'''simple docstring'''






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def    _SCREAMING_SNAKE_CASE		(			):
     def filter_log(__snake_case       : Tuple							):
          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(__snake_case							)
     try:
          yield
     finally:
          logger.removeFilter(__snake_case							)


_UpperCAmelCase						:    Tuple     					=					'''\
@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}
}
'''

_UpperCAmelCase						:    Union[str, Any]     					=					'''\
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.
'''

_UpperCAmelCase						:    Union[str, Any]     					=					'''
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 lowercase_					(		datasets.Metric ):







   """simple docstring"""

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

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

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

        _A								=     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:
                  _A								=     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__,   )

        ((_A)		,					(_A)		,					(_A))								=     self.cached_bertscorer.score(
            cands=UpperCamelCase__,   refs=UpperCamelCase__,   verbose=UpperCamelCase__,   batch_size=UpperCamelCase__,   )
        _A								=     {
            'precision': P.tolist(),
            'recall': R.tolist(),
            'f1': F.tolist(),
            'hashcode': hashcode,
        }
        return output_dict
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: str=None,   UpperCamelCase__			: Dict=None,   **UpperCamelCase__			: 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:
             _A								=     [[ref] if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else ref for ref in references]
        super().add_batch(predictions=UpperCamelCase__,   references=UpperCamelCase__,   **UpperCamelCase__    )

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




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase						:    Dict     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



