

'''simple docstring'''

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowercase__         =      "\\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"

lowercase__         =      "\\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"

lowercase__         =      "\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							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Dict	)						->     str:
      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			:					Optional[int]					,      lowercase_			:					Any	)						->     Optional[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			:					List[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					List[Any]					,      lowercase_			:					List[Any]=0.9					,      lowercase_			:					Tuple=3					,      lowercase_			:					Optional[Any]=0.5	)						->     Dict:
      if NLTK_VERSION >= version.Version('3.6.5'	):
       UpperCAmelCase	:     Union[str, Any]						 =			[
           meteor_score.single_meteor_score(
               word_tokenize(lowercase_	)					,      word_tokenize(lowercase_	)					,      alpha=lowercase_					,      beta=lowercase_					,      gamma=lowercase_	)
           for ref, pred in zip(lowercase_					,      lowercase_	)
       ]
      else:
       UpperCAmelCase	:     Dict						 =			[
           meteor_score.single_meteor_score(lowercase_					,      lowercase_					,      alpha=lowercase_					,      beta=lowercase_					,      gamma=lowercase_	)
           for ref, pred in zip(lowercase_					,      lowercase_	)
       ]

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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase__         =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMForMultipleChoice",
     "XLMForQuestionAnswering",
     "XLMForQuestionAnsweringSimple",
     "XLMForSequenceClassification",
     "XLMForTokenClassification",
     "XLMModel",
     "XLMPreTrainedModel",
     "XLMWithLMHeadModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLMForMultipleChoice",
     "TFXLMForQuestionAnsweringSimple",
     "TFXLMForSequenceClassification",
     "TFXLMForTokenClassification",
     "TFXLMMainLayer",
     "TFXLMModel",
     "TFXLMPreTrainedModel",
     "TFXLMWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

 lowercase__         =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




