

'''simple docstring'''
from typing import List

import numpy as np


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={key: len(_UpperCamelCase				) for key, value in gen_kwargs.items() if isinstance(_UpperCamelCase							, _UpperCamelCase				)}
     if len(set(lists_lengths.values()				)				) > 1:
          raise RuntimeError(
              (
                  'Sharding is ambiguous for this dataset: '
                  + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                  + '\n'.join(f"\t- key {key} has length {length}" for key, length in lists_lengths.items()				)
                  + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                  + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
              )				)
     _SCREAMING_SNAKE_CASE							    =max(lists_lengths.values()							, default=0				)
     return max(1							, _UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			List[range]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     for group_idx in range(_UpperCamelCase				):
          _SCREAMING_SNAKE_CASE							    =num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
          if num_shards_to_add == 0:
               break
          _SCREAMING_SNAKE_CASE							    =shards_indices_per_group[-1].stop if shards_indices_per_group else 0
          _SCREAMING_SNAKE_CASE							    =range(_UpperCamelCase							, start + num_shards_to_add				)
          shards_indices_per_group.append(_UpperCamelCase				)
     return shards_indices_per_group


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     int				)      ->			List[dict]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =_number_of_shards_in_gen_kwargs(_UpperCamelCase				)
     if num_shards == 1:
          return [dict(_UpperCamelCase				)]
     else:
          _SCREAMING_SNAKE_CASE							    =_distribute_shards(num_shards=_UpperCamelCase							, max_num_jobs=_UpperCamelCase				)
          return [
              {
                  key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                  if isinstance(_UpperCamelCase							, _UpperCamelCase				)
                  else value
                  for key, value in gen_kwargs.items()
              }
              for group_idx in range(len(_UpperCamelCase				)				)
          ]


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[dict]				)      ->			dict:
     """simple docstring"""




     return {
         key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
         if isinstance(gen_kwargs_list[0][key]							, _UpperCamelCase				)
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }







def        _lowerCAmelCase					(						_UpperCamelCase						:     np.random.Generator							, _UpperCamelCase						:     dict				)      ->			dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={len(_UpperCamelCase				) for value in gen_kwargs.values() if isinstance(_UpperCamelCase							, _UpperCamelCase				)}
     _SCREAMING_SNAKE_CASE							    ={}
     for size in list_sizes:
          _SCREAMING_SNAKE_CASE							    =list(range(_UpperCamelCase				)				)
          rng.shuffle(indices_per_size[size]				)
     # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
     _SCREAMING_SNAKE_CASE							    =dict(_UpperCamelCase				)
     for key, value in shuffled_kwargs.items():
          if isinstance(_UpperCamelCase							, _UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =[value[i] for i in indices_per_size[len(_UpperCamelCase				)]]
     return shuffled_kwargs



'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCamelCase :      Any					 =     "\\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"

lowerCamelCase :      Optional[Any]					 =     "\\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"

lowerCamelCase :      Optional[Any]					 =     "\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 							A__    (       datasets.Metric  ):





  def 						A  (		self      :	Tuple )	->    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 						A  (		self      :	Union[str, Any]							,					_a      :	Union[str, Any] )	->    Optional[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 						A  (		self      :	int							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	List[str]=0.9							,					_a      :	Dict=3							,					_a      :	Optional[int]=0.5 )	->    Optional[int]:





       '''simple docstring'''






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

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