





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						:   Optional[Any]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   List[Any]			  =     '''▁'''

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

UpperCAmelCase						:   str			  =     {
    '''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						:   List[Any]			  =     {
    '''facebook/m2m100_418M''': 10_24,
}

# fmt: off
UpperCAmelCase						:   Dict			  =     {
    '''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 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	VOCAB_FILES_NAMES
				UpperCamelCase    :  Tuple					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase    :  Optional[Any]					    =	PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase    :  List[str]					    =	['''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     ,					):
									__A	:						Union[str, Any]       =				{} if sp_model_kwargs is None else sp_model_kwargs

									__A	:						int       =				language_codes
									__A	:						str       =				FAIRSEQ_LANGUAGE_CODES[language_codes]
									__A	:						Any       =				{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

									__A	:						List[str]       =				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     ,					)

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

									__A	:						int       =				len(self.encoder     )

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

									__A	:						List[Any]       =				src_lang if src_lang is not None else 'en'
									__A	:						int       =				tgt_lang
									__A	:						List[Any]       =				self.get_lang_id(self._src_lang     )
									self.set_src_lang_special_tokens(self._src_lang     )

									__A	:						Optional[Any]       =				num_madeup_words



				@property
				def 					UpperCAmelCase_		(			self     ):
									return len(self.encoder     ) + len(self.lang_token_to_id     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self._src_lang



				@src_lang.setter
				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						str       =				new_src_lang
									self.set_src_lang_special_tokens(self._src_lang     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return self.sp_model.encode(_A     ,					out_type=_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									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 					UpperCAmelCase_		(			self     ,					_A     ):
									if index in self.id_to_lang_token:
														return self.id_to_lang_token[index]
									return self.decoder.get(_A     ,					self.unk_token     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						List[str]       =				[]
									__A	:						Tuple       =				''
									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
																			__A	:						Dict       =				[]
														else:
																			current_sub_tokens.append(_A     )
									out_string += self.sp_model.decode(_A     )
									return out_string.strip()



				def 					UpperCAmelCase_		(			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     )

									__A	:						Any       =				[1] * len(self.prefix_tokens     )
									__A	:						str       =				[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 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ):
									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 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[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     ):
									__A	:						Optional[int]       =				self.__dict__.copy()
									__A	:						Optional[Any]       =				None
									return state



				def __setstate__(			self     ,					_A     ):
									__A	:						int       =				d

									# for backward compatibility
									if not hasattr(self     ,					'sp_model_kwargs'     ):
														__A	:						Optional[int]       =				{}

									__A	:						List[str]       =				load_spm(self.spm_file     ,					self.sp_model_kwargs     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ):
									__A	:						List[str]       =				Path(_A     )
									if not save_dir.is_dir():
														raise OSError(F"""{save_directory} should be a directory"""     )
									__A	:						Any       =				save_dir / (
									    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
									)
									__A	:						int       =				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:
																			__A	:						List[str]       =				self.sp_model.serialized_model_proto()
																			fi.write(_A     )

									return (str(_A     ), str(_A     ))



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = "en"     ,					_A = None     ,					_A = "ro"     ,					**_A     ,					):
									__A	:						Tuple       =				src_lang
									__A	:						Any       =				tgt_lang
									self.set_src_lang_special_tokens(self.src_lang     )
									return super().prepare_seqaseq_batch(_A     ,					_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					**_A     ):
									if src_lang is None or tgt_lang is None:
														raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'     )
									__A	:						Optional[Any]       =				src_lang
									__A	:						List[Any]       =				self(_A     ,					add_special_tokens=_A     ,					**_A     )
									__A	:						Tuple       =				self.get_lang_id(_A     )
									__A	:						int       =				tgt_lang_id
									return inputs



				def 					UpperCAmelCase_		(			self     ):
									self.set_src_lang_special_tokens(self.src_lang     )



				def 					UpperCAmelCase_		(			self     ):
									self.set_tgt_lang_special_tokens(self.tgt_lang     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Dict       =				self.get_lang_token(_A     )
									__A	:						Union[str, Any]       =				self.lang_token_to_id[lang_token]
									__A	:						Dict       =				[self.cur_lang_id]
									__A	:						Optional[Any]       =				[self.eos_token_id]



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Union[str, Any]       =				self.get_lang_token(_A     )
									__A	:						List[str]       =				self.lang_token_to_id[lang_token]
									__A	:						Dict       =				[self.cur_lang_id]
									__A	:						List[str]       =				[self.eos_token_id]



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return self.lang_code_to_token[lang]





				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Any       =				self.get_lang_token(_A     )
									return self.lang_token_to_id[lang_token]







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      sentencepiece.SentencePieceProcessor:
					__A	:						Optional[int]       =				sentencepiece.SentencePieceProcessor(**a  )
					spm.Load(str(a  )  )
					return spm







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Union[Dict, List]:
					with open(a					,						'r'  ) as f:
										return json.load(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      None:
					with open(a					,						'w'  ) as f:
										json.dump(a					,						a					,						indent=2  )











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




