




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


__lowerCAmelCase	:       Tuple     =	logging.get_logger(__name__)

__lowerCAmelCase	:       Optional[Any]     =	{'vocab_file': 'spiece.model'}

__lowerCAmelCase	:       Union[str, Any]     =	{
    '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
__lowerCAmelCase	:       Union[str, Any]     =	{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

__lowerCAmelCase	:       Union[str, Any]     =	'▁'




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							int											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							int											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      ["""input_ids""", """attention_mask"""]





				def __init__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Any="</s>"      ,   __lowerCamelCase       :  Any="<unk>"      ,   __lowerCamelCase       :  Optional[int]="<pad>"      ,   __lowerCamelCase       :  List[Any]=1_00      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Optional[Dict[str, Any]] = None      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   **__lowerCamelCase       :  Optional[int]      ,   )      ->  None:
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
										a					=    [f"""<extra_id_{i}>""" for i in range(__lowerCamelCase							)]
							elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra_id special tokens
										a					=    len(set(filter(lambda __lowerCamelCase							: bool("extra_id" in str(__lowerCamelCase							)							)      ,   __lowerCamelCase							)							)							)
										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"							)

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

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

							a					=    vocab_file
							a					=    extra_ids

							a					=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(__lowerCamelCase							)





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  str							)      ->  Optional[Any]:
							if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
										a					=    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."      ,   __lowerCamelCase      ,   )

							return max_model_length





				@property
				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[Any]:
							return self.sp_model.get_piece_size() + self._extra_ids





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							a					=    {self.convert_ids_to_tokens(__lowerCamelCase							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None      ,   __lowerCamelCase       :  bool = False							)      ->  List[int]:
							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__lowerCamelCase      ,   token_ids_a=__lowerCamelCase      ,   already_has_special_tokens=__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Any:
							return list(
							    set(filter(lambda __lowerCamelCase							: bool(re.search(r"<extra_id_\d+>"      ,   __lowerCamelCase							)							) is not None      ,   self.additional_special_tokens							)							)							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[Any]:
							return [self._convert_token_to_id(__lowerCamelCase							) for token in self.get_sentinel_tokens()]





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[int]							)      ->  List[int]:
							if len(__lowerCamelCase							) > 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       :  Optional[Any]      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  List[int]:
							a					=    [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       :  Any      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  List[int]:
							a					=    self._add_eos_if_not_present(__lowerCamelCase							)
							if token_ids_a is None:
										return token_ids_a
							else:
										a					=    self._add_eos_if_not_present(__lowerCamelCase							)
										return token_ids_a + token_ids_a





				def __getstate__(   self       :  List[Any]							)      ->  int:
							a					=    self.__dict__.copy()
							a					=    None
							return state





				def __setstate__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Union[str, Any]:
							a					=    d

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

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





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  "TextInput"      ,   **__lowerCamelCase       :  Dict							)      ->  List[str]:
							# 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:
										a					=    SPIECE_UNDERLINE + text.replace(__lowerCamelCase      ,   " "							)
							return super().tokenize(__lowerCamelCase      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  int      ,   **__lowerCamelCase       :  str							)      ->  Dict:
							if not self.legacy:
										a					=    text.startswith(__lowerCamelCase							)
										if is_first:
													a					=    text[1:]

							a					=    self.sp_model.encode(__lowerCamelCase      ,   out_type=__lowerCamelCase							)

							if not self.legacy and not is_first and not text.startswith(" "							) and tokens[0].startswith(__lowerCamelCase							):
										a					=    ([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
							return tokens





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  Tuple:
							if token.startswith("<extra_id_"							):
										a					=    re.match(r"<extra_id_(\d+)>"      ,   __lowerCamelCase							)
										a					=    int(match.group(1							)							)
										return self.vocab_size - num - 1
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Dict:
							if index < self.sp_model.get_piece_size():
										a					=    self.sp_model.IdToPiece(__lowerCamelCase							)
							else:
										a					=    f"""<extra_id_{self.vocab_size - 1 - index}>"""
							return token





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Any							)      ->  List[Any]:
							a					=    []
							a					=    ""
							a					=    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(__lowerCamelCase							) + token
													a					=    True
													a					=    []
										else:
													current_sub_tokens.append(__lowerCamelCase							)
													a					=    False
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a					=    os.path.join(
							    __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.vocab_file							):
										copyfile(self.vocab_file      ,   __lowerCamelCase							)
							elif not os.path.isfile(self.vocab_file							):
										with open(__lowerCamelCase      ,   "wb"							) as fi:
													a					=    self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)












from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


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

__lowerCAmelCase	:       str     =	'\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

__lowerCAmelCase	:       List[Any]     =	'\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION     )
class 					snake_case__							(datasets.Metric     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  MetricInfo:
							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 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[List[List[str]]]      ,   __lowerCamelCase       :  List[List[str]]      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  int = 4      ,   )      ->  Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=__lowerCamelCase      ,   hypotheses=__lowerCamelCase      ,   min_len=__lowerCamelCase      ,   max_len=__lowerCamelCase							)
							}






