"""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


__lowerCAmelCase					: str        ="""\
@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\",
}
"""

__lowerCAmelCase					: str        ="""\
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
"""

__lowerCAmelCase					: Optional[int]        ="""
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 				_A		( datasets.Metric			):

      def        A__ (		self		):







            """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	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



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

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

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

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

            return result




"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)


__lowerCAmelCase					: str        ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
__lowerCAmelCase					: Optional[int]        ={
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

__lowerCAmelCase					: Optional[int]        ={
    """facebook/bart-base""": 1_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[int]													=			VOCAB_FILES_NAMES
      snake_case__    :     Tuple													=			PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :     Tuple													=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :     Optional[Any]													=			['input_ids', 'attention_mask']
      snake_case__    :     Dict													=			BartTokenizer

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase="replace"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="<unk>"	, __lowerCAmelCase="<pad>"	, __lowerCAmelCase="<mask>"	, __lowerCAmelCase=False	, __lowerCAmelCase=True	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(
                __lowerCAmelCase	, __lowerCAmelCase	, tokenizer_file=__lowerCAmelCase	, errors=__lowerCAmelCase	, bos_token=__lowerCAmelCase	, eos_token=__lowerCAmelCase	, sep_token=__lowerCAmelCase	, cls_token=__lowerCAmelCase	, unk_token=__lowerCAmelCase	, pad_token=__lowerCAmelCase	, mask_token=__lowerCAmelCase	, add_prefix_space=__lowerCAmelCase	, trim_offsets=__lowerCAmelCase	, **__lowerCAmelCase	, )

            lowercase														=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
            if pre_tok_state.get("""add_prefix_space"""	, __lowerCAmelCase		) != add_prefix_space:
                  lowercase														=			getattr(__lowerCAmelCase	, pre_tok_state.pop("""type"""		)		)
                  lowercase														=			add_prefix_space
                  lowercase														=			pre_tok_class(**__lowerCAmelCase		)

            lowercase														=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowercase														=			"""post_processor"""
            lowercase														=			getattr(self.backend_tokenizer	, __lowerCAmelCase	, __lowerCAmelCase		)
            if tokenizer_component_instance:
                  lowercase														=			json.loads(tokenizer_component_instance.__getstate__()		)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowercase														=			tuple(state["""sep"""]		)
                  if "cls" in state:
                        lowercase														=			tuple(state["""cls"""]		)

                  lowercase														=			False

                  if state.get("""add_prefix_space"""	, __lowerCAmelCase		) != add_prefix_space:
                        lowercase														=			add_prefix_space
                        lowercase														=			True

                  if state.get("""trim_offsets"""	, __lowerCAmelCase		) != trim_offsets:
                        lowercase														=			trim_offsets
                        lowercase														=			True

                  if changes_to_apply:
                        lowercase														=			getattr(__lowerCAmelCase	, state.pop("""type"""		)		)
                        lowercase														=			component_class(**__lowerCAmelCase		)
                        setattr(self.backend_tokenizer	, __lowerCAmelCase	, __lowerCAmelCase		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""		)
                  return None
            return str(self._mask_token		)

      @mask_token.setter
      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			AddedToken(__lowerCAmelCase	, lstrip=__lowerCAmelCase	, rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase	, __lowerCAmelCase		) else value
            lowercase														=			value

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.get("""is_split_into_words"""	, __lowerCAmelCase		)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""		)

            return super()._batch_encode_plus(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.get("""is_split_into_words"""	, __lowerCAmelCase		)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""		)

            return super()._encode_plus(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			self._tokenizer.model.save(__lowerCAmelCase	, name=__lowerCAmelCase		)
            return tuple(__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=None		):







            """simple docstring"""



            lowercase														=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			[self.sep_token_id]
            lowercase														=			[self.cls_token_id]

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



