules(
notes_encoder=_SCREAMING_SNAKE_CASE , continuous_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , melgan=_SCREAMING_SNAKE_CASE , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Any:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = output_range
if clip:
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , self.min_value , self.max_value )
# Scale to [0, 1].
UpperCamelCase = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=(-1.0, 1.0) , _SCREAMING_SNAKE_CASE=False ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase ,UpperCamelCase = input_range
UpperCamelCase = torch.clip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if clip else outputs
# Scale to [0, 1].
UpperCamelCase = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = input_tokens > 0
UpperCamelCase ,UpperCamelCase = self.notes_encoder(
encoder_input_tokens=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase = self.continuous_encoder(
encoder_inputs=_SCREAMING_SNAKE_CASE , encoder_inputs_mask=_SCREAMING_SNAKE_CASE )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
UpperCamelCase = noise_time
if not torch.is_tensor(_SCREAMING_SNAKE_CASE ):
UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(_SCREAMING_SNAKE_CASE ) and len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
UpperCamelCase = self.decoder(
encodings_and_masks=_SCREAMING_SNAKE_CASE , decoder_input_tokens=_SCREAMING_SNAKE_CASE , decoder_noise_time=_SCREAMING_SNAKE_CASE )
return logits
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = "numpy" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
"""simple docstring"""
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or callback_steps <= 0)
):
raise ValueError(
F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
F" {type(_SCREAMING_SNAKE_CASE )}." )
UpperCamelCase = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
UpperCamelCase = np.zeros([1, 0, self.n_dims] , np.floataa )
UpperCamelCase = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE ):
if i == 0:
UpperCamelCase = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
UpperCamelCase = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=_SCREAMING_SNAKE_CASE , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
UpperCamelCase = ones
UpperCamelCase = self.scale_features(
_SCREAMING_SNAKE_CASE , output_range=[-1.0, 1.0] , clip=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=_SCREAMING_SNAKE_CASE , continuous_mask=_SCREAMING_SNAKE_CASE , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
UpperCamelCase = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
UpperCamelCase = self.decode(
encodings_and_masks=_SCREAMING_SNAKE_CASE , input_tokens=_SCREAMING_SNAKE_CASE , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = self.scale_to_features(_SCREAMING_SNAKE_CASE , input_range=[-1.0, 1.0] )
UpperCamelCase = mel[:1]
UpperCamelCase = mel.cpu().float().numpy()
UpperCamelCase = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
logger.info("""Generated segment""" , _SCREAMING_SNAKE_CASE )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'.""" )
if output_type == "numpy":
UpperCamelCase = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
UpperCamelCase = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a_ ( lowerCamelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
UpperCamelCase = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
if isinstance(self.unet.config.sample_size , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
UpperCamelCase = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE )}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators." )
UpperCamelCase = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
UpperCamelCase = self.unet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
UpperCamelCase = self.scheduler.step(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , use_clipped_model_output=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = (image / 2 + 0.5).clamp(0 , 1 )
UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = b, a + b
return sum(__UpperCamelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = abs(__UpperCamelCase )
UpperCamelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowercase__ ( __UpperCamelCase )-> int:
UpperCamelCase = abs(__UpperCamelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def lowercase__ ( __UpperCamelCase )-> int:
return sum(int(__UpperCamelCase ) for c in str(abs(__UpperCamelCase ) ) )
def lowercase__ ( )-> None:
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(__UpperCamelCase , __UpperCamelCase ) -> None:
UpperCamelCase = F"{func.__name__}({value})"
UpperCamelCase = timeit(F"__main__.{call}" , setup="""import __main__""" )
print(F"{call:56} = {func(__UpperCamelCase )} -- {timing:.4f} seconds" )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(__UpperCamelCase , __UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
# Base Case
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] , __UpperCamelCase , __UpperCamelCase ):
# Color current vertex
UpperCamelCase = i
# Validate coloring
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , index + 1 ):
return True
