


'''simple docstring'''






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	(						lowercase__							):
							lowercase			:		Optional[Any]   		=  ['image_processor', 'tokenizer']
							lowercase			:		Optional[Any]   		=  'Pix2StructImageProcessor'
							lowercase			:		Optional[Any]   		=  ('T5Tokenizer', 'T5TokenizerFast')





							def __init__(  self		:Any  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Tuple			):
									snake_case_		:							Optional[int]			       =						False
									super().__init__(_UpperCamelCase  ,_UpperCamelCase			)





							def __call__(  self		:str  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,_UpperCamelCase		:bool = True  ,_UpperCamelCase		:Union[bool, str, PaddingStrategy] = False  ,_UpperCamelCase		:Union[bool, str, TruncationStrategy] = None  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[int] = 2_0_4_8  ,_UpperCamelCase		:int = 0  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[bool] = None  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = True  ,_UpperCamelCase		:Optional[Union[str, TensorType]] = None  ,**_UpperCamelCase		:Optional[Any]  ,):
									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_		:							Dict			       =						self.tokenizer
											snake_case_		:							int			       =						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

									if not self.image_processor.is_vqa:
											# add pixel_values
											snake_case_		:							Union[str, Any]			       =						self.image_processor(
											    _UpperCamelCase  ,return_tensors=_UpperCamelCase  ,max_patches=_UpperCamelCase  ,**_UpperCamelCase			)
									else:
											# add pixel_values and bbox
											snake_case_		:							str			       =						self.image_processor(
											    _UpperCamelCase  ,return_tensors=_UpperCamelCase  ,max_patches=_UpperCamelCase  ,header_text=_UpperCamelCase  ,**_UpperCamelCase			)

									if text is not None and not self.image_processor.is_vqa:
											snake_case_		:							Optional[int]			       =						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  ,)

											if "attention_mask" in text_encoding:
													snake_case_		:							Any			       =						text_encoding.pop("""attention_mask"""			)
											if "input_ids" in text_encoding:
													snake_case_		:							List[str]			       =						text_encoding.pop("""input_ids"""			)
									else:
											snake_case_		:							Union[str, Any]			       =						None

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

									return encoding_image_processor





							def 		a__   (  self		:Any  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:str			):
									return self.tokenizer.batch_decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,*_UpperCamelCase		:Dict  ,**_UpperCamelCase		:Optional[Any]			):
									return self.tokenizer.decode(*_UpperCamelCase  ,**_UpperCamelCase			)





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



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:set[int]  ,_UpperCamelCase		:Mapping[EdgeT, int]			):
									snake_case_		:							set[int]			       =						vertices
									snake_case_		:							dict[EdgeT, int]			       =						{
									    (min(_UpperCamelCase			), max(_UpperCamelCase			)): weight for edge, weight in edges.items()
									}





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:EdgeT  ,_UpperCamelCase		:int			):
									self.vertices.add(edge[0]			)
									self.vertices.add(edge[1]			)
									snake_case_		:							str			       =						weight





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Graph			       =						Graph({min(self.vertices			)}  ,{}			)
									snake_case_		:							EdgeT
									snake_case_		:							int
									snake_case_		:							EdgeT
									snake_case_		:							int

									while len(subgraph.vertices			) < len(self.vertices			):
											snake_case_		:							int			       =						max(self.edges.values()			) + 1
											for edge, weight in self.edges.items():
													if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																	snake_case_		:							Dict			       =						edge
																	snake_case_		:							Dict			       =						weight

											subgraph.add_edge(_UpperCamelCase  ,_UpperCamelCase			)

									return subgraph





def      UpperCAmelCase      (		lowerCamelCase_  :str = "p107_network.txt" ):







		'''simple docstring'''
		snake_case_		:							str			       =						os.path.abspath(os.path.dirname(lowerCamelCase_ ) )
		snake_case_		:							str			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							dict[EdgeT, int]			       =						{}
		snake_case_		:							list[str]
		snake_case_		:							int
		snake_case_		:							int

		with open(lowerCamelCase_ ) as f:
				snake_case_		:							Optional[int]			       =						f.read().strip().split("""\n""" )

		snake_case_		:							Any			       =						[line.split(""",""" ) for line in data]

		for edgea in range(1				,      len(lowerCamelCase_ ) ):
				for edgea in range(lowerCamelCase_ ):
						if adjaceny_matrix[edgea][edgea] != "-":
								snake_case_		:							str			       =						int(adjaceny_matrix[edgea][edgea] )

		snake_case_		:							Graph			       =						Graph(set(range(len(lowerCamelCase_ ) ) )				,      lowerCamelCase_ )

		snake_case_		:							Graph			       =						graph.prims_algorithm()

		snake_case_		:							int			       =						sum(graph.edges.values() )
		snake_case_		:							int			       =						sum(subgraph.edges.values() )

		return initial_total - optimal_total


if __name__ == "__main__":
							print(F'{solution() = }')