


import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[Any]							     =   {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__							:						Dict							     =   {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__							:						List[str]							     =   {
    """google/rembert""": 2_5_6,
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							Tuple	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							Union[str, Any]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(					self:Union[str, Any]     ,		_a:Union[str, Any]     ,		_a:Tuple=False     ,		_a:Dict=True     ,		_a:int=True     ,		_a:Optional[Any]="[CLS]"     ,		_a:int="[SEP]"     ,		_a:Dict="[UNK]"     ,		_a:Optional[int]="[SEP]"     ,		_a:Any="[PAD]"     ,		_a:Union[str, Any]="[CLS]"     ,		_a:Dict="[MASK]"     ,		**_a:str     ,		):
       super().__init__(
           do_lower_case=_a     ,		remove_space=_a     ,		keep_accents=_a     ,		bos_token=_a     ,		eos_token=_a     ,		unk_token=_a     ,		sep_token=_a     ,		pad_token=_a     ,		cls_token=_a     ,		mask_token=_a     ,		**_a     ,		)

       snake_case__						= do_lower_case
       snake_case__						= remove_space
       snake_case__						= keep_accents
       snake_case__						= vocab_file

       snake_case__						= spm.SentencePieceProcessor()
       self.sp_model.Load(_a   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return len(self.sp_model   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= {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:str   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       return state

     def __setstate__(					self:List[Any]     ,		_a:Optional[int]   ):
       snake_case__						= d
       snake_case__						= spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Tuple     ,		_a:Any=False   ):
       snake_case__						= self.sp_model.EncodeAsPieces(_a   )
       return pieces

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]   ):
       return self.sp_model.PieceToId(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]   ):
       return self.sp_model.IdToPiece(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:Union[str, Any]   ):
       snake_case__						= self.sp_model.decode_pieces(_a   )
       return out_string

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:List[int]     ,		_a:Optional[List[int]] = None     ,		_a:bool = False   ):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''   )
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_a   )) + [1] + ([0] * len(_a   )) + [1]
       return [1] + ([0] * len(_a   )) + [1]

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [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   ) * [0] + len(token_ids_a + sep   ) * [1]







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_a   )   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ):
         copyfile(self.vocab_file     ,		_a   )

       return (out_vocab_file,)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A		: Dict								=     {
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: Optional[Any]								=     [
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		A		: List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)