# Backtrack
UpperCamelCase = -1
return False
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list[int]:
UpperCamelCase = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , 0 ):
return colored_vertices
return []
| 321 | 1 |
'''simple docstring'''
import os
def lowercase__ ( __UpperCamelCase )-> Any:
UpperCamelCase = len(grid[0] )
UpperCamelCase = len(__UpperCamelCase )
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(__UpperCamelCase ):
for j in range(n_rows - 3 ):
UpperCamelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
UpperCamelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
UpperCamelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
UpperCamelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
UpperCamelCase = max(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if max_product > largest:
UpperCamelCase = max_product
return largest
def lowercase__ ( )-> Optional[Any]:
UpperCamelCase = []
with open(os.path.dirname(__UpperCamelCase ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
UpperCamelCase = [[int(__UpperCamelCase ) for i in grid[j]] for j in range(len(__UpperCamelCase ) )]
return largest_product(__UpperCamelCase )
if __name__ == "__main__":
print(solution())
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 2000000 )-> int:
UpperCamelCase = [0 for i in range(n + 1 )]
UpperCamelCase = 1
UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __UpperCamelCase ):
UpperCamelCase = 1
UpperCamelCase = 0
for i in range(__UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = OrderedDict(
[
('align', 'EfficientNetImageProcessor'),
('beit', 'BeitImageProcessor'),
('bit', 'BitImageProcessor'),
('blip', 'BlipImageProcessor'),
('blip-2', 'BlipImageProcessor'),
('bridgetower', 'BridgeTowerImageProcessor'),
('chinese_clip', 'ChineseCLIPImageProcessor'),
('clip', 'CLIPImageProcessor'),
('clipseg', 'ViTImageProcessor'),
('conditional_detr', 'ConditionalDetrImageProcessor'),
('convnext', 'ConvNextImageProcessor'),
('convnextv2', 'ConvNextImageProcessor'),
('cvt', 'ConvNextImageProcessor'),
('data2vec-vision', 'BeitImageProcessor'),
('deformable_detr', 'DeformableDetrImageProcessor'),
('deit', 'DeiTImageProcessor'),
('deta', 'DetaImageProcessor'),
('detr', 'DetrImageProcessor'),
('dinat', 'ViTImageProcessor'),
('donut-swin', 'DonutImageProcessor'),
('dpt', 'DPTImageProcessor'),
('efficientformer', 'EfficientFormerImageProcessor'),
('efficientnet', 'EfficientNetImageProcessor'),
('flava', 'FlavaImageProcessor'),
('focalnet', 'BitImageProcessor'),
('git', 'CLIPImageProcessor'),
('glpn', 'GLPNImageProcessor'),
('groupvit', 'CLIPImageProcessor'),
('imagegpt', 'ImageGPTImageProcessor'),
('instructblip', 'BlipImageProcessor'),
('layoutlmv2', 'LayoutLMv2ImageProcessor'),
('layoutlmv3', 'LayoutLMv3ImageProcessor'),
('levit', 'LevitImageProcessor'),
('mask2former', 'Mask2FormerImageProcessor'),
('maskformer', 'MaskFormerImageProcessor'),
('mgp-str', 'ViTImageProcessor'),
('mobilenet_v1', 'MobileNetV1ImageProcessor'),
('mobilenet_v2', 'MobileNetV2ImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevitv2', 'MobileViTImageProcessor'),
('nat', 'ViTImageProcessor'),
('oneformer', 'OneFormerImageProcessor'),
('owlvit', 'OwlViTImageProcessor'),
('perceiver', 'PerceiverImageProcessor'),
('pix2struct', 'Pix2StructImageProcessor'),
('poolformer', 'PoolFormerImageProcessor'),
('regnet', 'ConvNextImageProcessor'),
('resnet', 'ConvNextImageProcessor'),
('sam', 'SamImageProcessor'),
('segformer', 'SegformerImageProcessor'),
('swiftformer', 'ViTImageProcessor'),
('swin', 'ViTImageProcessor'),
('swin2sr', 'Swin2SRImageProcessor'),
('swinv2', 'ViTImageProcessor'),
('table-transformer', 'DetrImageProcessor'),
('timesformer', 'VideoMAEImageProcessor'),
('tvlt', 'TvltImageProcessor'),
('upernet', 'SegformerImageProcessor'),
('van', 'ConvNextImageProcessor'),
('videomae', 'VideoMAEImageProcessor'),
('vilt', 'ViltImageProcessor'),
('vit', 'ViTImageProcessor'),
('vit_hybrid', 'ViTHybridImageProcessor'),
('vit_mae', 'ViTImageProcessor'),
('vit_msn', 'ViTImageProcessor'),
('xclip', 'CLIPImageProcessor'),
('yolos', 'YolosImageProcessor'),
]
)
SCREAMING_SNAKE_CASE__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def lowercase__ ( __UpperCamelCase )-> int:
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
UpperCamelCase = model_type_to_module_name(__UpperCamelCase )
UpperCamelCase = importlib.import_module(F".{module_name}" , """transformers.models""" )
try:
return getattr(__UpperCamelCase , __UpperCamelCase )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(__UpperCamelCase , """__name__""" , __UpperCamelCase ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
UpperCamelCase = importlib.import_module("""transformers""" )
if hasattr(__UpperCamelCase , __UpperCamelCase ):
return getattr(__UpperCamelCase , __UpperCamelCase )
return None
def lowercase__ ( __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , **__UpperCamelCase , )-> Any:
UpperCamelCase = get_file_from_repo(
__UpperCamelCase , __UpperCamelCase , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , resume_download=__UpperCamelCase , proxies=__UpperCamelCase , use_auth_token=__UpperCamelCase , revision=__UpperCamelCase , local_files_only=__UpperCamelCase , )
if resolved_config_file is None:
