





"""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


a		:					Dict	      =     logging.get_logger(__name__)

a		:					int	      =     '''▁'''

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

a		:					Optional[int]	      =     {
    '''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''',
    },
}

a		:					str	      =     {
    '''facebook/m2m100_418M''': 1024,
}

# fmt: off
a		:					Union[str, Any]	      =     {
    '''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 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   int    =VOCAB_FILES_NAMES
							lowerCamelCase  :   Any    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase  :   Optional[Any]    =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase  :   Tuple    =["""input_ids""", """attention_mask"""]

							lowerCamelCase  :   Optional[int]    =[]
							lowerCamelCase  :   str    =[]






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

								a :						Optional[int]      =							language_codes
								a :						Dict      =							FAIRSEQ_LANGUAGE_CODES[language_codes]
								a :						Optional[int]      =							{lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

								a :						Dict      =							kwargs.get("additional_special_tokens" ,   [] )
								kwargs["additional_special_tokens"] += [
								    self.get_lang_token(lowerCAmelCase__ )
								    for lang_code in fairseq_language_code
								    if self.get_lang_token(lowerCAmelCase__ ) not in kwargs["additional_special_tokens"]
								]

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

								a :						Union[str, Any]      =							vocab_file
								a :						Optional[int]      =							load_json(lowerCAmelCase__ )
								a :						List[str]      =							{v: k for k, v in self.encoder.items()}
								a :						List[str]      =							spm_file
								a :						Union[str, Any]      =							load_spm(lowerCAmelCase__ ,   self.sp_model_kwargs )

								a :						Optional[int]      =							len(self.encoder )

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

								a :						List[str]      =							src_lang if src_lang is not None else 'en'
								a :						Dict      =							tgt_lang
								a :						List[str]      =							self.get_lang_id(self._src_lang )
								self.set_src_lang_special_tokens(self._src_lang )

								a :						List[str]      =							num_madeup_words






							@property
							def        __a  (  self )      ->					List[str]:
								return len(self.encoder ) + len(self.lang_token_to_id )






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






							@src_lang.setter
							def        __a  (  self ,   lowerCAmelCase__ )      ->					Tuple:
								a :						Union[str, Any]      =							new_src_lang
								self.set_src_lang_special_tokens(self._src_lang )






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






							def        __a  (  self ,   lowerCAmelCase__ )      ->					str:
								if token in self.lang_token_to_id:
									return self.lang_token_to_id[token]
								return self.encoder.get(lowerCAmelCase__ ,   self.encoder[self.unk_token] )






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






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Tuple:
								a :						str      =							[]
								a :						List[str]      =							''
								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(lowerCAmelCase__ ) + token
										a :						Dict      =							[]
									else:
										current_sub_tokens.append(lowerCAmelCase__ )
								out_string += self.sp_model.decode(lowerCAmelCase__ )
								return out_string.strip()






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = False )      ->					Dict:

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

								a :						Dict      =							[1] * len(self.prefix_tokens )
								a :						Any      =							[1] * len(self.suffix_tokens )
								if token_ids_a is None:
									return prefix_ones + ([0] * len(lowerCAmelCase__ )) + suffix_ones
								return prefix_ones + ([0] * len(lowerCAmelCase__ )) + ([0] * len(lowerCAmelCase__ )) + suffix_ones






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None )      ->					Tuple:
								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        __a  (  self )      ->					Dict:
								a :						List[Any]      =							{self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
								vocab.update(self.added_tokens_encoder )
								return vocab






							def __getstate__(  self )      ->					Any:
								a :						Tuple      =							self.__dict__.copy()
								a :						Dict      =							None
								return state






							def __setstate__(  self ,   lowerCAmelCase__ )      ->					Tuple:
								a :						Any      =							d

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

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None )      ->					int:
								a :						Dict      =							Path(lowerCAmelCase__ )
								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 :						Union[str, Any]      =							save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
								)

								save_json(self.encoder ,   lowerCAmelCase__ )

								if os.path.abspath(self.spm_file ) != os.path.abspath(lowerCAmelCase__ ) and os.path.isfile(self.spm_file ):
									copyfile(self.spm_file ,   lowerCAmelCase__ )
								elif not os.path.isfile(self.spm_file ):
									with open(lowerCAmelCase__ ,   "wb" ) as fi:
										a :						Optional[int]      =							self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase__ )

								return (str(lowerCAmelCase__ ), str(lowerCAmelCase__ ))






