




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def      __magic_name__							(					):






			'''simple docstring'''







			def filter_log(A						:   List[str]  ):
						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(lowerCamelCase__  )
			try:
						yield
			finally:
						logger.removeFilter(lowerCamelCase__  )


__lowerCAmelCase						:							List[str]			     =						'\\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'

__lowerCAmelCase						:							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'

__lowerCAmelCase						:							List[str]			     =						'\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        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



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

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

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

							a       =     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:
													a       =     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   , )

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





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	str=None   , __lowerCamelCase							:	Optional[Any]=None   , **__lowerCamelCase							:	List[str]				)   ->	List[str]:

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





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Any=None   , __lowerCamelCase							:	List[Any]=None   , **__lowerCamelCase							:	Dict				)   ->	Optional[int]:

							if isinstance(__lowerCamelCase   , __lowerCamelCase				):
										a       =     [reference]
							super().add(prediction=__lowerCamelCase   , reference=__lowerCamelCase   , **__lowerCamelCase				)











"""simple docstring"""







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


A     =      logging.get_logger(__name__)






class   UpperCAmelCase__		(   UpperCamelCase  ):



		def __init__(			self	:     List[str]   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Tuple:






									'''simple docstring'''

									A    =       language
									A    =       train_language



		def        A_		(			self	:     Optional[int]   ,				snake_case	:     Any					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.language if self.train_language is None else self.train_language
									A    =       self._read_tsv(os.path.join(snake_case   ,				f"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       f"""train-{i}"""
																A    =       line[0]
																A    =       line[1]
																A    =       'contradiction' if line[2] == 'contradictory' else line[2]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     int   ,				snake_case	:     int					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self._read_tsv(os.path.join(snake_case   ,				'XNLI-1.0/xnli.test.tsv'					)					)
									A    =       []
									for i, line in enumerate(snake_case					):
																if i == 0:
																							continue
																A    =       line[0]
																if language != self.language:
																							continue
																A    =       f"""test-{i}"""
																A    =       line[6]
																A    =       line[7]
																A    =       line[1]
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_a} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training input {text_b} is not a string"""					)
																if not isinstance(snake_case   ,				snake_case					):
																							raise ValueError(f"""Training label {label} is not a string"""					)
																examples.append(InputExample(guid=snake_case   ,				text_a=snake_case   ,				text_b=snake_case   ,				label=snake_case					)					)
									return examples



		def        A_		(			self	:     Any					)      ->    List[Any]:






									'''simple docstring'''

									return ["contradiction", "entailment", "neutral"]



A     =      {
    'xnli': XnliProcessor,
}

A     =      {
    'xnli': 'classification',
}

A     =      {
    'xnli': 3,
}



