
"""simple docstring"""





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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["image_processor", "tokenizer"]
			snake_case						   =					"BlipImageProcessor"
			snake_case						   =					"AutoTokenizer"




			def __init__(					self    :       Tuple							, _snake_case    :       Tuple							, _snake_case    :       int						)   ->			Any:







								"""simple docstring"""
								A_								=    False
								super().__init__(_snake_case							, _snake_case						)
								A_								=    self.image_processor




			def __call__(					self    :       Dict							, _snake_case    :       ImageInput = None							, _snake_case    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							, _snake_case    :       bool = True							, _snake_case    :       Union[bool, str, PaddingStrategy] = False							, _snake_case    :       Union[bool, str, TruncationStrategy] = None							, _snake_case    :       Optional[int] = None							, _snake_case    :       int = 0							, _snake_case    :       Optional[int] = None							, _snake_case    :       Optional[bool] = None							, _snake_case    :       bool = False							, _snake_case    :       bool = False							, _snake_case    :       bool = False							, _snake_case    :       bool = False							, _snake_case    :       bool = False							, _snake_case    :       bool = True							, _snake_case    :       Optional[Union[str, TensorType]] = None							, **_snake_case    :       Dict							, )   ->			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:
													A_								=    self.tokenizer
													A_								=    self.tokenizer(
													    text=_snake_case							, add_special_tokens=_snake_case							, padding=_snake_case							, truncation=_snake_case							, max_length=_snake_case							, stride=_snake_case							, pad_to_multiple_of=_snake_case							, return_attention_mask=_snake_case							, return_overflowing_tokens=_snake_case							, return_special_tokens_mask=_snake_case							, return_offsets_mapping=_snake_case							, return_token_type_ids=_snake_case							, return_length=_snake_case							, verbose=_snake_case							, return_tensors=_snake_case							, **_snake_case							, )
													return text_encoding

								# add pixel_values
								A_								=    self.image_processor(_snake_case							, return_tensors=_snake_case						)

								if text is not None:
													A_								=    self.tokenizer(
													    text=_snake_case							, add_special_tokens=_snake_case							, padding=_snake_case							, truncation=_snake_case							, max_length=_snake_case							, stride=_snake_case							, pad_to_multiple_of=_snake_case							, return_attention_mask=_snake_case							, return_overflowing_tokens=_snake_case							, return_special_tokens_mask=_snake_case							, return_offsets_mapping=_snake_case							, return_token_type_ids=_snake_case							, return_length=_snake_case							, verbose=_snake_case							, return_tensors=_snake_case							, **_snake_case							, )
								else:
													A_								=    None

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

								return encoding_image_processor




			def   lowerCamelCase__  (					self    :       Tuple							, *_snake_case    :       int							, **_snake_case    :       Dict						)   ->			Any:







								"""simple docstring"""
								return self.tokenizer.batch_decode(*_snake_case							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Any							, *_snake_case    :       Any							, **_snake_case    :       Any						)   ->			List[Any]:







								"""simple docstring"""
								return self.tokenizer.decode(*_snake_case							, **_snake_case						)






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def   lowerCamelCase__  (					self    :       Dict						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.tokenizer.model_input_names
								A_								=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					, __a = None					, __a = None					):

					'''simple docstring'''





					if start is None:
										A_								=    0

					if end is None:
										A_								=    len(__a					) - 1

					if start >= end:
										return

					A_								=    (start + end) // 2

					slowsort(__a					, __a					, __a					)
					slowsort(__a					, mid + 1					, __a					)

					if sequence[end] < sequence[mid]:
										A_			,     A_								=    sequence[mid], sequence[end]

					slowsort(__a					, __a					, end - 1					)


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

		testmod()




