

"""simple docstring"""




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def 		A    (       ):




   '''simple docstring'''


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

   SCREAMING_SNAKE_CASE__									= datasets.utils.logging.get_logger("""transformers.modeling_utils"""      )
   logger.addFilter(SCREAMING_SNAKE_CASE_      )
   try:
      yield
   finally:
      logger.removeFilter(SCREAMING_SNAKE_CASE_      )


A_							:     Tuple     =	"\\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"

A_							:     Union[str, Any]     =	"\\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"

A_							:     int     =	"\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 				lowerCamelCase  (datasets.Metric     ):

   def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      List[str]:
      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    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Any=None      ,			__UpperCAmelCase			:  Dict=None      ,			__UpperCAmelCase			:  str=None      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  int=False      ,			__UpperCAmelCase			:  Dict=None      ,			__UpperCAmelCase			:  Union[str, Any]=6_4      ,			__UpperCAmelCase			:  Any=4      ,			__UpperCAmelCase			:  Any=False      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  Dict=None      ,			__UpperCAmelCase			:  int=False      ,			)				->      Union[str, Any]:
      SCREAMING_SNAKE_CASE__									= bert_score.utils.get_hash
      SCREAMING_SNAKE_CASE__									= bert_score.BERTScorer

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

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

      SCREAMING_SNAKE_CASE__									= 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:
            SCREAMING_SNAKE_CASE__									= 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      ,			)

      ((SCREAMING_SNAKE_CASE__)							,						(SCREAMING_SNAKE_CASE__)							,						(SCREAMING_SNAKE_CASE__))									= self.cached_bertscorer.score(
          cands=__UpperCAmelCase      ,			refs=__UpperCAmelCase      ,			verbose=__UpperCAmelCase      ,			batch_size=__UpperCAmelCase      ,			)
      SCREAMING_SNAKE_CASE__									= {
          """precision""": P.tolist(),
          """recall""": R.tolist(),
          """f1""": F.tolist(),
          """hashcode""": hashcode,
      }
      return output_dict

   def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  List[Any]=None      ,			__UpperCAmelCase			:  str=None      ,			**__UpperCAmelCase			:  List[Any]    )				->      Union[str, Any]:






      if references is not None:
         SCREAMING_SNAKE_CASE__									= [[ref] if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ) else ref for ref in references]
      super().add_batch(predictions=__UpperCAmelCase      ,			references=__UpperCAmelCase      ,			**__UpperCAmelCase    )





   def    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  str=None      ,			__UpperCAmelCase			:  List[str]=None      ,			**__UpperCAmelCase			:  Tuple    )				->      Dict:






      if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
         SCREAMING_SNAKE_CASE__									= [reference]
      super().add(prediction=__UpperCAmelCase      ,			reference=__UpperCAmelCase      ,			**__UpperCAmelCase    )





"""simple docstring"""




class 				lowerCamelCase  :

			def __init__(   self			:  Dict    )				->      None:
						SCREAMING_SNAKE_CASE__									= [0, 1]





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  int    )				->      list:
						if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(__UpperCAmelCase    ):
												self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
						return self.sequence[:index]

def 		A    (       ):




			'''simple docstring'''


			print(
			    """Fibonacci Series Using Dynamic Programming\n"""						,			"""Enter the index of the Fibonacci number you want to calculate """						,			"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""						,			sep=""""""						,			)
			SCREAMING_SNAKE_CASE__									= Fibonacci()

			while True:
						SCREAMING_SNAKE_CASE__									= input(""">> """      )
						if prompt in {"exit", "quit"}:
									break

						try:
									SCREAMING_SNAKE_CASE__									= int(snake_case__      )
						except ValueError:
									print("""Enter a number or 'exit'"""      )
									continue

						print(fibonacci.get(snake_case__      )      )


if __name__ == "__main__":
						main()