logger.info(
"""Could not locate the image processor configuration file, will try to use the model config instead.""" )
return {}
with open(__UpperCamelCase , encoding="""utf-8""" ) as reader:
return json.load(__UpperCamelCase )
class a_ :
def __init__( self ) -> List[str]:
"""simple docstring"""
raise EnvironmentError(
"""AutoImageProcessor is designed to be instantiated """
"""using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.""" )
@classmethod
@replace_list_option_in_docstrings(_SCREAMING_SNAKE_CASE )
def A__ ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase = kwargs.pop("""config""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = kwargs.pop("""trust_remote_code""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = True
UpperCamelCase ,UpperCamelCase = ImageProcessingMixin.get_image_processor_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = config_dict.get("""image_processor_type""" , _SCREAMING_SNAKE_CASE )
UpperCamelCase = None
if "AutoImageProcessor" in config_dict.get("""auto_map""" , {} ):
UpperCamelCase = config_dict["""auto_map"""]["""AutoImageProcessor"""]
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
UpperCamelCase = config_dict.pop("""feature_extractor_type""" , _SCREAMING_SNAKE_CASE )
if feature_extractor_class is not None:
logger.warning(
"""Could not find image processor class in the image processor config or the model config. Loading"""
""" based on pattern matching with the model's feature extractor configuration.""" )
UpperCamelCase = feature_extractor_class.replace("""FeatureExtractor""" , """ImageProcessor""" )
if "AutoFeatureExtractor" in config_dict.get("""auto_map""" , {} ):
UpperCamelCase = config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
UpperCamelCase = feature_extractor_auto_map.replace("""FeatureExtractor""" , """ImageProcessor""" )
logger.warning(
"""Could not find image processor auto map in the image processor config or the model config."""
""" Loading based on pattern matching with the model's feature extractor configuration.""" )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# It could be in `config.image_processor_type``
UpperCamelCase = getattr(_SCREAMING_SNAKE_CASE , """image_processor_type""" , _SCREAMING_SNAKE_CASE )
if hasattr(_SCREAMING_SNAKE_CASE , """auto_map""" ) and "AutoImageProcessor" in config.auto_map:
UpperCamelCase = config.auto_map["""AutoImageProcessor"""]
if image_processor_class is not None:
UpperCamelCase = image_processor_class_from_name(_SCREAMING_SNAKE_CASE )
UpperCamelCase = image_processor_auto_map is not None
UpperCamelCase = image_processor_class is not None or type(_SCREAMING_SNAKE_CASE ) in IMAGE_PROCESSOR_MAPPING
UpperCamelCase = resolve_trust_remote_code(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if has_remote_code and trust_remote_code:
UpperCamelCase = get_class_from_dynamic_module(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCamelCase = kwargs.pop("""code_revision""" , _SCREAMING_SNAKE_CASE )
if os.path.isdir(_SCREAMING_SNAKE_CASE ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
elif image_processor_class is not None:
return image_processor_class.from_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(_SCREAMING_SNAKE_CASE ) in IMAGE_PROCESSOR_MAPPING:
UpperCamelCase = IMAGE_PROCESSOR_MAPPING[type(_SCREAMING_SNAKE_CASE )]
return image_processor_class.from_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
raise ValueError(
F"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
F"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}" )
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
IMAGE_PROCESSOR_MAPPING.register(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
from timeit import timeit
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
number &= number - 1
result += 1
return result
def lowercase__ ( __UpperCamelCase )-> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCamelCase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowercase__ ( )-> None:
def do_benchmark(__UpperCamelCase ) -> None:
UpperCamelCase = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" )
UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" )
UpperCamelCase = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(__UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 321 | 1 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 321 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TimesformerModel',
'TimesformerForVideoClassification',
'TimesformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 321 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative in a semiconductor""" )
elif hole_conc < 0:
raise ValueError("""Hole concentration cannot be negative in a semiconductor""" )
elif intrinsic_conc < 0:
raise ValueError(
"""Intrinsic concentration cannot be negative in a semiconductor""" )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 321 | 1 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowercase__ ( __UpperCamelCase )-> Tuple:
return getitem, k
