






"""simple docstring"""



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



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

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


__A		:	int      =		'''\
@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}
}
'''

__A		:	Any      =		'''\
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.
'''

__A		:	List[str]      =		'''
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  _UpperCAmelCase	(			datasets.Metric							):




					def   A  (    self					:						Any       )  ->  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   A  (    self					:						Optional[int]   ,						A					:						Dict   ,						A					:						List[Any]   ,						A					:						int=None   ,						A					:						List[Any]=None   ,						A					:						Optional[Any]=None   ,						A					:						str=False   ,						A					:						Union[str, Any]=False   ,						A					:						Tuple=None   ,						A					:						Tuple=64   ,						A					:						int=4   ,						A					:						Dict=False   ,						A					:						Tuple=False   ,						A					:						Dict=None   ,						A					:						int=False   ,						)  ->  Any:
							lowercase_			:			Union[str, Any]								=       bert_score.utils.get_hash
							lowercase_			:			Union[str, Any]								=       bert_score.BERTScorer

							if version.parse(bert_score.__version__       ) >= version.parse('''0.3.10'''       ):
									lowercase_			:			Optional[Any]								=       functools.partial(A   ,						use_fast_tokenizer=A       )
									lowercase_			:			Tuple								=       functools.partial(A   ,						use_fast_tokenizer=A       )
							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"
									lowercase_			:			Dict								=       bert_score.utils.langamodel[lang.lower()]

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

							lowercase_			:			Dict								=       get_hash(
							    model=A   ,						num_layers=A   ,						idf=A   ,						rescale_with_baseline=A   ,						use_custom_baseline=baseline_path is not None   ,						)

							with filter_logging_context():
									if not hasattr(self   ,						'''cached_bertscorer'''       ) or self.cached_bertscorer.hash != hashcode:
											lowercase_			:			List[Any]								=       scorer(
											    model_type=A   ,						num_layers=A   ,						batch_size=A   ,						nthreads=A   ,						all_layers=A   ,						idf=A   ,						device=A   ,						lang=A   ,						rescale_with_baseline=A   ,						baseline_path=A   ,						)

							(lowercase_)			:			int								=       self.cached_bertscorer.score(
							    cands=A   ,						refs=A   ,						verbose=A   ,						batch_size=A   ,						)
							lowercase_			:			List[Any]								=       {
							    '''precision''': P.tolist(),
							    '''recall''': R.tolist(),
							    '''f1''': F.tolist(),
							    '''hashcode''': hashcode,
							}
							return output_dict




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






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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		:	Any      =		{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	List[str]      =		['''MarkupLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MarkupLMForQuestionAnswering''',
						    '''MarkupLMForSequenceClassification''',
						    '''MarkupLMForTokenClassification''',
						    '''MarkupLMModel''',
						    '''MarkupLMPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
						from .feature_extraction_markuplm import MarkupLMFeatureExtractor
						from .processing_markuplm import MarkupLMProcessor
						from .tokenization_markuplm import MarkupLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_markuplm_fast import MarkupLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_markuplm import (
												    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MarkupLMForQuestionAnswering,
												    MarkupLMForSequenceClassification,
												    MarkupLMForTokenClassification,
												    MarkupLMModel,
												    MarkupLMPreTrainedModel,
												)


else:
						import sys

						__A		:	Optional[Any]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)



