
'''simple docstring'''




import os
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


lowercase			:				Dict    		=			logging.get_logger(__name__)

lowercase			:				Any    		=			{'vocab_file': 'sentencepiece.bpe.model'}

lowercase			:				Dict    		=			{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

lowercase			:				Optional[Any]    		=			{
    'camembert-base': 5_12,
}

lowercase			:				Optional[int]    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		['''input_ids''', '''attention_mask''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE="<s>"						,			SCREAMING_SNAKE_CASE="</s>"						,			SCREAMING_SNAKE_CASE="</s>"						,			SCREAMING_SNAKE_CASE="<s>"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="<mask>"						,			SCREAMING_SNAKE_CASE=["<s>NOTUSED", "</s>NOTUSED"]						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				None:






         """simple docstring"""






         A       :   Tuple					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

         A       :   str					=						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			sep_token=SCREAMING_SNAKE_CASE						,			cls_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			mask_token=SCREAMING_SNAKE_CASE						,			additional_special_tokens=SCREAMING_SNAKE_CASE						,			sp_model_kwargs=self.sp_model_kwargs						,			**SCREAMING_SNAKE_CASE						,			)
         A       :   Optional[int]					=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(str(SCREAMING_SNAKE_CASE	)	)
         A       :   str					=						vocab_file
         # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
         # sentencepiece vocabulary (this is the case for <s> and </s>
         A       :   Optional[Any]					=						{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
         A       :   List[str]					=						len(self.fairseq_tokens_to_ids	)
         A       :   List[str]					=						len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
         A       :   List[Any]					=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A       :   Optional[int]					=						[self.cls_token_id]
         A       :   int					=						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






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

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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Optional[Any]					=						[self.sep_token_id]
         A       :   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 + sep + token_ids_a + sep	) * [0]





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   str					=						{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         return self.sp_model.encode(SCREAMING_SNAKE_CASE						,			out_type=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(SCREAMING_SNAKE_CASE	) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
         return self.fairseq_offset + self.sp_model.PieceToId(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						[]
         A       :   Union[str, Any]					=						''''''
         A       :   Union[str, 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(SCREAMING_SNAKE_CASE	) + token
                 A       :   Dict					=						True
                 A       :   Optional[Any]					=						[]
             else:
                 current_sub_tokens.append(SCREAMING_SNAKE_CASE	)
                 A       :   Optional[int]					=						False
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE	)
         return out_string.strip()





     def __getstate__(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   Dict					=						self.__dict__.copy()
         A       :   Optional[Any]					=						None
         return state





     def __setstate__(							self						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   Dict					=						d

         # for backward compatibility
         if not hasattr(self						,			'''sp_model_kwargs'''	):
             A       :   Dict					=						{}

         A       :   Any					=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE	):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
             return
         A       :   List[str]					=						os.path.join(
             SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file						,			SCREAMING_SNAKE_CASE	)
         elif not os.path.isfile(self.vocab_file	):
             with open(SCREAMING_SNAKE_CASE						,			'''wb'''	) as fi:
                 A       :   Tuple					=						self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE	)

         return (out_vocab_file,)









A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





