




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase__					:  Dict      							=				version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
   from nltk import word_tokenize


lowerCamelCase__					:  List[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[int]      							=				'\\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__					:  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 			lowerCamelCase_				( datasets.Metric   ):


 '''simple docstring'''







 def  lowerCAmelCase_    (				self    :						List[Any]		):
       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  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						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  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						Dict=0.9							,							_lowerCAmelCase    :						Tuple=3							,							_lowerCAmelCase    :						List[str]=0.5		):
       if NLTK_VERSION >= version.Version('3.6.5'		):
             SCREAMING_SNAKE_CASE_      		=     [
                 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:
             SCREAMING_SNAKE_CASE_      		=     [
                 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_torch_available


lowerCamelCase__					:  Optional[int]      							=				{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Any      							=				['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

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