

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


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

_SCREAMING_SNAKE_CASE  :						str         =    '''\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'''

_SCREAMING_SNAKE_CASE  :						List[str]         =    '''\nCalculates average rouge scores for a list of hypotheses and 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    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results[\"rouge1\"])\n    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))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n'''






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






   """simple docstring"""


   def    lowercase_	(							self		:	int      )	->							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/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    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	int=None			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	Union[str, Any]=False      )	->							Any:

        if rouge_types is None:
             SCREAMING_SNAKE_CASE__					=					["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

        SCREAMING_SNAKE_CASE__					=					rouge_scorer.RougeScorer(rouge_types=_lowercase			,     use_stemmer=_lowercase      )
        if use_aggregator:
             SCREAMING_SNAKE_CASE__					=					scoring.BootstrapAggregator()
        else:
             SCREAMING_SNAKE_CASE__					=					[]

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

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

        return result





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


