

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

import yaml




class      UpperCamelCase (     yaml.SafeLoader   ):

				def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			[self.constructed_objects[key_node] for key_node, _ in node.value]
					SCREAMING_SNAKE_CASE								=			[tuple(snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else key for key in keys]
					SCREAMING_SNAKE_CASE								=			Counter(snake_case__    )
					SCREAMING_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 :      str							,    snake_case__ :      str							,    snake_case__ :      Any=False    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			super().construct_mapping(snake_case__							,    deep=snake_case__    )
					self._check_no_duplicates_on_constructed_node(snake_case__    )
					return mapping


def        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							Tuple[Optional[str], str]:
	'''simple docstring'''


	SCREAMING_SNAKE_CASE								=			list(readme_content.splitlines()	)
	if full_content and full_content[0] == "---" and "---" in full_content[1:]:
		SCREAMING_SNAKE_CASE								=			full_content[1:].index('---'	) + 1
		SCREAMING_SNAKE_CASE								=			'\n'.join(full_content[1:sep_idx]	)
		return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

	return None, "\n".join(_UpperCamelCase	)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
				# class attributes
				__UpperCamelCase         ={"train_eval_index"}  # train-eval-index in the YAML metadata

				@classmethod
				def UpperCamelCase			(			cls :      Union[str, Any]							,    snake_case__ :      Path    ):

					"""simple docstring"""






					with open(snake_case__							,    encoding='utf-8'    ) as readme_file:
						SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			_split_yaml_from_readme(readme_file.read()    )
					if yaml_string is not None:
						return cls.from_yaml_string(snake_case__    )
					else:
						return cls()

				def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Path    ):

					"""simple docstring"""






					if path.exists():
						with open(snake_case__							,    encoding='utf-8'    ) as readme_file:
							SCREAMING_SNAKE_CASE								=			readme_file.read()
					else:
						SCREAMING_SNAKE_CASE								=			None
					SCREAMING_SNAKE_CASE								=			self._to_readme(snake_case__    )
					with open(snake_case__							,    'w'							,    encoding='utf-8'    ) as readme_file:
						readme_file.write(snake_case__    )

				def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Optional[str] = None    ):

					"""simple docstring"""






					if readme_content is not None:
						SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			_split_yaml_from_readme(snake_case__    )
						SCREAMING_SNAKE_CASE								=			'---\n' + self.to_yaml_string() + '---\n' + content
					else:
						SCREAMING_SNAKE_CASE								=			'---\n' + self.to_yaml_string() + '---\n'
					return full_content

				@classmethod
				def UpperCamelCase			(			cls :      Dict							,    snake_case__ :      str    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			yaml.load(snake_case__							,    Loader=_NoDuplicateSafeLoader    ) or {}

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




				def UpperCamelCase			(			self :      str    ):

					"""simple docstring"""






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


a_    :						int    		=      {
    "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

						a_    :						int    		=      ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
						ap.add_argument("readme_filepath")
						a_    :						Optional[int]    		=      ap.parse_args()

						a_    :						List[str]    		=      Path(args.readme_filepath)
						a_    :						str    		=      DatasetMetadata.from_readme(readme_filepath)
						print(dataset_metadata)
						dataset_metadata.to_readme(readme_filepath)




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

a_    :						str    		=      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a_    :						int    		=      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,			_KWARGS_DESCRIPTION   )
class      UpperCamelCase (     datasets.Metric   ):

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     return datasets.MetricInfo(
         description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
             {
                 'predictions': datasets.Value('string'							,    id='sequence'    ),
                 'references': datasets.Value('string'							,    id='sequence'    ),
             }    )							,    codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,    reference_urls=[
             'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
             'https://en.wikipedia.org/wiki/METEOR',
         ]							,    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     import nltk

     nltk.download('wordnet'    )
     if NLTK_VERSION >= version.Version('3.6.5'    ):
      nltk.download('punkt'    )
     if NLTK_VERSION >= version.Version('3.6.6'    ):
      nltk.download('omw-1.4'    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






     if NLTK_VERSION >= version.Version('3.6.5'    ):
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(
              word_tokenize(snake_case__    )							,    word_tokenize(snake_case__    )							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]
     else:
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(snake_case__							,    snake_case__							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]

     return {"meteor": np.mean(snake_case__    )}

