


'''simple docstring'''
import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case				 =	'\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

__snake_case				 =	'\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

__snake_case				 =	'\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,						_KWARGS_DESCRIPTION   )
class   lowercase      (				datasets.Metric   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							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'''				),
							        }				)       ,  codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']       ,  reference_urls=[
							        '''https://en.wikipedia.org/wiki/BLEU''',
							        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
							    ]       ,  )




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_=4       ,  UpperCamelCase_=False				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					compute_bleu(
							    reference_corpus=A__       ,  translation_corpus=A__       ,  max_order=A__       ,  smooth=A__				)
							(UpperCamelCase__)  :Optional[int]				   =					score
							return {
							    "bleu": bleu,
							    "precisions": precisions,
							    "brevity_penalty": bp,
							    "length_ratio": ratio,
							    "translation_length": translation_length,
							    "reference_length": reference_length,
							}


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {'vocab_file': 'spiece.model'}

A_			:					Optional[Any]    			=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

A_			:					Tuple    			=     {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[int]    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  str    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  List[str]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  str    				=			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:  List[int]    				=			[]






					def __init__(					self       ,			A__       ,			A__="<unk>"       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<pad>"       ,			A__="[SEP]"       ,			A__="[MASK]"       ,			A__="[CLS]"       ,			A__ = None       ,			**A__       ,			):
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else bos_token
						A__				:       int	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else eos_token
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else unk_token
						A__				:       str	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else pad_token
						A__				:       Dict	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else cls_token
						A__				:       Any	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__				:       Optional[Any]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

						A__				:       Optional[Any]	  =						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=A__       ,			eos_token=A__       ,			unk_token=A__       ,			pad_token=A__       ,			sep_token=A__       ,			mask_token=A__       ,			cls_token=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)

						A__				:       Any	  =						vocab_file

						A__				:       Optional[int]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )






					@property
					def     __A     (					self     ):
						return self.sp_model.get_piece_size()






					def     __A     (					self     ):
						A__				:       List[str]	  =						{self.convert_ids_to_tokens(A__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab






					def __getstate__(					self     ):
						A__				:       Optional[Any]	  =						self.__dict__.copy()
						A__				:       Optional[Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       Dict	  =						d

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

						A__				:       Tuple	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.piece_to_id(A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       Dict	  =						self.sp_model.IdToPiece(A__     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						[]
						A__				:       Optional[int]	  =						""""""
						A__				:       Any	  =						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(A__     ) + token
								A__				:       Union[str, Any]	  =						True
								A__				:       Union[str, Any]	  =						[]
							else:
								current_sub_tokens.append(A__     )
								A__				:       Any	  =						False
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__       ,			A__ = False       ,			A__ = None       ,			A__ = True       ,			**A__       ,			):
						A__				:       List[str]	  =						kwargs.pop("""use_source_tokenizer"""       ,			A__     )

						A__				:       Union[str, Any]	  =						self.convert_ids_to_tokens(A__       ,			skip_special_tokens=A__     )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						A__				:       Dict	  =						[]
						A__				:       List[Any]	  =						[]
						for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
								continue
							if token in self.added_tokens_encoder:
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A__     )     )
									A__				:       Optional[Any]	  =						[]
								sub_texts.append(A__     )
							else:
								current_sub_text.append(A__     )
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(A__     )     )

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
							A__				:       List[Any]	  =						re.sub(r""" (\[(MASK|SEP)\])"""       ,			r"""\1"""       ,			""" """.join(A__     )     )
						else:
							A__				:       Optional[Any]	  =						"""""".join(A__     )

						A__				:       Union[str, Any]	  =						(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
							A__				:       Union[str, Any]	  =						self.clean_up_tokenization(A__     )
							return clean_text
						else:
							return text






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

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

						return (out_vocab_file,)






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__				:       List[Any]	  =						[self.cls_token_id]
						A__				:       List[Any]	  =						[self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep






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

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






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Any	  =						[self.sep_token_id]
						A__				:       List[Any]	  =						[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     ) * [0] + len(token_ids_a + sep     ) * [1]



