






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 :      Dict										=				logging.get_logger(__name__)

__lowerCamelCase :      Tuple										=				{"""vocab_file""": """sentencepiece.model"""}

__lowerCamelCase :      Optional[Any]										=				{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

__lowerCamelCase :      Union[str, Any]										=				{
    """google/rembert""": 256,
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		VOCAB_FILES_NAMES
					a_     =		PRETRAINED_VOCAB_FILES_MAP
					a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



					def __init__( self					:  Optional[int]							,		__A					:  int							,		__A					:  Dict=False							,		__A					:  Any=True							,		__A					:  Optional[Any]=True							,		__A					:  Any="[CLS]"							,		__A					:  int="[SEP]"							,		__A					:  int="[UNK]"							,		__A					:  List[Any]="[SEP]"							,		__A					:  Tuple="[PAD]"							,		__A					:  Optional[Any]="[CLS]"							,		__A					:  Tuple="[MASK]"							,		**__A					:  Union[str, Any]							,		):
						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__			:				Any       =  do_lower_case
						snake_case__			:				Dict       =  remove_space
						snake_case__			:				Any       =  keep_accents
						snake_case__			:				List[str]       =  vocab_file

						snake_case__			:				int       =  spm.SentencePieceProcessor()
						self.sp_model.Load(__A	)



					@property
					def 	_lowercase					( self					:  Optional[Any]	):
						return len(self.sp_model	)



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				str       =  {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					:  Tuple	):
						snake_case__			:				List[Any]       =  self.__dict__.copy()
						snake_case__			:				Optional[int]       =  None
						return state



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



					def 	_lowercase					( self					:  Dict							,		__A					:  Dict							,		__A					:  List[str]=False	):
						snake_case__			:				Optional[Any]       =  self.sp_model.EncodeAsPieces(__A	)
						return pieces



					def 	_lowercase					( self					:  str							,		__A					:  Dict	):
						return self.sp_model.PieceToId(__A	)



					def 	_lowercase					( self					:  Tuple							,		__A					:  Optional[Any]	):
						return self.sp_model.IdToPiece(__A	)



					def 	_lowercase					( self					:  int							,		__A					:  Dict	):
						snake_case__			:				Tuple       =  self.sp_model.decode_pieces(__A	)
						return out_string



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[int]							,		__A					:  Optional[List[int]] = None	):
						snake_case__			:				Optional[Any]       =  [self.sep_token_id]
						snake_case__			:				Any       =  [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 	_lowercase					( self					:  Optional[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 	_lowercase					( self					:  Dict							,		__A					:  List[int]							,		__A					:  Optional[List[int]] = None	):
						snake_case__			:				Tuple       =  [self.sep_token_id]
						snake_case__			:				List[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	) * [0] + len(token_ids_a + sep	) * [1]






					def 	_lowercase					( self					:  Tuple							,		__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__			:				Union[str, Any]       =  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


__lowerCamelCase :      Any										=				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[Any]										=				[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



