






'''simple docstring'''

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


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

UpperCAmelCase__    :List[str] 							=      {"""vocab_file""": """sentencepiece.model"""}

UpperCAmelCase__    :List[str] 							=      {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

UpperCAmelCase__    :str 							=      {
    """google/rembert""": 256,
}







class 						SCREAMING_SNAKE_CASE						(   lowerCamelCase__	):
       snake_case__     :  List[str] 		=      VOCAB_FILES_NAMES
       snake_case__     :  Tuple 		=      PRETRAINED_VOCAB_FILES_MAP
       snake_case__     :  Optional[Any] 		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




       def __init__(       self   :			List[str]						,     A__   :			str						,     A__   :			str=False						,     A__   :			List[str]=True						,     A__   :			Dict=True						,     A__   :			Tuple="[CLS]"						,     A__   :			Tuple="[SEP]"						,     A__   :			Dict="[UNK]"						,     A__   :			List[Any]="[SEP]"						,     A__   :			List[Any]="[PAD]"						,     A__   :			Any="[CLS]"						,     A__   :			List[str]="[MASK]"						,     **A__   :			List[Any]						,     ):


            """simple docstring"""







            super().__init__(
                do_lower_case=SCREAMING_SNAKE_CASE__						,     remove_space=SCREAMING_SNAKE_CASE__						,     keep_accents=SCREAMING_SNAKE_CASE__						,     bos_token=SCREAMING_SNAKE_CASE__						,     eos_token=SCREAMING_SNAKE_CASE__						,     unk_token=SCREAMING_SNAKE_CASE__						,     sep_token=SCREAMING_SNAKE_CASE__						,     pad_token=SCREAMING_SNAKE_CASE__						,     cls_token=SCREAMING_SNAKE_CASE__						,     mask_token=SCREAMING_SNAKE_CASE__						,     **SCREAMING_SNAKE_CASE__						,     )

            __lowerCamelCase    :							Optional[int]					 =					do_lower_case
            __lowerCamelCase    :							int					 =					remove_space
            __lowerCamelCase    :							str					 =					keep_accents
            __lowerCamelCase    :							List[Any]					 =					vocab_file

            __lowerCamelCase    :							Optional[int]					 =					spm.SentencePieceProcessor()
            self.sp_model.Load(SCREAMING_SNAKE_CASE__       )




       @property
       def        a_       (       self   :			str       ):


            """simple docstring"""







            return len(self.sp_model       )




       def        a_       (       self   :			str       ):


            """simple docstring"""







            __lowerCamelCase    :							Optional[int]					 =					{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 __getstate__(       self   :			List[str]       ):


            """simple docstring"""







            __lowerCamelCase    :							str					 =					self.__dict__.copy()
            __lowerCamelCase    :							List[Any]					 =					None
            return state




       def __setstate__(       self   :			List[str]						,     A__   :			Optional[int]       ):


            """simple docstring"""







            __lowerCamelCase    :							Any					 =					d
            __lowerCamelCase    :							int					 =					spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file       )




       def        a_       (       self   :			List[str]						,     A__   :			Dict						,     A__   :			Optional[int]=False       ):


            """simple docstring"""







            __lowerCamelCase    :							Tuple					 =					self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE__       )
            return pieces




       def        a_       (       self   :			List[str]						,     A__   :			Any       ):


            """simple docstring"""







            return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__       )




       def        a_       (       self   :			Optional[int]						,     A__   :			Tuple       ):


            """simple docstring"""







            return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__       )




       def        a_       (       self   :			Optional[int]						,     A__   :			Optional[int]       ):


            """simple docstring"""







            __lowerCamelCase    :							int					 =					self.sp_model.decode_pieces(SCREAMING_SNAKE_CASE__       )
            return out_string




       def        a_       (       self   :			str						,     A__   :			Dict						,     A__   :			Optional[int] = None       ):


            """simple docstring"""







            __lowerCamelCase    :							Optional[Any]					 =					[self.sep_token_id]
            __lowerCamelCase    :							Dict					 =					[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        a_       (       self   :			Tuple						,     A__   :			Tuple						,     A__   :			Optional[Any] = None						,     A__   :			Union[str, Any] = False       ):


            """simple docstring"""







            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(SCREAMING_SNAKE_CASE__       )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1]
            return [1] + ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1]




       def        a_       (       self   :			Any						,     A__   :			str						,     A__   :			int = None       ):


            """simple docstring"""







            __lowerCamelCase    :							Union[str, Any]					 =					[self.sep_token_id]
            __lowerCamelCase    :							Union[str, 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        a_       (       self   :			Any						,     A__   :			Dict						,     A__   :			str = None       ):


            """simple docstring"""







            if not os.path.isdir(SCREAMING_SNAKE_CASE__       ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__       )       )
                 return
            __lowerCamelCase    :							int					 =					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__       ):
                 copyfile(self.vocab_file						,     SCREAMING_SNAKE_CASE__       )

            return (out_vocab_file,)







"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[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__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if text is None and visual_prompt is None and images is None:
								raise ValueError('''You have to specify either text, visual prompt or images.'''   )

					if text is not None and visual_prompt is not None:
								raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

					if text is not None:
								_UpperCamelCase					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''pixel_values''': image_features.pixel_values,
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								_UpperCamelCase					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor_class





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
					return self.image_processor

