


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 				UpperCamelCase_	(					)->   List[str]:




						'''simple docstring'''
						def filter_log(__snake_case      :    str  ):
												return False if "This IS expected if you are initializing" in record.msg else True

						__magic_name__					=      datasets.utils.logging.get_logger('transformers.modeling_utils'  )
						logger.addFilter(__snake_case  )
						try:
												yield
						finally:
												logger.removeFilter(__snake_case  )


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

UpperCamelCase			:					Optional[int]         =							'''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

UpperCamelCase			:					Dict         =							'''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,      _KWARGS_DESCRIPTION)
class      __snake_case				(   datasets.Metric):
					def 	a			(						self       :						str  )							->					Union[str, Any]:



											'''simple docstring'''





											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 	a			(						self       :						str	,      _lowerCAmelCase       :						List[str]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						int=None	,      _lowerCAmelCase       :						List[str]=None	,      _lowerCAmelCase       :						List[str]=None	,      _lowerCAmelCase       :						str=False	,      _lowerCAmelCase       :						str=False	,      _lowerCAmelCase       :						Union[str, Any]=None	,      _lowerCAmelCase       :						Union[str, Any]=6_4	,      _lowerCAmelCase       :						str=4	,      _lowerCAmelCase       :						List[Any]=False	,      _lowerCAmelCase       :						Union[str, Any]=False	,      _lowerCAmelCase       :						Optional[Any]=None	,      _lowerCAmelCase       :						List[Any]=False	,      )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      bert_score.utils.get_hash
											__magic_name__					=      bert_score.BERTScorer

											if version.parse(bert_score.__version__  ) >= version.parse('0.3.10'  ):
																	__magic_name__					=      functools.partial(_lowerCAmelCase	,      use_fast_tokenizer=_lowerCAmelCase  )
																	__magic_name__					=      functools.partial(_lowerCAmelCase	,      use_fast_tokenizer=_lowerCAmelCase  )
											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"
																	__magic_name__					=      bert_score.utils.langamodel[lang.lower()]

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

											__magic_name__					=      get_hash(
											    model=_lowerCAmelCase	,      num_layers=_lowerCAmelCase	,      idf=_lowerCAmelCase	,      rescale_with_baseline=_lowerCAmelCase	,      use_custom_baseline=baseline_path is not None	,      )

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

											((__magic_name__)		,      (__magic_name__)		,      (__magic_name__))					=      self.cached_bertscorer.score(
											    cands=_lowerCAmelCase	,      refs=_lowerCAmelCase	,      verbose=_lowerCAmelCase	,      batch_size=_lowerCAmelCase	,      )
											__magic_name__					=      {
											    'precision': P.tolist(),
											    'recall': R.tolist(),
											    'f1': F.tolist(),
											    'hashcode': hashcode,
											}
											return output_dict
					def 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						List[str]=None	,      _lowerCAmelCase       :						Optional[Any]=None	,      **_lowerCAmelCase       :						List[str]  )							->					str:



											'''simple docstring'''





											if references is not None:
																	__magic_name__					=      [[ref] if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) else ref for ref in references]
											super().add_batch(predictions=_lowerCAmelCase	,      references=_lowerCAmelCase	,      **_lowerCAmelCase  )







					def 	a			(						self       :						Any	,      _lowerCAmelCase       :						Dict=None	,      _lowerCAmelCase       :						Any=None	,      **_lowerCAmelCase       :						Any  )							->					List[Any]:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      [reference]
											super().add(prediction=_lowerCAmelCase	,      reference=_lowerCAmelCase	,      **_lowerCAmelCase  )





from typing import TYPE_CHECKING

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


UpperCamelCase			:					Optional[int]         =							{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					List[str]         =							[
					    '''DPRContextEncoderTokenizerFast''',
					    '''DPRQuestionEncoderTokenizerFast''',
					    '''DPRReaderTokenizerFast''',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[Any]         =							[
					    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPRContextEncoder''',
					    '''DPRPretrainedContextEncoder''',
					    '''DPRPreTrainedModel''',
					    '''DPRPretrainedQuestionEncoder''',
					    '''DPRPretrainedReader''',
					    '''DPRQuestionEncoder''',
					    '''DPRReader''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[int]         =							[
					    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFDPRContextEncoder''',
					    '''TFDPRPretrainedContextEncoder''',
					    '''TFDPRPretrainedQuestionEncoder''',
					    '''TFDPRPretrainedReader''',
					    '''TFDPRQuestionEncoder''',
					    '''TFDPRReader''',
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					UpperCamelCase			:					List[Any]         =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

