





"""simple docstring"""






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


A			      =	'''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

A			      =	'''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

A			      =	'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''




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







					'''simple docstring'''




					def 			_lowerCamelCase		(    self  ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION						,      citation=_CITATION						,      inputs_description=_KWARGS_DESCRIPTION						,      features=datasets.Features(
									        {
									            '''predictions''': datasets.Sequence(datasets.Value('''string'''						,      id='''token'''  )						,      id='''sequence'''  ),
									            '''references''': datasets.Sequence(
									                datasets.Sequence(datasets.Value('''string'''						,      id='''token'''  )						,      id='''sequence'''  )						,      id='''references'''  ),
									        }  )						,      )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase = 1						,      _UpperCAmelCase = 4						,      ):
									return {
									    "google_bleu": gleu_score.corpus_gleu(
									        list_of_references=_UpperCAmelCase						,      hypotheses=_UpperCAmelCase						,      min_len=_UpperCAmelCase						,      max_len=_UpperCAmelCase  )
									}






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{"vocab_file": "spiece.model"}

UpperCAmelCase_           =	{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_           =	{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

UpperCAmelCase_           =	"▁"




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   VOCAB_FILES_NAMES
     __A	:  Union[str, Any]							  =   PRETRAINED_VOCAB_FILES_MAP
     __A	:  Any							  =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Tuple							  =   ["""input_ids""", """attention_mask"""]





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase="</s>"				,							_UpperCamelCase="<unk>"				,							_UpperCamelCase="<pad>"				,							_UpperCamelCase=100				,							_UpperCamelCase=None				,							_UpperCamelCase = None				,							_UpperCamelCase=True				,							**_UpperCamelCase				,							):
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
           _UpperCAmelCase           =   [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase					)]
        elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           _UpperCAmelCase           =   len(set(filter(lambda _UpperCamelCase					: bool('''extra_id''' in str(_UpperCamelCase					)					)				,							_UpperCamelCase					)					)					)
           if extra_tokens != extra_ids:
              raise ValueError(
                  f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                  ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                  ''' tokens'''					)
        if legacy:
           logger.warning_once(
               f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
               ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''					)

        _UpperCAmelCase           =   legacy
        _UpperCAmelCase           =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=_UpperCamelCase				,							unk_token=_UpperCamelCase				,							pad_token=_UpperCamelCase				,							extra_ids=_UpperCamelCase				,							additional_special_tokens=_UpperCamelCase				,							sp_model_kwargs=self.sp_model_kwargs				,							legacy=_UpperCamelCase				,							**_UpperCamelCase				,							)

        _UpperCAmelCase           =   vocab_file
        _UpperCAmelCase           =   extra_ids

        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(_UpperCamelCase					)





     @staticmethod
     def 		UpperCamelCase(     _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           _UpperCAmelCase           =   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
              return init_max_model_length
           elif init_max_model_length is None:
              warnings.warn(
                  '''This tokenizer was incorrectly instantiated with a model max length of'''
                  f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                  ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                  ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                  f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                  f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                  f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                  ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                  ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''				,							_UpperCamelCase				,							)

        return max_model_length





     @property
     def 		UpperCamelCase(     self					):
        return self.sp_model.get_piece_size() + self._extra_ids





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   {self.convert_ids_to_tokens(_UpperCamelCase					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = False					):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCamelCase				,							token_ids_a=_UpperCamelCase				,							already_has_special_tokens=_UpperCamelCase					)

        # normal case: some special tokens
        if token_ids_a is None:
           return ([0] * len(_UpperCamelCase					)) + [1]
        return ([0] * len(_UpperCamelCase					)) + [1] + ([0] * len(_UpperCamelCase					)) + [1]





     def 		UpperCamelCase(     self					):
        return list(
            set(filter(lambda _UpperCamelCase					: bool(re.search(R'''<extra_id_\d+>'''				,							_UpperCamelCase					)					) is not None				,							self.additional_special_tokens					)					)					)





     def 		UpperCamelCase(     self					):
        return [self._convert_token_to_id(_UpperCamelCase					) for token in self.get_sentinel_tokens()]





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if len(_UpperCamelCase					) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
               ''' eos tokens being added.'''					)
           return token_ids
        else:
           return token_ids + [self.eos_token_id]





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   [self.eos_token_id]

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   self._add_eos_if_not_present(_UpperCamelCase					)
        if token_ids_a is None:
           return token_ids_a
        else:
           _UpperCAmelCase           =   self._add_eos_if_not_present(_UpperCamelCase					)
           return token_ids_a + token_ids_a





     def __getstate__(     self					):
        _UpperCAmelCase           =   self.__dict__.copy()
        _UpperCAmelCase           =   None
        return state





     def __setstate__(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   d

        # for backward compatibility
        if not hasattr(self				,							'''sp_model_kwargs'''					):
           _UpperCAmelCase           =   {}

        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(self.vocab_file					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
        # the beginning of the text
        if not self.legacy:
           _UpperCAmelCase           =   SPIECE_UNDERLINE + text.replace(_UpperCamelCase				,							''' '''					)
        return super().tokenize(_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        if not self.legacy:
           _UpperCAmelCase           =   text.startswith(_UpperCamelCase					)
           if is_first:
              _UpperCAmelCase           =   text[1:]

        _UpperCAmelCase           =   self.sp_model.encode(_UpperCamelCase				,							out_type=_UpperCamelCase					)

        if not self.legacy and not is_first and not text.startswith(''' '''					) and tokens[0].startswith(_UpperCamelCase					):
           _UpperCAmelCase           =   ([tokens[0][1:]] if len(tokens[0]					) > 1 else []) + tokens[1:]
        return tokens





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if token.startswith('''<extra_id_'''					):
           _UpperCAmelCase           =   re.match(R'''<extra_id_(\d+)>'''				,							_UpperCamelCase					)
           _UpperCAmelCase           =   int(match.group(1					)					)
           return self.vocab_size - num - 1
        return self.sp_model.piece_to_id(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if index < self.sp_model.get_piece_size():
           _UpperCAmelCase           =   self.sp_model.IdToPiece(_UpperCamelCase					)
        else:
           _UpperCAmelCase           =   f'''<extra_id_{self.vocab_size - 1 - index}>'''
        return token





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   []
        _UpperCAmelCase           =   ''''''
        _UpperCAmelCase           =   False
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              if not prev_is_special:
                 out_string += " "
              out_string += self.sp_model.decode(_UpperCamelCase					) + token
              _UpperCAmelCase           =   True
              _UpperCAmelCase           =   []
           else:
              current_sub_tokens.append(_UpperCamelCase					)
              _UpperCAmelCase           =   False
        out_string += self.sp_model.decode(_UpperCamelCase					)
        return out_string.strip()





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        if not os.path.isdir(_UpperCamelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
        _UpperCAmelCase           =   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					) and os.path.isfile(self.vocab_file					):
           copyfile(self.vocab_file				,							_UpperCamelCase					)
        elif not os.path.isfile(self.vocab_file					):
           with open(_UpperCamelCase				,							'''wb'''					) as fi:
              _UpperCAmelCase           =   self.sp_model.serialized_model_proto()
              fi.write(_UpperCamelCase					)

        return (out_vocab_file,)