




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 		UpperCAmelCase   (				_snake_case			):







						'''simple docstring'''





						lowerCAmelCase_     		=   ['''image_processor''', '''tokenizer''']
						lowerCAmelCase_     		=   '''Pix2StructImageProcessor'''
						lowerCAmelCase_     		=   ('''T5Tokenizer''', '''T5TokenizerFast''')






						def __init__(						self							:     List[str]       ,	__lowercase							:     List[str]       ,	__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     False
							super().__init__(_lowerCamelCase       ,	_lowerCamelCase						)






						def __call__(						self							:     str       ,	__lowercase							:     int=None       ,	__lowercase							:     Any = None       ,	__lowercase							:     Tuple = True       ,	__lowercase							:     Dict = False       ,	__lowercase							:     str = None       ,	__lowercase							:     int = None       ,	__lowercase							:     Union[str, Any] = 20_48       ,	__lowercase							:     Dict = 0       ,	__lowercase							:     str = None       ,	__lowercase							:     List[str] = None       ,	__lowercase							:     Optional[Any] = False       ,	__lowercase							:     List[Any] = False       ,	__lowercase							:     Tuple = False       ,	__lowercase							:     Optional[Any] = False       ,	__lowercase							:     Optional[Any] = False       ,	__lowercase							:     Union[str, Any] = True       ,	__lowercase							:     Any = None       ,	**__lowercase							:     Union[str, Any]       ,	):






							"""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 and not self.image_processor.is_vqa:
								snake_case_		    =     self.tokenizer
								snake_case_		    =     self.tokenizer(
								    text=_lowerCamelCase       ,	add_special_tokens=_lowerCamelCase       ,	padding=_lowerCamelCase       ,	truncation=_lowerCamelCase       ,	max_length=_lowerCamelCase       ,	stride=_lowerCamelCase       ,	pad_to_multiple_of=_lowerCamelCase       ,	return_attention_mask=_lowerCamelCase       ,	return_overflowing_tokens=_lowerCamelCase       ,	return_special_tokens_mask=_lowerCamelCase       ,	return_offsets_mapping=_lowerCamelCase       ,	return_token_type_ids=_lowerCamelCase       ,	return_length=_lowerCamelCase       ,	verbose=_lowerCamelCase       ,	return_tensors=_lowerCamelCase       ,	**_lowerCamelCase       ,	)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								snake_case_		    =     self.image_processor(
								    _lowerCamelCase       ,	return_tensors=_lowerCamelCase       ,	max_patches=_lowerCamelCase       ,	**_lowerCamelCase						)
							else:
								# add pixel_values and bbox
								snake_case_		    =     self.image_processor(
								    _lowerCamelCase       ,	return_tensors=_lowerCamelCase       ,	max_patches=_lowerCamelCase       ,	header_text=_lowerCamelCase       ,	**_lowerCamelCase						)

							if text is not None and not self.image_processor.is_vqa:
								snake_case_		    =     self.tokenizer(
								    text=_lowerCamelCase       ,	add_special_tokens=_lowerCamelCase       ,	padding=_lowerCamelCase       ,	truncation=_lowerCamelCase       ,	max_length=_lowerCamelCase       ,	stride=_lowerCamelCase       ,	pad_to_multiple_of=_lowerCamelCase       ,	return_attention_mask=_lowerCamelCase       ,	return_overflowing_tokens=_lowerCamelCase       ,	return_special_tokens_mask=_lowerCamelCase       ,	return_offsets_mapping=_lowerCamelCase       ,	return_token_type_ids=_lowerCamelCase       ,	return_length=_lowerCamelCase       ,	verbose=_lowerCamelCase       ,	return_tensors=_lowerCamelCase       ,	**_lowerCamelCase       ,	)

								if "attention_mask" in text_encoding:
									snake_case_		    =     text_encoding.pop("attention_mask"						)
								if "input_ids" in text_encoding:
									snake_case_		    =     text_encoding.pop("input_ids"						)
							else:
								snake_case_		    =     None

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

							return encoding_image_processor






						def        snake_case__						(						self							:     List[str]       ,	*__lowercase							:     int       ,	**__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Optional[Any]       ,	*__lowercase							:     Tuple       ,	**__lowercase							:     Optional[int]						):






							"""simple docstring"""






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






						@property
						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     self.tokenizer.model_input_names
							snake_case_		    =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




import math







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase=0    ):  # a graph with Node 0,1,...,N-1
       a						:Optional[int] 							=       n
       a						:Union[str, Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # adjacency matrix for weight
       a						:List[Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # dp[i][j] stores minimum distance from i to j




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       w




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for k in range(0					,  self.n    ):
             for i in range(0					,  self.n    ):
                   for j in range(0					,  self.n    ):
                         a						:Union[str, Any] 							=       min(self.dp[i][j]					,  self.dp[i][k] + self.dp[k][j]    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return self.dp[u][v]


if __name__ == "__main__":
     snake_case				:    str       				=     Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 10)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 10)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)





