





'''simple docstring'''







from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_lowercase       						=			'\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

_lowercase       						=			'\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

_lowercase       						=			'\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,		_KWARGS_DESCRIPTION					)
class 			_lowercase						(       datasets.Metric					):







     def     UpperCamelCase			( self    ) ->	MetricInfo:
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''string'''    ,   id='''token'''    )    ,   id='''sequence'''    ),
                  '''references''': datasets.Sequence(
                      datasets.Sequence(datasets.Value('''string'''    ,   id='''token'''    )    ,   id='''sequence'''    )    ,   id='''references'''    ),
              }    )    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__ = 1    ,   A__ = 4    ,   ) ->	Dict[str, float]:
      return {
          "google_bleu": gleu_score.corpus_gleu(
              list_of_references=A__    ,   hypotheses=A__    ,   min_len=A__    ,   max_len=A__    )
      }








'''simple docstring'''







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 			_lowercase						(       yaml.SafeLoader					):







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      snake_case				=			[self.constructed_objects[key_node] for key_node, _ in node.value]
      snake_case				=			[tuple(A__    ) if isinstance(A__    ,   A__    ) else key for key in keys]
      snake_case				=			Counter(A__    )
      snake_case				=			[key for key in counter if counter[key] > 1]
      if duplicate_keys:
       raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=False    ) ->	List[Any]:
      snake_case				=			super().construct_mapping(A__    ,   deep=A__    )
      self._check_no_duplicates_on_constructed_node(A__    )
      return mapping




def    __UpperCamelCase	(	a    : str     ) ->Tuple[Optional[str], str]:
 snake_case				=			list(readme_content.splitlines()     )
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  snake_case				=			full_content[1:].index('''---'''     ) + 1
  snake_case				=			'''\n'''.join(full_content[1:sep_idx]     )
  return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

 return None, "\n".join(a     )
class 			_lowercase						(       __a					):
     # class attributes
     _UpperCAmelCase										=					{'''train_eval_index'''}  # train-eval-index in the YAML metadata







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
       snake_case				,		snake_case				=			_split_yaml_from_readme(readme_file.read()    )
      if yaml_string is not None:
       return cls.from_yaml_string(A__    )
      else:
       return cls()







     def     UpperCamelCase			( self    ,   A__    ) ->	str:
      if path.exists():
       with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
        snake_case				=			readme_file.read()
      else:
       snake_case				=			None
      snake_case				=			self._to_readme(A__    )
      with open(A__    ,   '''w'''    ,   encoding='''utf-8'''    ) as readme_file:
       readme_file.write(A__    )







     def     UpperCamelCase			( self    ,   A__ = None    ) ->	str:
      if readme_content is not None:
       snake_case				,		snake_case				=			_split_yaml_from_readme(A__    )
       snake_case				=			'''---\n''' + self.to_yaml_string() + '''---\n''' + content
      else:
       snake_case				=			'''---\n''' + self.to_yaml_string() + '''---\n'''
      return full_content







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      snake_case				=			yaml.load(A__    ,   Loader=_NoDuplicateSafeLoader    ) or {}

      # Convert the YAML keys to DatasetMetadata fields
      snake_case				=			{
          (key.replace('''-'''    ,   '''_'''    ) if key.replace('''-'''    ,   '''_'''    ) in cls._FIELDS_WITH_DASHES else key): value
          for key, value in metadata_dict.items()
      }
      return cls(**A__    )







     def     UpperCamelCase			( self    ) ->	str:
      return yaml.safe_dump(
          {
              (key.replace('''_'''    ,   '''-'''    ) if key in self._FIELDS_WITH_DASHES else key): value
              for key, value in self.items()
          }    ,   sort_keys=A__    ,   allow_unicode=A__    ,   encoding='''utf-8'''    ,   ).decode('''utf-8'''    )




_lowercase       						=			{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _lowercase       						=			ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
   ap.add_argument('readme_filepath')
   _lowercase       						=			ap.parse_args()

   _lowercase       						=			Path(args.readme_filepath)
   _lowercase       						=			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)