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = "en" ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = "ro" ,   **lowerCAmelCase__ ,   )      ->					str:
								a :						List[str]      =							src_lang
								a :						Union[str, Any]      =							tgt_lang
								self.set_src_lang_special_tokens(self.src_lang )
								return super().prepare_seqaseq_batch(lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )      ->					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" )
								a :						List[str]      =							src_lang
								a :						List[str]      =							self(lowerCAmelCase__ ,   add_special_tokens=lowerCAmelCase__ ,   **lowerCAmelCase__ )
								a :						Optional[int]      =							self.get_lang_id(lowerCAmelCase__ )
								a :						Dict      =							tgt_lang_id
								return inputs






							def        __a  (  self )      ->					List[str]:
								self.set_src_lang_special_tokens(self.src_lang )






							def        __a  (  self )      ->					List[str]:
								self.set_tgt_lang_special_tokens(self.tgt_lang )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[Any]:
								a :						Optional[Any]      =							self.get_lang_token(lowerCAmelCase__ )
								a :						List[str]      =							self.lang_token_to_id[lang_token]
								a :						List[str]      =							[self.cur_lang_id]
								a :						int      =							[self.eos_token_id]






							def        __a  (  self ,   lowerCAmelCase__ )      ->					int:
								a :						Union[str, Any]      =							self.get_lang_token(lowerCAmelCase__ )
								a :						Optional[Any]      =							self.lang_token_to_id[lang_token]
								a :						Optional[Any]      =							[self.cur_lang_id]
								a :						Dict      =							[self.eos_token_id]






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[Any]:
								return self.lang_code_to_token[lang]






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Any:
								a :						Dict      =							self.get_lang_token(lowerCAmelCase__ )
								return self.lang_token_to_id[lang_token]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       List[str]   )					->Optional[Any]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]   )					->Any:






	'''simple docstring'''






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







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Dict   )					->List[Any]:






	'''simple docstring'''






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








"""simple docstring"""






from typing import Any







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validation(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				# Creates data structures and fill initial step
				_lowerCamelCase	:     dict       =       {}
				_lowerCamelCase	:     dict       =       {}
				for state in states_space:
								_lowerCamelCase	:     Optional[Any]       =       observations_space[0]
								_lowerCamelCase	:     int       =       (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_lowerCamelCase	:     List[Any]       =       None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1				,       len(lowercase__						)						):
								_lowerCamelCase	:     List[str]       =       observations_space[o]
								_lowerCamelCase	:     str       =       observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												_lowerCamelCase	:     Dict       =       ''
												_lowerCamelCase	:     int       =       -1
												for k_state in states_space:
																_lowerCamelCase	:     Any       =       (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				_lowerCamelCase	:     Dict       =       probability
																				_lowerCamelCase	:     int       =       k_state

            # Update probabilities and pointers dicts
												_lowerCamelCase	:     List[str]       =       (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_lowerCamelCase	:     Union[str, Any]       =       arg_max

    # The final observation
				_lowerCamelCase	:     Tuple       =       observations_space[len(lowercase__						) - 1]

				# argmax for given final observation
				_lowerCamelCase	:     Optional[int]       =       ''
				_lowerCamelCase	:     str       =       -1
				for k_state in states_space:
								_lowerCamelCase	:     Optional[int]       =       probabilities[(k_state, final_observation)]
								if probability > max_probability:
												_lowerCamelCase	:     int       =       probability
												_lowerCamelCase	:     Optional[int]       =       k_state
				_lowerCamelCase	:     Any       =       arg_max

				# Process pointers backwards
				_lowerCamelCase	:     Optional[Any]       =       last_state
				_lowerCamelCase	:     Union[str, Any]       =       []
				for o in range(len(lowercase__						) - 1				,       -1				,       -1						):
								result.append(lowercase__						)
								_lowerCamelCase	:     Dict       =       pointers[previous, observations_space[o]]
				result.reverse()

				return result







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_not_empty(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				_validate_lists(lowercase__				,       lowercase__						)
				_validate_dicts(
				    lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]						):
								raise ValueError('There\'s an empty parameter'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_list(lowercase__				,       'observations_space'						)
				_validate_list(lowercase__				,       'states_space'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a list'''
								raise ValueError(lowercase__						)
				else:
								for x in _object:
												if not isinstance(lowercase__				,       lowercase__						):
																_lowerCamelCase	:     int       =       f'''{var_name} must be a list of strings'''
																raise ValueError(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_dict(lowercase__				,       'initial_probabilities'				,       lowercase__						)
				_validate_nested_dict(lowercase__				,       'transition_probabilities'						)
				_validate_nested_dict(lowercase__				,       'emission_probabilities'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_dict(_object				,       lowercase__				,       lowercase__						)
				for x in _object.values():
								_validate_dict(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a dict'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object						):
								_lowerCamelCase	:     Optional[int]       =       f'''{var_name} all keys must be strings'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object.values()						):
								_lowerCamelCase	:     Optional[int]       =       'nested dictionary ' if nested else ''
								_lowerCamelCase	:     Optional[Any]       =       f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
								raise ValueError(lowercase__						)


if __name__ == "__main__":
			from doctest import testmod

			testmod()