





"""simple docstring"""




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


UpperCamelCase_									="""\
@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},
}
"""

UpperCamelCase_									="""\
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.
"""

UpperCamelCase_									="""
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     snake_case     (			self		:   Tuple							)     ->				List[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     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   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     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Tuple=0.9,			lowerCAmelCase__		:   str=3,			lowerCAmelCase__		:   Optional[Any]=0.5							)     ->				int:




          '''simple docstring'''



          if NLTK_VERSION >= version.Version('''3.6.5'''							):
                _UpperCamelCase      : Dict         =   [
                    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      : Tuple         =   [
                    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"""




import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(pad_token_id=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



