


"""simple docstring"""


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_snake_case        =				version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
   from nltk import word_tokenize


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

_snake_case        =				'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_snake_case        =				'''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION			)
class 	_a   (   datasets.Metric			):
    def   _UpperCamelCase							(			self				:       int					):
          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				:       str				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]					):
          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				:       int				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       Any				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=0.9				,       SCREAMING_SNAKE_CASE__				:       List[str]=3				,       SCREAMING_SNAKE_CASE__				:       int=0.5					):
          if NLTK_VERSION >= version.Version('3.6.5'					):
                lowerCamelCase__							     =		[
                    meteor_score.single_meteor_score(
                        word_tokenize(_lowerCAmelCase					)				,       word_tokenize(_lowerCAmelCase					)				,       alpha=_lowerCAmelCase				,       beta=_lowerCAmelCase				,       gamma=_lowerCAmelCase					)
                    for ref, pred in zip(_lowerCAmelCase				,       _lowerCAmelCase					)
                ]
          else:
                lowerCamelCase__							     =		[
                    meteor_score.single_meteor_score(_lowerCAmelCase				,       _lowerCAmelCase				,       alpha=_lowerCAmelCase				,       beta=_lowerCAmelCase				,       gamma=_lowerCAmelCase					)
                    for ref, pred in zip(_lowerCAmelCase				,       _lowerCAmelCase					)
                ]

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						:str			=  {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE						:List[str]			=  [
       '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechForCTC''',
       '''UniSpeechForPreTraining''',
       '''UniSpeechForSequenceClassification''',
       '''UniSpeechModel''',
       '''UniSpeechPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE						:List[str]			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




