


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

import sentencepiece as spm

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


snake_case							: Optional[int]      =   logging.get_logger(__name__)

snake_case							: Any      =   {'''vocab_file''': '''spiece.model'''}

snake_case							: Optional[Any]      =   {
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

snake_case							: Any      =   {'''bert_for_seq_generation''': 5_12}







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					List[str]					   =    VOCAB_FILES_NAMES
					UpperCAmelCase__      :					int					   =    PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__      :					Optional[Any]					   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__      :					List[int]					   =    []
					UpperCAmelCase__      :					str					   =    ['''input_ids''', '''attention_mask''']
					def __init__(   self     :Tuple    ,__snake_case     :List[str]    ,__snake_case     :int="<s>"    ,__snake_case     :Tuple="</s>"    ,__snake_case     :List[Any]="<unk>"    ,__snake_case     :Optional[Any]="<pad>"    ,__snake_case     :Union[str, Any]="<::::>"    ,__snake_case     :Optional[Dict[str, Any]] = None    ,**__snake_case     :List[Any]    ,)      ->					None:
												a__    =  {} if sp_model_kwargs is None else sp_model_kwargs

												# Add extra_ids to the special token list
												super().__init__(
												    bos_token=__snake_case    ,eos_token=__snake_case    ,unk_token=__snake_case    ,pad_token=__snake_case    ,sep_token=__snake_case    ,sp_model_kwargs=self.sp_model_kwargs    ,**__snake_case    ,)

												a__    =  vocab_file

												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(__snake_case   )
					@property
					def 				lowerCamelCase__(   self     :Dict   )      ->					Dict:
												return self.sp_model.get_piece_size()
					def 				lowerCamelCase__(   self     :Any   )      ->					Optional[int]:
												a__    =  {self.convert_ids_to_tokens(__snake_case   ): i for i in range(self.vocab_size   )}
												vocab.update(self.added_tokens_encoder   )
												return vocab
					def __getstate__(   self     :str   )      ->					str:
												a__    =  self.__dict__.copy()
												a__    =  None
												return state
					def __setstate__(   self     :Optional[int]    ,__snake_case     :str   )      ->					Any:
												a__    =  d

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

												a__    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
												self.sp_model.Load(self.vocab_file   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :str   )      ->					List[str]:
												return self.sp_model.encode(__snake_case    ,out_type=__snake_case   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Optional[Any]   )      ->					int:
												return self.sp_model.piece_to_id(__snake_case   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :List[str]   )      ->					Tuple:
												a__    =  self.sp_model.IdToPiece(__snake_case   )
												return token
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Tuple   )      ->					List[str]:
												a__    =  []
												a__    =  ''
												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(__snake_case   ) + token
																										a__    =  []
																			else:
																										current_sub_tokens.append(__snake_case   )
												out_string += self.sp_model.decode(__snake_case   )
												return out_string.strip()



					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :str    ,__snake_case     :Optional[str] = None   )      ->					Tuple[str]:
												if not os.path.isdir(__snake_case   ):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
																			return
												a__    =  os.path.join(
												    __snake_case    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

												return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




