code: 
  
  
  
  
  
  import functools
  from contextlib import contextmanager
  
  import bert_score
  from packaging import version
  
  import datasets
  
  
  
  
  @contextmanager
  def 						A_			(  )			->						Dict:
         def filter_log(lowercase_  ):
                return False if "This IS expected if you are initializing" in record.msg else True
  
         _snake_case      :							List[str]     				=						datasets.utils.logging.get_logger('''transformers.modeling_utils'''  )
         logger.addFilter(lowercase_  )
         try:
                yield
         finally:
                logger.removeFilter(lowercase_  )
  
  
  lowerCAmelCase_   =   "\\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_   =   "\\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_   =   "\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 				A    (datasets.Metric				):
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=False     ,  lowercase_=False     ,  lowercase_=None     ,  lowercase_=64     ,  lowercase_=4     ,  lowercase_=False     ,  lowercase_=False     ,  lowercase_=None     ,  lowercase_=False     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						bert_score.utils.get_hash
               _snake_case      :							Dict     				=						bert_score.BERTScorer
  
               if version.parse(bert_score.__version__					) >= version.parse('''0.3.10'''					):
                      _snake_case      :							Optional[Any]     				=						functools.partial(lowercase_     ,  use_fast_tokenizer=lowercase_					)
                      _snake_case      :							Tuple     				=						functools.partial(lowercase_     ,  use_fast_tokenizer=lowercase_					)
               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"
                      _snake_case      :							Any     				=						bert_score.utils.langamodel[lang.lower()]
  
               if num_layers is None:
                      _snake_case      :							List[Any]     				=						bert_score.utils.modelalayers[model_type]
  
               _snake_case      :							Dict     				=						get_hash(
                   model=lowercase_     ,  num_layers=lowercase_     ,  idf=lowercase_     ,  rescale_with_baseline=lowercase_     ,  use_custom_baseline=baseline_path is not None     ,  )
  
               with filter_logging_context():
                      if not hasattr(self     ,  '''cached_bertscorer'''					) or self.cached_bertscorer.hash != hashcode:
                             _snake_case      :							int     				=						scorer(
                                 model_type=lowercase_     ,  num_layers=lowercase_     ,  batch_size=lowercase_     ,  nthreads=lowercase_     ,  all_layers=lowercase_     ,  idf=lowercase_     ,  device=lowercase_     ,  lang=lowercase_     ,  rescale_with_baseline=lowercase_     ,  baseline_path=lowercase_     ,  )
  
               ((_snake_case)				,    (_snake_case)				,    (_snake_case))      :							Union[str, Any]     				=						self.cached_bertscorer.score(
                   cands=lowercase_     ,  refs=lowercase_     ,  verbose=lowercase_     ,  batch_size=lowercase_     ,  )
               _snake_case      :							Any     				=						{
                   '''precision''': P.tolist(),
                   '''recall''': R.tolist(),
                   '''f1''': F.tolist(),
                   '''hashcode''': hashcode,
               }
               return output_dict
  
  
  
        def    __a (					self     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_					)      ->   str:
  
  
  
               '''simple docstring'''
  
               if references is not None:
                      _snake_case      :							List[Any]     				=						[[ref] if isinstance(lowercase_     ,  lowercase_					) else ref for ref in references]
               super().add_batch(predictions=lowercase_     ,  references=lowercase_     ,  **lowercase_					)
  
  
  
        def    __a (					self     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               if isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							List[Any]     				=						[reference]
               super().add(prediction=lowercase_     ,  reference=lowercase_     ,  **lowercase_					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						torch.Tensor:
         # The first operation in a checkpoint can't be in-place, but it's
         # nice to have in-place addition during inference. Thus...
         if not inplace:
                _snake_case      :							Any     				=						ma + ma
         else:
                ma += ma
  
         return ma
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						-1 * len(lowercase_  )
         _snake_case      :							Any     				=						list(range(len(tensor.shape[:zero_index]  )  )  )
         return tensor.permute(first_inds + [zero_index + i for i in inds]  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         return t.reshape(t.shape[:-no_dims] + (-1,)  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_ = 1E-4  )			->						torch.Tensor:
         _snake_case      :							Any     				=						mask.expand(*value.shape  )
         return torch.sum(mask * value      ,		dim=lowercase_  ) / (eps + torch.sum(lowercase_      ,		dim=lowercase_  ))
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_ = 2.31_25      ,		lowercase_ = 21.68_75      ,		lowercase_ = 64  )			->						torch.Tensor:
         _snake_case      :							int     				=						torch.linspace(lowercase_      ,		lowercase_      ,		no_bins - 1      ,		device=pts.device  )
         _snake_case      :							Optional[Any]     				=						torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2      ,		dim=-1  )  )
         return torch.bucketize(lowercase_      ,		lowercase_  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict:
         _snake_case      :							Dict     				=						dicts[0]
         _snake_case      :							str     				=						{}
         for k, v in first.items():
                _snake_case      :							List[Any]     				=						[d[k] for d in dicts]
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Any     				=						dict_multimap(lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							Dict     				=						fn(lowercase_  )
  
         return new_dict
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						v_bins.view(((1,) * len(x.shape  )) + (len(lowercase_  ),)  )
         _snake_case      :							Union[str, Any]     				=						x[..., None] - reshaped_bins
         _snake_case      :							Optional[Any]     				=						torch.argmin(torch.abs(lowercase_  )      ,		dim=-1  )
         return nn.functional.one_hot(lowercase_      ,		num_classes=len(lowercase_  )  ).float()
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = 0      ,		lowercase_ = 0  )			->						torch.Tensor:
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[]
         for i, s in enumerate(data.shape[:no_batch_dims]  ):
                _snake_case      :							Optional[Any]     				=						torch.arange(lowercase_  )
                _snake_case      :							Union[str, Any]     				=						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
                ranges.append(lowercase_  )
  
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[slice(lowercase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
         _snake_case      :							Union[str, Any]     				=						inds
         ranges.extend(lowercase_  )
         # Matt note: Editing this to get around the behaviour of using a list as an array index changing
         # in recent Numpy versions
         return data[tuple(lowercase_  )]
  
  
  lowerCAmelCase_   =   TypeVar("T")
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict[Any, Union[dict, list, tuple, Any]]:
         _snake_case      :							Dict[Any, Union[dict, list, tuple, Any]]     				=						{}
         for k, v in dic.items():
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Dict     				=						dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							List[Any]     				=						tree_map(lowercase_      ,		lowercase_      ,		lowercase_  )
  
         return new_dict
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						dict:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						list:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						tuple:
         ...
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         if isinstance(lowercase_      ,		lowercase_  ):
                return dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return [tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree]
         elif isinstance(lowercase_      ,		lowercase_  ):
                return tuple(tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return fn(lowercase_  )
         else:
                print(type(lowercase_  )  )
                raise ValueError('''Not supported'''  )
  lowerCAmelCase_   =   partial(tree_map, leaf_type=torch.Tensor)
  
  
  
  
  
  
  