


import torch
from torch import autograd



class     snake_case__  (      autograd.Function  ):
				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      inputs.numel()
										__magic_name__				:							Optional[Any]				=      int(0.005 * nb_elems      ) + 1
										if sigmoid:
																__magic_name__				:							Optional[int]				=      (torch.sigmoid(_UpperCAmelCase      ) > threshold).type(inputs.type()      )
										else:
																__magic_name__				:							List[Any]				=      (inputs > threshold).type(inputs.type()      )
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																__magic_name__				:							Dict				=      inputs.flatten().kthvalue(max(nb_elems - nb_min     ,      1      )      ).values
																__magic_name__				:							Union[str, Any]				=      (inputs > k_threshold).type(inputs.type()      )
										return mask





				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return gradOutput, None, None






class     snake_case__  (      autograd.Function  ):
				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      inputs.clone()
										__magic_name__				:							Union[str, Any]				=      inputs.flatten().sort(descending=_UpperCAmelCase      )
										__magic_name__				:							int				=      int(threshold * inputs.numel()      )

										# flat_out and mask access the same memory.
										__magic_name__				:							List[str]				=      mask.flatten()
										__magic_name__				:							Tuple				=      0
										__magic_name__				:							Union[str, Any]				=      1
										return mask





				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										return gradOutput, None






class     snake_case__  (      a_  ):
				@staticmethod
				def  UpperCamelCase_						(  _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      inputs.clone()
										__magic_name__				:							Union[str, Any]				=      inputs.abs().flatten().sort(descending=_UpperCAmelCase      )
										__magic_name__				:							Optional[Any]				=      int(threshold * inputs.numel()      )

										# flat_out and mask access the same memory.
										__magic_name__				:							Any				=      mask.flatten()
										__magic_name__				:							str				=      0
										__magic_name__				:							Optional[Any]				=      1
										return mask






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						def filter_log(lowercase__  :List[Any]				):
												return False if "This IS expected if you are initializing" in record.msg else True

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


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'

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'

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  UpperCamelCase_						(  self      ):




										"""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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=64     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      bert_score.utils.get_hash
										__magic_name__				:							Dict				=      bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse('0.3.10'      ):
																__magic_name__				:							Any				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
																__magic_name__				:							Tuple				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
										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__				:							List[Any]				=      bert_score.utils.langamodel[lang.lower()]

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

										__magic_name__				:							List[str]				=      get_hash(
										    model=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      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__				:							Optional[int]				=      scorer(
																						    model_type=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      nthreads=_UpperCAmelCase     ,      all_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      device=_UpperCAmelCase     ,      lang=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      baseline_path=_UpperCAmelCase     ,      )

										((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							List[Any]				=      self.cached_bertscorer.score(
										    cands=_UpperCAmelCase     ,      refs=_UpperCAmelCase     ,      verbose=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      )
										__magic_name__				:							int				=      {
										    'precision': P.tolist(),
										    'recall': R.tolist(),
										    'f1': F.tolist(),
										    'hashcode': hashcode,
										}
										return output_dict
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




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


