
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__   ( A__						):
						UpperCamelCase_				:			List[Any]						=		['image_processor', 'tokenizer']
						UpperCamelCase_				:			Tuple						=		'BlipImageProcessor'
						UpperCamelCase_				:			Optional[Any]						=		'AutoTokenizer'

						def __init__(      self							,				a							,				a					)       ->   List[str]:


								'''simple docstring'''

								_UpperCamelCase         =						False
								super().__init__(__UpperCAmelCase							,				__UpperCAmelCase					)
								_UpperCamelCase         =						self.image_processor

						def __call__(      self							,				a = None							,				a = None							,				a = True							,				a = False							,				a = None							,				a = None							,				a = 0							,				a = None							,				a = None							,				a = False							,				a = False							,				a = False							,				a = False							,				a = False							,				a = True							,				a = None							,				**a							,				)       ->   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:
										_UpperCamelCase         =						self.tokenizer
										_UpperCamelCase         =						self.tokenizer(
										    text=__UpperCAmelCase							,				add_special_tokens=__UpperCAmelCase							,				padding=__UpperCAmelCase							,				truncation=__UpperCAmelCase							,				max_length=__UpperCAmelCase							,				stride=__UpperCAmelCase							,				pad_to_multiple_of=__UpperCAmelCase							,				return_attention_mask=__UpperCAmelCase							,				return_overflowing_tokens=__UpperCAmelCase							,				return_special_tokens_mask=__UpperCAmelCase							,				return_offsets_mapping=__UpperCAmelCase							,				return_token_type_ids=__UpperCAmelCase							,				return_length=__UpperCAmelCase							,				verbose=__UpperCAmelCase							,				return_tensors=__UpperCAmelCase							,				**__UpperCAmelCase							,				)
										return text_encoding

								# add pixel_values
								_UpperCamelCase         =						self.image_processor(__UpperCAmelCase							,				return_tensors=__UpperCAmelCase					)

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

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

								return encoding_image_processor

						def 			A_					(      self							,				*a							,				**a					)       ->   Tuple:


								'''simple docstring'''

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

						def 			A_					(      self							,				*a							,				**a					)       ->   Dict:


								'''simple docstring'''

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







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


								'''simple docstring'''

								_UpperCamelCase         =						self.tokenizer.model_input_names
								_UpperCamelCase         =						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							(  snake_case__     ):




			'''simple docstring'''


			for i in range(1		,					len(matrix[0]     )     ):
						matrix[0][i] += matrix[0][i - 1]

			# preprocessing the first column
			for i in range(1		,					len(snake_case__     )     ):
						matrix[i][0] += matrix[i - 1][0]

			# updating the path cost for current position
			for i in range(1		,					len(snake_case__     )     ):
						for j in range(1		,					len(matrix[0]     )     ):
									matrix[i][j] += min(matrix[i - 1][j]		,					matrix[i][j - 1]     )

			return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()