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Any:
return setitem, k, v
def lowercase__ ( __UpperCamelCase )-> Optional[Any]:
return delitem, k
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , *__UpperCamelCase )-> str:
try:
return fun(__UpperCamelCase , *__UpperCamelCase ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE__ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
SCREAMING_SNAKE_CASE__ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
SCREAMING_SNAKE_CASE__ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
SCREAMING_SNAKE_CASE__ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
SCREAMING_SNAKE_CASE__ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE__ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = HashMap(initial_block_size=4 )
UpperCamelCase = {}
for _, (fun, *args) in enumerate(__UpperCamelCase ):
UpperCamelCase ,UpperCamelCase = _run_operation(__UpperCamelCase , __UpperCamelCase , *__UpperCamelCase )
UpperCamelCase ,UpperCamelCase = _run_operation(__UpperCamelCase , __UpperCamelCase , *__UpperCamelCase )
assert my_res == py_res
assert str(__UpperCamelCase ) == str(__UpperCamelCase )
assert set(__UpperCamelCase ) == set(__UpperCamelCase )
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowercase__ ( )-> int:
def is_public(__UpperCamelCase ) -> bool:
return not name.startswith("""_""" )
UpperCamelCase = {name for name in dir({} ) if is_public(__UpperCamelCase )}
UpperCamelCase = {name for name in dir(HashMap() ) if is_public(__UpperCamelCase )}
assert dict_public_names > hash_public_names
| 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
SCREAMING_SNAKE_CASE__ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
SCREAMING_SNAKE_CASE__ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def A__ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
UpperCamelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 321 | 1 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> np.array:
UpperCamelCase = F"{sampling_rate}"
UpperCamelCase = """1"""
UpperCamelCase = """f32le"""
UpperCamelCase = [
"""ffmpeg""",
"""-i""",
"""pipe:0""",
"""-ac""",
ac,
"""-ar""",
ar,
"""-f""",
format_for_conversion,
"""-hide_banner""",
"""-loglevel""",
"""quiet""",
"""pipe:1""",
]
try:
with subprocess.Popen(__UpperCamelCase , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
UpperCamelCase = ffmpeg_process.communicate(__UpperCamelCase )
except FileNotFoundError as error:
raise ValueError("""ffmpeg was not found but is required to load audio files from filename""" ) from error
UpperCamelCase = output_stream[0]
UpperCamelCase = np.frombuffer(__UpperCamelCase , np.floataa )
if audio.shape[0] == 0:
raise ValueError("""Malformed soundfile""" )
return audio
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "f32le" , )-> Any:
UpperCamelCase = F"{sampling_rate}"
UpperCamelCase = """1"""
if format_for_conversion == "s16le":
UpperCamelCase = 2
elif format_for_conversion == "f32le":
UpperCamelCase = 4
else:
raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
UpperCamelCase = platform.system()
if system == "Linux":
UpperCamelCase = """alsa"""
UpperCamelCase = """default"""
elif system == "Darwin":
UpperCamelCase = """avfoundation"""
UpperCamelCase = """:0"""
elif system == "Windows":
UpperCamelCase = """dshow"""
UpperCamelCase = """default"""
UpperCamelCase = [
"""ffmpeg""",
"""-f""",
format_,
"""-i""",
input_,
"""-ac""",
ac,
"""-ar""",
ar,
"""-f""",
format_for_conversion,
"""-fflags""",
"""nobuffer""",
"""-hide_banner""",
"""-loglevel""",
"""quiet""",
"""pipe:1""",
]
UpperCamelCase = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
UpperCamelCase = _ffmpeg_stream(__UpperCamelCase , __UpperCamelCase )
for item in iterator:
yield item
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = "f32le" , )-> Optional[Any]:
if stream_chunk_s is not None:
UpperCamelCase = stream_chunk_s
else:
UpperCamelCase = chunk_length_s
UpperCamelCase = ffmpeg_microphone(__UpperCamelCase , __UpperCamelCase , format_for_conversion=__UpperCamelCase )
if format_for_conversion == "s16le":
UpperCamelCase = np.intaa
UpperCamelCase = 2
elif format_for_conversion == "f32le":
UpperCamelCase = np.floataa
UpperCamelCase = 4
else:
raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
if stride_length_s is None:
UpperCamelCase = chunk_length_s / 6
UpperCamelCase = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__UpperCamelCase , (int, float) ):
UpperCamelCase = [stride_length_s, stride_length_s]
UpperCamelCase = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
UpperCamelCase = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
UpperCamelCase = datetime.datetime.now()
UpperCamelCase = datetime.timedelta(seconds=__UpperCamelCase )
for item in chunk_bytes_iter(__UpperCamelCase , __UpperCamelCase , stride=(stride_left, stride_right) , stream=__UpperCamelCase ):
# Put everything back in numpy scale
UpperCamelCase = np.frombuffer(item["""raw"""] , dtype=__UpperCamelCase )
UpperCamelCase = (
item["""stride"""][0] // size_of_sample,
item["""stride"""][1] // size_of_sample,
)
UpperCamelCase = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False )-> Any:
UpperCamelCase = b""""""
