





"""simple docstring"""
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def    A__							(     ):
							def filter_log(UpperCamelCase			):
														return False if "This IS expected if you are initializing" in record.msg else True

							A						=    datasets.utils.logging.get_logger("transformers.modeling_utils"			)
							logger.addFilter(UpperCamelCase			)
							try:
														yield
							finally:
														logger.removeFilter(UpperCamelCase			)


_snake_case	:						Optional[Any]					=					'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

_snake_case	:						Union[str, Any]					=					'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

_snake_case	:						Union[str, Any]					=					'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,				_KWARGS_DESCRIPTION					)
class    _UpperCAmelCase  (      datasets.Metric					):


					def       lowerCamelCase			(   self     :List[Any]						):
												return datasets.MetricInfo(
												    description=_DESCRIPTION	,			citation=_CITATION	,			homepage="https://github.com/Tiiiger/bert_score"	,			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/Tiiiger/bert_score"]	,			reference_urls=[
												        "https://github.com/Tiiiger/bert_score",
												        "https://arxiv.org/abs/1904.09675",
												    ]	,			)


					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :List[str]=None	,			__UpperCamelCase     :str=None	,			__UpperCamelCase     :Optional[Any]=None	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=False	,			__UpperCamelCase     :List[str]=None	,			__UpperCamelCase     :int=64	,			__UpperCamelCase     :int=4	,			__UpperCamelCase     :Any=False	,			__UpperCamelCase     :Any=False	,			__UpperCamelCase     :Optional[int]=None	,			__UpperCamelCase     :List[Any]=False	,			):
												A						=    bert_score.utils.get_hash
												A						=    bert_score.BERTScorer

												if version.parse(bert_score.__version__						) >= version.parse("0.3.10"						):
																			A						=    functools.partial(__UpperCamelCase	,			use_fast_tokenizer=__UpperCamelCase						)
																			A						=    functools.partial(__UpperCamelCase	,			use_fast_tokenizer=__UpperCamelCase						)
												elif use_fast_tokenizer:
																			raise ImportWarning(
																			    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
																			    "You can install it with `pip install \"bert-score>=0.3.10\"`."						)

												if model_type is None:
																			assert lang is not None, "either lang or model_type should be specified"
																			A						=    bert_score.utils.langamodel[lang.lower()]

												if num_layers is None:
																			A						=    bert_score.utils.modelalayers[model_type]

												A						=    get_hash(
												    model=__UpperCamelCase	,			num_layers=__UpperCamelCase	,			idf=__UpperCamelCase	,			rescale_with_baseline=__UpperCamelCase	,			use_custom_baseline=baseline_path is not None	,			)

												with filter_logging_context():
																			if not hasattr(self	,			"cached_bertscorer"						) or self.cached_bertscorer.hash != hashcode:
																										A						=    scorer(
																										    model_type=__UpperCamelCase	,			num_layers=__UpperCamelCase	,			batch_size=__UpperCamelCase	,			nthreads=__UpperCamelCase	,			all_layers=__UpperCamelCase	,			idf=__UpperCamelCase	,			device=__UpperCamelCase	,			lang=__UpperCamelCase	,			rescale_with_baseline=__UpperCamelCase	,			baseline_path=__UpperCamelCase	,			)

												((A),      (A),      (A))						=    self.cached_bertscorer.score(
												    cands=__UpperCamelCase	,			refs=__UpperCamelCase	,			verbose=__UpperCamelCase	,			batch_size=__UpperCamelCase	,			)
												A						=    {
												    "precision": P.tolist(),
												    "recall": R.tolist(),
												    "f1": F.tolist(),
												    "hashcode": hashcode,
												}
												return output_dict


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :Tuple=None	,			__UpperCamelCase     :List[str]=None	,			**__UpperCamelCase     :Optional[int]						):
												# References can be strings or lists of strings
												# Let's change strings to lists of strings with one element
												if references is not None:
																			A						=    [[ref] if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else ref for ref in references]
												super().add_batch(predictions=__UpperCamelCase	,			references=__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :Union[str, Any]=None	,			__UpperCamelCase     :Optional[Any]=None	,			**__UpperCamelCase     :List[Any]						):
												# References can be strings or lists of strings
												# Let's change strings to lists of strings with one element
												if isinstance(__UpperCamelCase	,			__UpperCamelCase						):
																			A						=    [reference]
												super().add(prediction=__UpperCamelCase	,			reference=__UpperCamelCase	,			**__UpperCamelCase						)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


