


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


__snake_case   							=						'''\
@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",
}
'''

__snake_case   							=						'''\
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
'''

__snake_case   							=						'''
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 	__lowerCamelCase			(datasets.Metric						):







       def 		snake_case_	(			self: 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/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 		snake_case_	(			self: Any,A_: Any,A_: Union[str, Any],A_: Optional[int]=None,A_: Optional[Any]=True,A_: Tuple=False					):






            '''simple docstring'''




            if rouge_types is None:
                 __UpperCamelCase =			['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

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

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

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

            return result






from typing import TYPE_CHECKING

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


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

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