UpperCamelCase ,UpperCamelCase = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}" )
UpperCamelCase = 0
for raw in iterator:
acc += raw
if stream and len(__UpperCamelCase ) < chunk_len:
UpperCamelCase = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__UpperCamelCase ) >= chunk_len:
# We are flushing the accumulator
UpperCamelCase = (_stride_left, stride_right)
UpperCamelCase = {"""raw""": acc[:chunk_len], """stride""": stride}
if stream:
UpperCamelCase = False
yield item
UpperCamelCase = stride_left
UpperCamelCase = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__UpperCamelCase ) > stride_left:
UpperCamelCase = {"""raw""": acc, """stride""": (_stride_left, 0)}
if stream:
UpperCamelCase = False
yield item
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
UpperCamelCase = 2**24 # 16Mo
try:
with subprocess.Popen(__UpperCamelCase , stdout=subprocess.PIPE , bufsize=__UpperCamelCase ) as ffmpeg_process:
while True:
UpperCamelCase = ffmpeg_process.stdout.read(__UpperCamelCase )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError("""ffmpeg was not found but is required to stream audio files from filename""" ) from error
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None )-> str:
if version.parse(hfh.__version__ ).release < version.parse("""0.11.0""" ).release:
# old versions of hfh don't url-encode the file path
UpperCamelCase = quote(__UpperCamelCase )
return hfh.hf_hub_url(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" , revision=__UpperCamelCase )
| 321 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCamelCase , 2 ) - pow(__UpperCamelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCamelCase , 2 ) + pow(__UpperCamelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class a_ ( unittest.TestCase ):
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [[1, 2, 4], [1, 2, 3, 4]]
UpperCamelCase = DisjunctiveConstraint(_SCREAMING_SNAKE_CASE )
self.assertTrue(isinstance(dc.token_ids , _SCREAMING_SNAKE_CASE ) )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
DisjunctiveConstraint(_SCREAMING_SNAKE_CASE ) # fails here
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [[1, 2, 3], [1, 2, 4]]
UpperCamelCase = DisjunctiveConstraint(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(1 )
UpperCamelCase = stepped is True and completed is False and reset is False
self.assertTrue(_SCREAMING_SNAKE_CASE )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(2 )
UpperCamelCase = stepped is True and completed is False and reset is False
self.assertTrue(_SCREAMING_SNAKE_CASE )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(3 )
UpperCamelCase = stepped is True and completed is True and reset is False
self.assertTrue(_SCREAMING_SNAKE_CASE )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
UpperCamelCase = DisjunctiveConstraint(_SCREAMING_SNAKE_CASE )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 321 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowercase__ ( __UpperCamelCase )-> Union[str, Any]:
UpperCamelCase = min(__UpperCamelCase ) # min() finds the minimum value
UpperCamelCase = max(__UpperCamelCase ) # max() finds the maximum value
UpperCamelCase = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCamelCase = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCamelCase = 0
for count in range(__UpperCamelCase ):
while holes[count] > 0:
holes[count] -= 1
UpperCamelCase = count + min_val
i += 1
def lowercase__ ( )-> Any:
UpperCamelCase = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__UpperCamelCase )
print("""Sorted order is:""" , """ """.join(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class a_ ( unittest.TestCase ):
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = tempfile.mkdtemp()
# fmt: off
UpperCamelCase = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
UpperCamelCase = dict(zip(_SCREAMING_SNAKE_CASE , range(len(_SCREAMING_SNAKE_CASE ) ) ) )
UpperCamelCase = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
UpperCamelCase = {"""unk_token""": """<unk>"""}
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_SCREAMING_SNAKE_CASE ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"""image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
UpperCamelCase = os.path.join(self.tmpdirname , _SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase = [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_rust_tokenizer()
UpperCamelCase = self.get_image_processor()
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
processor_slow.save_pretrained(self.tmpdirname )
UpperCamelCase = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=_SCREAMING_SNAKE_CASE )
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
processor_fast.save_pretrained(self.tmpdirname )
UpperCamelCase = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _SCREAMING_SNAKE_CASE )
self.assertIsInstance(processor_fast.image_processor , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase = self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
UpperCamelCase = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
UpperCamelCase = processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer(_SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """lower newer"""
