'''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


snake_case	=      '''\\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'''

snake_case	=      '''\\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'''

snake_case	=      '''\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 							SCREAMING_SNAKE_CASE (					datasets.Metric			):
  """simple docstring"""







  def 	a (	self :       Union[str, Any]       ):


        """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 	a (	self :       Tuple       ,       __lowerCAmelCase :       List[Any]       ,       __lowerCAmelCase :       List[str]       ,       __lowerCAmelCase :       Any=None       ,       __lowerCAmelCase :       Tuple=True       ,       __lowerCAmelCase :       List[str]=False       ):


        """simple docstring"""



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

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

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

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

        return result













'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      SCREAMING_SNAKE_CASE							=						None


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							=						{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE							=						{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

SCREAMING_SNAKE_CASE							=						'▁'

# Segments (not really needed)
SCREAMING_SNAKE_CASE							=						0
SCREAMING_SNAKE_CASE							=						1
SCREAMING_SNAKE_CASE							=						2
SCREAMING_SNAKE_CASE							=						3
SCREAMING_SNAKE_CASE							=						4
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      VOCAB_FILES_NAMES
      UpperCamelCase_             =      PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_             =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_             =      '''left'''
      UpperCamelCase_             =      XLNetTokenizer






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		str=False  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		int="<s>"  ,   UpperCAmelCase      :		Optional[int]="</s>"  ,   UpperCAmelCase      :		str="<unk>"  ,   UpperCAmelCase      :		Optional[Any]="<sep>"  ,   UpperCAmelCase      :		Optional[int]="<pad>"  ,   UpperCAmelCase      :		Optional[Any]="<cls>"  ,   UpperCAmelCase      :		Dict="<mask>"  ,   UpperCAmelCase      :		int=["<eop>", "<eod>"]  ,   **UpperCAmelCase      :		List[Any]  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=AddedToken(UpperCAmelCase  ,   lstrip=UpperCAmelCase  ,   rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) else mask_token

       super().__init__(
           vocab_file=UpperCAmelCase  ,   tokenizer_file=UpperCAmelCase  ,   do_lower_case=UpperCAmelCase  ,   remove_space=UpperCAmelCase  ,   keep_accents=UpperCAmelCase  ,   bos_token=UpperCAmelCase  ,   eos_token=UpperCAmelCase  ,   unk_token=UpperCAmelCase  ,   sep_token=UpperCAmelCase  ,   pad_token=UpperCAmelCase  ,   cls_token=UpperCAmelCase  ,   mask_token=UpperCAmelCase  ,   additional_special_tokens=UpperCAmelCase  ,   **UpperCAmelCase  ,   )

       lowercase       : Tuple						=3
       lowercase       : Union[str, Any]						=do_lower_case
       lowercase       : Any						=remove_space
       lowercase       : int						=keep_accents
       lowercase       : int						=vocab_file
       lowercase       : Union[str, Any]						=False if not self.vocab_file else True






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[self.sep_token_id]
       lowercase       : Optional[Any]						=[self.cls_token_id]
       if token_ids_a is None:
        return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=[self.sep_token_id]
       lowercase       : Union[str, Any]						=[2]

       if token_ids_a is None:
        return len(token_ids_a + sep		) * [0] + cls_segment_id
       return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Optional[str] = None		)	->	Tuple[str]:


       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''		)

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase       : Dict						=os.path.join(
           UpperCAmelCase  ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase		):
        copyfile(self.vocab_file  ,   UpperCAmelCase		)

       return (out_vocab_file,)



