





'''simple docstring'''

from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCAmelCase     		=   '\\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'

UpperCAmelCase     		=   '\\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'

UpperCAmelCase     		=   '\\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     __snake_case   ( self				)     ->     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     __snake_case   ( self     ,	A_     ,	A_     ,	A_ = 1     ,	A_ = 4     ,	)     ->     Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=A_     ,	hypotheses=A_     ,	min_len=A_     ,	max_len=A_				)
							}




'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   '▁'

UpperCAmelCase     		=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase     		=   {
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

UpperCAmelCase     		=   {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
UpperCAmelCase     		=   {
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		VOCAB_FILES_NAMES
						UpperCAmelCase							:   Optional[Any]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:   Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:   int									=		["input_ids", "attention_mask"]

						UpperCAmelCase							:   List[int]									=		[]
						UpperCAmelCase							:   List[int]									=		[]


						def __init__( self     ,	A_     ,	A_     ,	A_=None     ,	A_=None     ,	A_="<s>"     ,	A_="</s>"     ,	A_="</s>"     ,	A_="<pad>"     ,	A_="<unk>"     ,	A_="m2m100"     ,	A_ = None     ,	A_=8     ,	**A_     ,	)     ->     None:
							lowerCAmelCase   =     {} if sp_model_kwargs is None else sp_model_kwargs

							lowerCAmelCase   =     language_codes
							lowerCAmelCase   =     FAIRSEQ_LANGUAGE_CODES[language_codes]
							lowerCAmelCase   =     {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}

							lowerCAmelCase   =     kwargs.get("""additional_special_tokens"""     ,	[]				)
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(A_				)
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(A_				) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=A_     ,	tgt_lang=A_     ,	bos_token=A_     ,	eos_token=A_     ,	sep_token=A_     ,	unk_token=A_     ,	pad_token=A_     ,	language_codes=A_     ,	sp_model_kwargs=self.sp_model_kwargs     ,	num_madeup_words=A_     ,	**A_     ,	)

							lowerCAmelCase   =     vocab_file
							lowerCAmelCase   =     load_json(A_				)
							lowerCAmelCase   =     {v: k for k, v in self.encoder.items()}
							lowerCAmelCase   =     spm_file
							lowerCAmelCase   =     load_spm(A_     ,	self.sp_model_kwargs				)

							lowerCAmelCase   =     len(self.encoder				)

							lowerCAmelCase   =     {
							    self.get_lang_token(A_				): self.encoder_size + i for i, lang_code in enumerate(A_				)
							}
							lowerCAmelCase   =     {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_				)}
							lowerCAmelCase   =     {v: k for k, v in self.lang_token_to_id.items()}

							lowerCAmelCase   =     src_lang if src_lang is not None else """en"""
							lowerCAmelCase   =     tgt_lang
							lowerCAmelCase   =     self.get_lang_id(self._src_lang				)
							self.set_src_lang_special_tokens(self._src_lang				)

							lowerCAmelCase   =     num_madeup_words


						@property
						def     __snake_case   ( self				)     ->     int:
							return len(self.encoder				) + len(self.lang_token_to_id				)


						@property
						def     __snake_case   ( self				)     ->     str:
							return self._src_lang


						@src_lang.setter
						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     new_src_lang
							self.set_src_lang_special_tokens(self._src_lang				)


						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							return self.sp_model.encode(A_     ,	out_type=A_				)


						def     __snake_case   ( self     ,	A_				)     ->     Any:
							if token in self.lang_token_to_id:
								return self.lang_token_to_id[token]
							return self.encoder.get(A_     ,	self.encoder[self.unk_token]				)


						def     __snake_case   ( self     ,	A_				)     ->     str:
							if index in self.id_to_lang_token:
								return self.id_to_lang_token[index]
							return self.decoder.get(A_     ,	self.unk_token				)


						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							lowerCAmelCase   =     []
							lowerCAmelCase   =     """"""
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									out_string += self.sp_model.decode(A_				) + token
									lowerCAmelCase   =     []
								else:
									current_sub_tokens.append(A_				)
							out_string += self.sp_model.decode(A_				)
							return out_string.strip()


						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = False				)     ->     List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_     ,	token_ids_a=A_     ,	already_has_special_tokens=A_				)

							lowerCAmelCase   =     [1] * len(self.prefix_tokens				)
							lowerCAmelCase   =     [1] * len(self.suffix_tokens				)
							if token_ids_a is None:
								return prefix_ones + ([0] * len(A_				)) + suffix_ones
							return prefix_ones + ([0] * len(A_				)) + ([0] * len(A_				)) + suffix_ones


						def     __snake_case   ( self     ,	A_     ,	A_ = None				)     ->     List[int]:
							if token_ids_a is None:
								return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     {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				)     ->     Dict:
							lowerCAmelCase   =     self.__dict__.copy()
							lowerCAmelCase   =     None
							return state


						def __setstate__( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     d

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

							lowerCAmelCase   =     load_spm(self.spm_file     ,	self.sp_model_kwargs				)


						def     __snake_case   ( self     ,	A_     ,	A_ = None				)     ->     Tuple[str]:
							lowerCAmelCase   =     Path(A_				)
							if not save_dir.is_dir():
								raise OSError(f'{save_directory} should be a directory'				)
							lowerCAmelCase   =     save_dir / (
							    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
							)
							lowerCAmelCase   =     save_dir / (
							    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
							)

							save_json(self.encoder     ,	A_				)

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

							return (str(A_				), str(A_				))


						def     __snake_case   ( self     ,	A_     ,	A_ = "en"     ,	A_ = None     ,	A_ = "ro"     ,	**A_     ,	)     ->     BatchEncoding:
							lowerCAmelCase   =     src_lang
							lowerCAmelCase   =     tgt_lang
							self.set_src_lang_special_tokens(self.src_lang				)
							return super().prepare_seqaseq_batch(A_     ,	A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	**A_				)     ->     str:
							if src_lang is None or tgt_lang is None:
								raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
							lowerCAmelCase   =     src_lang
							lowerCAmelCase   =     self(A_     ,	add_special_tokens=A_     ,	**A_				)
							lowerCAmelCase   =     self.get_lang_id(A_				)
							lowerCAmelCase   =     tgt_lang_id
							return inputs


						def     __snake_case   ( self				)     ->     Any:
							self.set_src_lang_special_tokens(self.src_lang				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							self.set_tgt_lang_special_tokens(self.tgt_lang				)


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self.get_lang_token(A_				)
							lowerCAmelCase   =     self.lang_token_to_id[lang_token]
							lowerCAmelCase   =     [self.cur_lang_id]
							lowerCAmelCase   =     [self.eos_token_id]


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self.get_lang_token(A_				)
							lowerCAmelCase   =     self.lang_token_to_id[lang_token]
							lowerCAmelCase   =     [self.cur_lang_id]
							lowerCAmelCase   =     [self.eos_token_id]


						def     __snake_case   ( self     ,	A_				)     ->     str:
							return self.lang_code_to_token[lang]




						def     __snake_case   ( self     ,	A_				)     ->     int:
							lowerCAmelCase   =     self.get_lang_token(A_				)
							return self.lang_token_to_id[lang_token]







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Dict[str, Any]							)      ->						sentencepiece.SentencePieceProcessor:







	"""simple docstring"""
	lowerCAmelCase   =     sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE							)
	spm.Load(str(_SCREAMING_SNAKE_CASE							)							)
	return spm






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Union[Dict, List]:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""r"""							) as f:
		return json.load(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as f:
		json.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		indent=2							)