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_SCREAMING_SNAKE_CASE ):
processor()
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(images=_SCREAMING_SNAKE_CASE , visual_prompt=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """conditional_pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_SCREAMING_SNAKE_CASE ):
processor()
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.batch_decode(_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 321 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE , beta_end=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_SCREAMING_SNAKE_CASE , prediction_type=_SCREAMING_SNAKE_CASE , sample_max_value=_SCREAMING_SNAKE_CASE , )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = self.dummy_sample_deter + 0.1
UpperCamelCase = self.dummy_sample_deter - 0.1
UpperCamelCase = samplea.shape[0]
UpperCamelCase = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase = torch.arange(_SCREAMING_SNAKE_CASE )[0:3, None].repeat(1 , _SCREAMING_SNAKE_CASE )
UpperCamelCase = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase = scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1e-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1e-3
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3
def A__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter
UpperCamelCase = torch.manual_seed(0 )
for t in reversed(range(_SCREAMING_SNAKE_CASE ) ):
# 1. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict previous mean of sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(_SCREAMING_SNAKE_CASE ):
if i == len(_SCREAMING_SNAKE_CASE ) - 1:
UpperCamelCase = -1
else:
UpperCamelCase = timesteps[i + 1]
UpperCamelCase = scheduler.previous_timestep(_SCREAMING_SNAKE_CASE )
UpperCamelCase = prev_t.item()
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [100, 87, 50, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> str:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if num == 0:
return "0b0"
UpperCamelCase = False
if num < 0:
UpperCamelCase = True
UpperCamelCase = -num
UpperCamelCase = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(__UpperCamelCase ) for e in binary )
return "0b" + "".join(str(__UpperCamelCase ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
from __future__ import annotations
import math
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
# approximate the overall size of segment tree with given value
UpperCamelCase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCamelCase = [0 for i in range(0 , 4 * size )]
UpperCamelCase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return idx * 2 + 1
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCamelCase = a[left_element - 1]
else:
UpperCamelCase = (left_element + right_element) // 2
self.build(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.build(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCamelCase = val
if left_element != right_element:
UpperCamelCase = val
UpperCamelCase = val
UpperCamelCase = True
UpperCamelCase = True
return True
UpperCamelCase = (left_element + right_element) // 2
self.update(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.update(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = max(
self.segment_tree[self.left(_SCREAMING_SNAKE_CASE )] , self.segment_tree[self.right(_SCREAMING_SNAKE_CASE )] )
return True
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = False
if left_element != right_element:
UpperCamelCase = self.lazy[idx]
UpperCamelCase = self.lazy[idx]
UpperCamelCase = True
UpperCamelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCamelCase = (left_element + right_element) // 2
UpperCamelCase = self.query(self.left(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCamelCase = self.query(self.right(_SCREAMING_SNAKE_CASE ) , mid + 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
SCREAMING_SNAKE_CASE__ = 1_5
SCREAMING_SNAKE_CASE__ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 321 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class a_ ( unittest.TestCase ):
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
UpperCamelCase = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=_SCREAMING_SNAKE_CASE , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = """sgugger/tiny-distilbert-classification"""
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , only_pretrain_model=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=_SCREAMING_SNAKE_CASE , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE , [config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE , [config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE , [config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = """patrickvonplaten/t5-tiny-random"""
UpperCamelCase = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE , configs=[config] )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , """Cannot do xla on CPU.""" )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=_SCREAMING_SNAKE_CASE , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , use_xla=_SCREAMING_SNAKE_CASE , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
UpperCamelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=_SCREAMING_SNAKE_CASE , save_to_csv=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE , """inf_time.csv""" ) , inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE , """inf_mem.csv""" ) , env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE , """env.csv""" ) , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
benchmark.run()
self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE , """env.csv""" ) ).exists() )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE ):
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """sequential""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """cumulative""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """current""" ) )
self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=_SCREAMING_SNAKE_CASE , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_SCREAMING_SNAKE_CASE , """log.txt""" ) , log_print=_SCREAMING_SNAKE_CASE , trace_memory_line_by_line=_SCREAMING_SNAKE_CASE , eager_mode=_SCREAMING_SNAKE_CASE , multi_process=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = TensorFlowBenchmark(_SCREAMING_SNAKE_CASE )
UpperCamelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE , """log.txt""" ) ).exists() )
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 1000 )-> int:
UpperCamelCase = -1
UpperCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase = (n * n - 2 * a * n) // (2 * n - 2 * a)
UpperCamelCase = n - a - b
if c * c == (a * a + b * b):
UpperCamelCase = a * b * c
if candidate >= product:
UpperCamelCase = candidate
return product
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> list[int]:
UpperCamelCase = len(__UpperCamelCase )
for i in range(__UpperCamelCase ):
for j in range(i + 1 , __UpperCamelCase ):
if numbers[j] < numbers[i]:
UpperCamelCase ,UpperCamelCase = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE__ = [int(item) for item in user_input.split(',')]
print(exchange_sort(unsorted))
| 321 |
'''simple docstring'''
import argparse
import struct
import unittest
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = data
# Initialize hash values
UpperCamelCase = [
0x6A_09_E6_67,
0xBB_67_AE_85,
0x3C_6E_F3_72,
0xA5_4F_F5_3A,
0x51_0E_52_7F,
0x9B_05_68_8C,
0x1F_83_D9_AB,
0x5B_E0_CD_19,
]
# Initialize round constants
UpperCamelCase = [
0x42_8A_2F_98,
0x71_37_44_91,
0xB5_C0_FB_CF,
0xE9_B5_DB_A5,
0x39_56_C2_5B,
0x59_F1_11_F1,
0x92_3F_82_A4,
0xAB_1C_5E_D5,
0xD8_07_AA_98,
0x12_83_5B_01,
0x24_31_85_BE,
0x55_0C_7D_C3,
0x72_BE_5D_74,
0x80_DE_B1_FE,
0x9B_DC_06_A7,
0xC1_9B_F1_74,
0xE4_9B_69_C1,
0xEF_BE_47_86,
0x0F_C1_9D_C6,
0x24_0C_A1_CC,
0x2D_E9_2C_6F,
0x4A_74_84_AA,
0x5C_B0_A9_DC,
0x76_F9_88_DA,
0x98_3E_51_52,
0xA8_31_C6_6D,
0xB0_03_27_C8,
0xBF_59_7F_C7,
0xC6_E0_0B_F3,
0xD5_A7_91_47,
0x06_CA_63_51,
0x14_29_29_67,
0x27_B7_0A_85,
0x2E_1B_21_38,
0x4D_2C_6D_FC,
0x53_38_0D_13,
0x65_0A_73_54,
0x76_6A_0A_BB,
0x81_C2_C9_2E,
0x92_72_2C_85,
0xA2_BF_E8_A1,
0xA8_1A_66_4B,
0xC2_4B_8B_70,
0xC7_6C_51_A3,
0xD1_92_E8_19,
0xD6_99_06_24,
0xF4_0E_35_85,
0x10_6A_A0_70,
0x19_A4_C1_16,
0x1E_37_6C_08,
0x27_48_77_4C,
0x34_B0_BC_B5,
0x39_1C_0C_B3,
0x4E_D8_AA_4A,
0x5B_9C_CA_4F,
0x68_2E_6F_F3,
0x74_8F_82_EE,
0x78_A5_63_6F,
0x84_C8_78_14,
0x8C_C7_02_08,
0x90_BE_FF_FA,
0xA4_50_6C_EB,
0xBE_F9_A3_F7,
0xC6_71_78_F2,
]
UpperCamelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( _SCREAMING_SNAKE_CASE ) -> bytes:
"""simple docstring"""
UpperCamelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_SCREAMING_SNAKE_CASE ) + 8) % 64))
UpperCamelCase = struct.pack(""">Q""" , (len(_SCREAMING_SNAKE_CASE ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) -> None:
"""simple docstring"""
UpperCamelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase = list(struct.unpack(""">16L""" , _SCREAMING_SNAKE_CASE ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_00_00_00_00
# Compression
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 6 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 11 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 25 )
UpperCamelCase = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g)
UpperCamelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_00_00_00_00
UpperCamelCase = self.ror(_SCREAMING_SNAKE_CASE , 2 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 13 ) ^ self.ror(_SCREAMING_SNAKE_CASE , 22 )
UpperCamelCase = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase = (sa + maj) % 0x1_00_00_00_00
UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = (
g,
f,
e,
((d + tempa) % 0x1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0x1_00_00_00_00),
)
UpperCamelCase = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase = [
((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase = """""".join([hex(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)
class a_ ( unittest.TestCase ):
def A__ ( self ) -> None:
"""simple docstring"""
import hashlib
UpperCamelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_SCREAMING_SNAKE_CASE ).hash , hashlib.shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest() )
def lowercase__ ( )-> None:
