






'''simple docstring'''
import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_					=	'''\
@inproceedings{lin-2004-rouge,
    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
    author = "Lin, Chin-Yew",
    booktitle = "Text Summarization Branches Out",
    month = jul,
    year = "2004",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W04-1013",
    pages = "74--81",
}
'''

a_					=	'''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''

a_					=	'''
Calculates average rouge scores for a list of hypotheses and 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.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
        `"rougeL"`: Longest common subsequence based scoring.
        `"rougeLSum"`: rougeLsum splits text using `"\n"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric(\'rouge\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
    >>> print(results["rouge1"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results["rouge1"].mid.fmeasure)
    1.0
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,			_KWARGS_DESCRIPTION			)
class        __SCREAMING_SNAKE_CASE (     datasets.Metric			):




 def        __magic_name__		( self		:							Optional[int] )		->  List[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/google-research/google-research/tree/master/rouge''']   , reference_urls=[
               '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
               '''https://github.com/google-research/google-research/tree/master/rouge''',
           ]   , )




 def        __magic_name__		( self		:							Any   , __lowercase		:							int   , __lowercase		:							int   , __lowercase		:							str=None   , __lowercase		:							int=True   , __lowercase		:							List[str]=False )		->  int:
       if rouge_types is None:
             SCREAMING_SNAKE_CASE__       :			List[str]													=["rouge1", "rouge2", "rougeL", "rougeLsum"]

       SCREAMING_SNAKE_CASE__       :			Dict													=rouge_scorer.RougeScorer(rouge_types=_SCREAMING_SNAKE_CASE   , use_stemmer=_SCREAMING_SNAKE_CASE )
       if use_aggregator:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=scoring.BootstrapAggregator()
       else:
             SCREAMING_SNAKE_CASE__       :			Any													=[]

       for ref, pred in zip(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE ):
             SCREAMING_SNAKE_CASE__       :			Tuple													=scorer.score(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE )
             if use_aggregator:
                   aggregator.add_scores(_SCREAMING_SNAKE_CASE )
             else:
                   scores.append(_SCREAMING_SNAKE_CASE )

       if use_aggregator:
             SCREAMING_SNAKE_CASE__       :			Tuple													=aggregator.aggregate()
       else:
             SCREAMING_SNAKE_CASE__       :			int													={}
             for key in scores[0]:
                   SCREAMING_SNAKE_CASE__       :			Dict													=[score[key] for score in scores]

       return result







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MegatronBertForCausalLM',
      'MegatronBertForMaskedLM',
      'MegatronBertForMultipleChoice',
      'MegatronBertForNextSentencePrediction',
      'MegatronBertForPreTraining',
      'MegatronBertForQuestionAnswering',
      'MegatronBertForSequenceClassification',
      'MegatronBertForTokenClassification',
      'MegatronBertModel',
      'MegatronBertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)