


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def 	lowerCamelCase_       (			):




		"""simple docstring"""






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

		lowerCAmelCase__  :     Union[str, Any]       	=   datasets.utils.logging.get_logger('''transformers.modeling_utils'''			)
		logger.addFilter(_a			)
		try:
				yield
		finally:
				logger.removeFilter(_a			)


lowerCamelCase		=						'''\
@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}
}
'''

lowerCamelCase		=						'''\
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.
'''

lowerCamelCase		=						'''
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    _a				(							datasets.Metric):




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

			if version.parse(bert_score.__version__						) >= version.parse('''0.3.10'''						):
					lowerCAmelCase__  :     Optional[Any]       	=   functools.partial(_SCREAMING_SNAKE_CASE						,   use_fast_tokenizer=_SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     List[str]       	=   functools.partial(_SCREAMING_SNAKE_CASE						,   use_fast_tokenizer=_SCREAMING_SNAKE_CASE						)
			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"
					lowerCAmelCase__  :     Optional[Any]       	=   bert_score.utils.langamodel[lang.lower()]

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

			lowerCAmelCase__  :     str       	=   get_hash(
			    model=_SCREAMING_SNAKE_CASE						,   num_layers=_SCREAMING_SNAKE_CASE						,   idf=_SCREAMING_SNAKE_CASE						,   rescale_with_baseline=_SCREAMING_SNAKE_CASE						,   use_custom_baseline=baseline_path is not None						,   )

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

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




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




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








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase		=						{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