import doctest
doctest.testmod()
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
UpperCamelCase = f.read()
else:
UpperCamelCase = bytes(__UpperCamelCase , """utf-8""" )
print(SHAaaa(__UpperCamelCase ).hash )
if __name__ == "__main__":
main()
| 321 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase = StableDiffusionXLImgaImgPipeline
lowercase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
lowercase = PipelineTesterMixin.required_optional_params - {"""latents"""}
lowercase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowercase = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A__ ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=_SCREAMING_SNAKE_CASE , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=80 , cross_attention_dim=64 , )
UpperCamelCase = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
UpperCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=32 , )
UpperCamelCase = CLIPTextModel(_SCREAMING_SNAKE_CASE )
UpperCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=_SCREAMING_SNAKE_CASE )
UpperCamelCase = CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE )
UpperCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ) -> Any:
"""simple docstring"""
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
UpperCamelCase = image / 2 + 0.5
if str(_SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
UpperCamelCase = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase = self.get_dummy_components()
UpperCamelCase = StableDiffusionXLImgaImgPipeline(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = sd_pipe.to(_SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase = sd_pipe(**_SCREAMING_SNAKE_CASE ).images
UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCamelCase = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def A__ ( self ) -> Tuple:
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def A__ ( self ) -> Dict:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
pass
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.get_dummy_components()
UpperCamelCase = StableDiffusionXLImgaImgPipeline(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = sd_pipe.to(_SCREAMING_SNAKE_CASE )
UpperCamelCase = sd_pipe.to(_SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
# forward without prompt embeds
UpperCamelCase = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase = 3 * ["""this is a negative prompt"""]
UpperCamelCase = negative_prompt
UpperCamelCase = 3 * [inputs["""prompt"""]]
UpperCamelCase = sd_pipe(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
UpperCamelCase = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase = 3 * ["""this is a negative prompt"""]
UpperCamelCase = 3 * [inputs.pop("""prompt""" )]
(
(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,(
UpperCamelCase
) ,
) = sd_pipe.encode_prompt(_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE )
UpperCamelCase = sd_pipe(
**_SCREAMING_SNAKE_CASE , prompt_embeds=_SCREAMING_SNAKE_CASE , negative_prompt_embeds=_SCREAMING_SNAKE_CASE , pooled_prompt_embeds=_SCREAMING_SNAKE_CASE , negative_pooled_prompt_embeds=_SCREAMING_SNAKE_CASE , )
UpperCamelCase = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="cpu" , _SCREAMING_SNAKE_CASE=torch.floataa , _SCREAMING_SNAKE_CASE=0 ) -> str:
"""simple docstring"""
UpperCamelCase = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
UpperCamelCase = np.random.RandomState(_SCREAMING_SNAKE_CASE ).standard_normal((1, 4, 64, 64) )
UpperCamelCase = torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
UpperCamelCase = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.get_inputs(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pipe(**_SCREAMING_SNAKE_CASE ).images
UpperCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
UpperCamelCase = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7e-3
| 321 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
SCREAMING_SNAKE_CASE__ = _symbol_database.Default()
SCREAMING_SNAKE_CASE__ = _descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
SCREAMING_SNAKE_CASE__ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
SCREAMING_SNAKE_CASE__ = 4_5
SCREAMING_SNAKE_CASE__ = 1_5_8_1
SCREAMING_SNAKE_CASE__ = 1_5_1_7
SCREAMING_SNAKE_CASE__ = 1_5_7_0
SCREAMING_SNAKE_CASE__ = 1_5_8_4
SCREAMING_SNAKE_CASE__ = 1_7_9_3
SCREAMING_SNAKE_CASE__ = 1_7_9_5
SCREAMING_SNAKE_CASE__ = 1_9_1_6
SCREAMING_SNAKE_CASE__ = 1_8_6_4
SCREAMING_SNAKE_CASE__ = 1_9_0_5
SCREAMING_SNAKE_CASE__ = 1_9_1_9
SCREAMING_SNAKE_CASE__ = 2_4_2_9
SCREAMING_SNAKE_CASE__ = 2_2_0_8
SCREAMING_SNAKE_CASE__ = 2_4_1_8
SCREAMING_SNAKE_CASE__ = 2_3_2_3
SCREAMING_SNAKE_CASE__ = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 321 | 1 |
'''simple docstring'''
from math import factorial
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
UpperCamelCase = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
UpperCamelCase = float(factorial(__UpperCamelCase ) )
coefficient /= factorial(__UpperCamelCase ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 321 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 321 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.