
"""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': 10_24,
}

# 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 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Dict				 =						VOCAB_FILES_NAMES
			UpperCAmelCase_  :List[Any]				 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_  :Optional[int]				 =						PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_  :Tuple				 =						["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_    :Optional[int]			     =  {} if sp_model_kwargs is None else sp_model_kwargs

						lowerCAmelCase_    :List[str]			     =  language_codes
						lowerCAmelCase_    :Dict			     =  FAIRSEQ_LANGUAGE_CODES[language_codes]
						lowerCAmelCase_    :List[str]			     =  {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

						lowerCAmelCase_    :List[Any]			     =  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_    :int			     =  vocab_file
						lowerCAmelCase_    :Tuple			     =  load_json(__A       )
						lowerCAmelCase_    :Tuple			     =  {v: k for k, v in self.encoder.items()}
						lowerCAmelCase_    :List[str]			     =  spm_file
						lowerCAmelCase_    :Optional[Any]			     =  load_spm(__A       ,   self.sp_model_kwargs       )

						lowerCAmelCase_    :str			     =  len(self.encoder       )

						lowerCAmelCase_    :Union[str, Any]			     =  {
						    self.get_lang_token(__A       ): self.encoder_size + i for i, lang_code in enumerate(__A       )
						}
						lowerCAmelCase_    :List[str]			     =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(__A       )}
						lowerCAmelCase_    :Dict			     =  {v: k for k, v in self.lang_token_to_id.items()}

						lowerCAmelCase_    :List[str]			     =  src_lang if src_lang is not None else """en"""
						lowerCAmelCase_    :int			     =  tgt_lang
						lowerCAmelCase_    :Optional[Any]			     =  self.get_lang_id(self._src_lang       )
						self.set_src_lang_special_tokens(self._src_lang       )

						lowerCAmelCase_    :Optional[int]			     =  num_madeup_words






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






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






			@src_lang.setter
			def  __lowerCAmelCase (					self       ,   __A       )			->     None:
						lowerCAmelCase_    :Dict			     =  new_src_lang
						self.set_src_lang_special_tokens(self._src_lang       )






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






			def  __lowerCAmelCase (					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  __lowerCAmelCase (					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  __lowerCAmelCase (					self       ,   __A       )			->     Optional[int]:
						lowerCAmelCase_    :str			     =  []
						lowerCAmelCase_    :int			     =  """"""
						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_    :Dict			     =  []
									else:
												current_sub_tokens.append(__A       )
						out_string += self.sp_model.decode(__A       )
						return out_string.strip()






			def  __lowerCAmelCase (					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_    :Any			     =  [1] * len(self.prefix_tokens       )
						lowerCAmelCase_    :Union[str, Any]			     =  [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  __lowerCAmelCase (					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  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :int			     =  {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_    :List[Any]			     =  self.__dict__.copy()
						lowerCAmelCase_    :List[str]			     =  None
						return state






			def __setstate__(					self       ,   __A       )			->     None:
						lowerCAmelCase_    :List[Any]			     =  d

						# for backward compatibility
						if not hasattr(self       ,   """sp_model_kwargs"""       ):
									lowerCAmelCase_    :List[Any]			     =  {}

						lowerCAmelCase_    :str			     =  load_spm(self.spm_file       ,   self.sp_model_kwargs       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       )			->     Tuple[str]:
						lowerCAmelCase_    :Optional[Any]			     =  Path(__A       )
						if not save_dir.is_dir():
									raise OSError(f"""{save_directory} should be a directory"""       )
						lowerCAmelCase_    :Any			     =  save_dir / (
						    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
						)
						lowerCAmelCase_    :Tuple			     =  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_    :Union[str, Any]			     =  self.sp_model.serialized_model_proto()
												fi.write(__A       )

						return (str(__A       ), str(__A       ))






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = "en"       ,   __A = None       ,   __A = "ro"       ,   **__A       ,   )			->     BatchEncoding:
						lowerCAmelCase_    :Union[str, Any]			     =  src_lang
						lowerCAmelCase_    :List[Any]			     =  tgt_lang
						self.set_src_lang_special_tokens(self.src_lang       )
						return super().prepare_seqaseq_batch(__A       ,   __A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   **__A       )			->     Tuple:
						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_    :Dict			     =  src_lang
						lowerCAmelCase_    :Optional[int]			     =  self(__A       ,   add_special_tokens=__A       ,   **__A       )
						lowerCAmelCase_    :Union[str, Any]			     =  self.get_lang_id(__A       )
						lowerCAmelCase_    :Union[str, Any]			     =  tgt_lang_id
						return inputs






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






			def  __lowerCAmelCase (					self       )			->     Dict:
						self.set_tgt_lang_special_tokens(self.tgt_lang       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     None:
						lowerCAmelCase_    :str			     =  self.get_lang_token(__A       )
						lowerCAmelCase_    :Any			     =  self.lang_token_to_id[lang_token]
						lowerCAmelCase_    :Optional[Any]			     =  [self.cur_lang_id]
						lowerCAmelCase_    :List[str]			     =  [self.eos_token_id]






			def  __lowerCAmelCase (					self       ,   __A       )			->     None:
						lowerCAmelCase_    :str			     =  self.get_lang_token(__A       )
						lowerCAmelCase_    :int			     =  self.lang_token_to_id[lang_token]
						lowerCAmelCase_    :str			     =  [self.cur_lang_id]
						lowerCAmelCase_    :Union[str, Any]			     =  [self.eos_token_id]






			def  __lowerCAmelCase (					self       ,   __A       )			->     str:
						return self.lang_code_to_token[lang]






			def  __lowerCAmelCase (					self       ,   __A       )			->     int:
						lowerCAmelCase_    :Optional[Any]			     =  self.get_lang_token(__A       )
						return self.lang_token_to_id[lang_token]






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


			'''simple docstring'''





			lowerCAmelCase_    :Any			     =  sentencepiece.SentencePieceProcessor(**lowercase__		)
			spm.Load(str(lowercase__		)		)
			return spm






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


			'''simple docstring'''





			with open(lowercase__      ,     """r"""		) as f:
						return json.load(lowercase__		)






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  str		)  ->				None:


			'''simple docstring'''





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








import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

SCREAMING_SNAKE_CASE__   :			int												=    "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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






		def  __A       (		self		:							Dict  )     ->							Tuple:
						if version.parse(scb.__version__  ) < version.parse('''1.4.12'''  ):
										raise ImportWarning(
										    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
										    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''  )
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''string'''							,			id='''sequence'''  ),
						            '''references''': datasets.Sequence(datasets.Value('''string'''							,			id='''sequence'''  )							,			id='''references'''  ),
						        }  )							,			codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']							,			reference_urls=[
						        '''https://github.com/m-popovic/chrF''',
						    ]							,			)







		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.CHAR_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.WORD_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.BETA							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			)     ->							Optional[Any]:
						__lowerCamelCase    					=	len(references[0]  )
						if any(len(SCREAMING_SNAKE_CASE__  ) != references_per_prediction for refs in references  ):
										raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
						__lowerCamelCase    					=	[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__  )]

						__lowerCamelCase    					=	CHRF(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						return {
						    "score": output.score,
						    "char_order": output.char_order,
						    "word_order": output.word_order,
						    "beta": output.beta,
						}



