

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,)











import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["image_processor", "tokenizer"]
					a_     =		"ViltImageProcessor"
					a_     =		("BertTokenizer", "BertTokenizerFast")



					def __init__( self					:  Optional[int]							,		__A					:  Optional[int]=None							,		__A					:  Optional[Any]=None							,		**__A					:  int	):
						snake_case__			:				Union[str, Any]       =  None
						if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."							,		__A							,		)
							snake_case__			:				Tuple       =  kwargs.pop("feature_extractor"	)

						snake_case__			:				Any       =  image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."	)
						if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."	)

						super().__init__(__A							,		__A	)
						snake_case__			:				Tuple       =  self.image_processor



					def __call__( self					:  List[Any]							,		__A					:  int							,		__A					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,		__A					:  bool = True							,		__A					:  Union[bool, str, PaddingStrategy] = False							,		__A					:  Union[bool, str, TruncationStrategy] = None							,		__A					:  Optional[int] = None							,		__A					:  int = 0							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[bool] = None							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = True							,		__A					:  Optional[Union[str, TensorType]] = None							,		**__A					:  List[Any]							,		):
						snake_case__			:				Optional[int]       =  self.tokenizer(
						    text=__A							,		add_special_tokens=__A							,		padding=__A							,		truncation=__A							,		max_length=__A							,		stride=__A							,		pad_to_multiple_of=__A							,		return_token_type_ids=__A							,		return_attention_mask=__A							,		return_overflowing_tokens=__A							,		return_special_tokens_mask=__A							,		return_offsets_mapping=__A							,		return_length=__A							,		verbose=__A							,		return_tensors=__A							,		**__A							,		)
						# add pixel_values + pixel_mask
						snake_case__			:				Optional[Any]       =  self.image_processor(__A							,		return_tensors=__A	)
						encoding.update(__A	)

						return encoding



					def 	_lowercase					( self					:  Optional[Any]							,		*__A					:  List[str]							,		**__A					:  Optional[int]	):
						return self.tokenizer.batch_decode(*__A							,		**__A	)



					def 	_lowercase					( self					:  Dict							,		*__A					:  str							,		**__A					:  str	):
						return self.tokenizer.decode(*__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  self.tokenizer.model_input_names
						snake_case__			:				Any       =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,		__A							,		)
						return self.image_processor_class






					@property
					def 	_lowercase					( self					:  str	):
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,		__A							,		)
						return self.image_processor



