


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     List[Any]	 =    ["""image_processor""", """tokenizer"""]
						__magic_name__:     str	 =    """BlipImageProcessor"""
						__magic_name__:     List[str]	 =    """AutoTokenizer"""






						def __init__( self : str						,				_A : Any						,				_A : str		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Dict     =		False
							super().__init__(_A						,				_A		)
							snake_case_						:     int     =		self.image_processor






						def __call__( self : Dict						,				_A : ImageInput = None						,				_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 : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = False						,				_A : bool = True						,				_A : Optional[Union[str, TensorType]] = None						,				**_A : int						,				)							->      BatchEncoding:







							"""simple docstring"""


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

							# Get only text
							if images is None:
								snake_case_						:     Optional[Any]     =		self.tokenizer
								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_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_token_type_ids=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)
								return text_encoding

							# add pixel_values
							snake_case_						:     Dict     =		self.image_processor(_A						,				return_tensors=_A		)

							if text is not None:
								snake_case_						:     List[str]     =		self.tokenizer(
								    text=_A						,				add_special_tokens=_A						,				padding=_A						,				truncation=_A						,				max_length=_A						,				stride=_A						,				pad_to_multiple_of=_A						,				return_attention_mask=_A						,				return_overflowing_tokens=_A						,				return_special_tokens_mask=_A						,				return_offsets_mapping=_A						,				return_token_type_ids=_A						,				return_length=_A						,				verbose=_A						,				return_tensors=_A						,				**_A						,				)
							else:
								snake_case_						:     Optional[int]     =		None

							if text_encoding is not None:
								encoding_image_processor.update(_A		)

							return encoding_image_processor






						def UpperCAmelCase_			( self : Optional[Any]						,				*_A : Any						,				**_A : Tuple		)							->      str:







							"""simple docstring"""


							return self.tokenizer.batch_decode(*_A						,				**_A		)






						def UpperCAmelCase_			( self : Tuple						,				*_A : int						,				**_A : str		)							->      Tuple:







							"""simple docstring"""


							return self.tokenizer.decode(*_A						,				**_A		)






						@property
						# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
						def UpperCAmelCase_			( self : str		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     str     =		self.tokenizer.model_input_names
							snake_case_						:     List[str]     =		self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)








def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a = False				):

	if not isinstance(__a				,	__a				):
		snake_case_						:     str     =		f"""Expected string as input, found {type(__a				)}"""
		raise ValueError(__a				)
	if not isinstance(__a				,	__a				):
		snake_case_						:     int     =		f"""Expected boolean as use_pascal parameter, found {type(__a				)}"""
		raise ValueError(__a				)

	snake_case_						:     Union[str, Any]     =		input_str.split('_'				)

	snake_case_						:     int     =		0 if use_pascal else 1

	snake_case_						:     List[Any]     =		words[start_index:]

	snake_case_						:     str     =		[word[0].upper() + word[1:] for word in words_to_capitalize]

	snake_case_						:     Optional[Any]     =		'' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
						from doctest import testmod

						testmod()

