
'''simple docstring'''


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def 				__magic_name__(					):
						def filter_log(UpperCAmelCase__   :					Tuple  ):
												return False if "This IS expected if you are initializing" in record.msg else True

						UpperCamelCase_    :  Optional[Any]				    =    datasets.utils.logging.get_logger("""transformers.modeling_utils"""  )
						logger.addFilter(UpperCAmelCase__  )
						try:
												yield
						finally:
												logger.removeFilter(UpperCAmelCase__  )


__SCREAMING_SNAKE_CASE  =			"\\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"

__SCREAMING_SNAKE_CASE  =			"\\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"

__SCREAMING_SNAKE_CASE  =			"\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  ):




		def   _snake_case  (     self				:Optional[int])				-> List[str]:






								'''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   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:int=None ,						_SCREAMING_SNAKE_CASE				:Tuple=None ,						_SCREAMING_SNAKE_CASE				:Tuple=None ,						_SCREAMING_SNAKE_CASE				:Optional[int]=False ,						_SCREAMING_SNAKE_CASE				:Any=False ,						_SCREAMING_SNAKE_CASE				:Dict=None ,						_SCREAMING_SNAKE_CASE				:Dict=6_4 ,						_SCREAMING_SNAKE_CASE				:str=4 ,						_SCREAMING_SNAKE_CASE				:List[str]=False ,						_SCREAMING_SNAKE_CASE				:Any=False ,						_SCREAMING_SNAKE_CASE				:int=None ,						_SCREAMING_SNAKE_CASE				:List[Any]=False ,						)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  Any				    =    bert_score.utils.get_hash
								UpperCamelCase_    :  List[str]				    =    bert_score.BERTScorer

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

								if num_layers is None:
														UpperCamelCase_    :  List[Any]				    =    bert_score.utils.modelalayers[model_type]

								UpperCamelCase_    :  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:
																				UpperCamelCase_    :  Any				    =    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 ,						)

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




		def   _snake_case  (     self				:Any ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=None ,						_SCREAMING_SNAKE_CASE				:Tuple=None ,						**_SCREAMING_SNAKE_CASE				:List[str])				-> List[str]:






								'''simple docstring'''






								if references is not None:
														UpperCamelCase_    :  Optional[int]				    =    [[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   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Dict=None ,						_SCREAMING_SNAKE_CASE				:List[str]=None ,						**_SCREAMING_SNAKE_CASE				:List[Any])				-> Optional[int]:






								'''simple docstring'''






								if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  Any				    =    [reference]
								super().add(prediction=_SCREAMING_SNAKE_CASE ,						reference=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)






'''simple docstring'''


from __future__ import annotations

from typing import Any




class 						__snake_case :




		def __init__(     self				:int ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:  # noqa: A002






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    item
								UpperCamelCase_    :  int				    =    next







class 						__snake_case :




		def __init__(     self				:Optional[Any])				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  Node | None				    =    None
								UpperCamelCase_    :  Tuple				    =    0




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    Node(_SCREAMING_SNAKE_CASE ,						self.head)
								self.size += 1




		def   _snake_case  (     self				:int)				-> Any:






								'''simple docstring'''






								if self.head is None:
														return None
								else:
														UpperCamelCase_    :  Any				    =    self.head.item
														UpperCamelCase_    :  List[str]				    =    self.head.next
														self.size -= 1
														return item




		def   _snake_case  (     self				:int)				-> bool:






								'''simple docstring'''






								return self.head is None




		def __str__(     self				:Any)				-> str:






								'''simple docstring'''






								if self.is_empty():
														return ""
								else:
														UpperCamelCase_    :  List[str]				    =    self.head
														UpperCamelCase_    :  Tuple				    =    """"""
														UpperCamelCase_    :  list[str]				    =    []
														while iterate:
																				item_list.append(str(iterate.item))
																				UpperCamelCase_    :  Tuple				    =    iterate.next

														UpperCamelCase_    :  Optional[int]				    =    """ --> """.join(_SCREAMING_SNAKE_CASE)

														return item_str





		def __len__(     self				:List[Any])				-> int:






								'''simple docstring'''






								return self.size




