





"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


A 			=  logging.get_logger(__name__)




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  ['''pixel_values''']







					def __init__(						self	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = PILImageResampling.BICUBIC	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 1 / 255	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = True	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  size if size is not None else {'''height''': 384, '''width''': 384}
									__a :						str			       =  get_size_dict(_UpperCAmelCase	,			default_to_square=_UpperCAmelCase     )

									__a :						str			       =  do_resize
									__a :						Dict			       =  size
									__a :						Union[str, Any]			       =  resample
									__a :						Union[str, Any]			       =  do_rescale
									__a :						Tuple			       =  rescale_factor
									__a :						Optional[Any]			       =  do_normalize
									__a :						Tuple			       =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									__a :						Any			       =  image_std if image_std is not None else OPENAI_CLIP_STD
									__a :						List[str]			       =  do_convert_rgb







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = PILImageResampling.BICUBIC	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						str			       =  get_size_dict(_UpperCAmelCase	,			default_to_square=_UpperCAmelCase     )
									if "height" not in size or "width" not in size:
													raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""     )
									__a :						List[Any]			       =  (size['''height'''], size['''width'''])
									return resize(_UpperCAmelCase	,			size=_UpperCAmelCase	,			resample=_UpperCAmelCase	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									return rescale(_UpperCAmelCase	,			scale=_UpperCAmelCase	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									return normalize(_UpperCAmelCase	,			mean=_UpperCAmelCase	,			std=_UpperCAmelCase	,			data_format=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = ChannelDimension.FIRST	,			**_UpperCAmelCase	,			):
									__a :						int			       =  do_resize if do_resize is not None else self.do_resize
									__a :						Dict			       =  resample if resample is not None else self.resample
									__a :						Dict			       =  do_rescale if do_rescale is not None else self.do_rescale
									__a :						str			       =  rescale_factor if rescale_factor is not None else self.rescale_factor
									__a :						Optional[int]			       =  do_normalize if do_normalize is not None else self.do_normalize
									__a :						Optional[int]			       =  image_mean if image_mean is not None else self.image_mean
									__a :						List[Any]			       =  image_std if image_std is not None else self.image_std
									__a :						int			       =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									__a :						List[Any]			       =  size if size is not None else self.size
									__a :						List[Any]			       =  get_size_dict(_UpperCAmelCase	,			default_to_square=_UpperCAmelCase     )

									__a :						Optional[int]			       =  make_list_of_images(_UpperCAmelCase     )

									if not valid_images(_UpperCAmelCase     ):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

									if do_resize and size is None or resample is None:
													raise ValueError('''Size and resample must be specified if do_resize is True.'''     )

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and std must be specified if do_normalize is True.'''     )

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
													__a :						Dict			       =  [convert_to_rgb(_UpperCAmelCase     ) for image in images]

									# All transformations expect numpy arrays.
									__a :						Tuple			       =  [to_numpy_array(_UpperCAmelCase     ) for image in images]

									if do_resize:
													__a :						Tuple			       =  [self.resize(image=_UpperCAmelCase	,			size=_UpperCAmelCase	,			resample=_UpperCAmelCase     ) for image in images]

									if do_rescale:
													__a :						List[str]			       =  [self.rescale(image=_UpperCAmelCase	,			scale=_UpperCAmelCase     ) for image in images]

									if do_normalize:
													__a :						Union[str, Any]			       =  [self.normalize(image=_UpperCAmelCase	,			mean=_UpperCAmelCase	,			std=_UpperCAmelCase     ) for image in images]

									__a :						List[str]			       =  [to_channel_dimension_format(_UpperCAmelCase	,			_UpperCAmelCase     ) for image in images]

									__a :						List[str]			       =  BatchFeature(data={'''pixel_values''': images}	,			tensor_type=_UpperCAmelCase     )

									return encoded_outputs






"""simple docstring"""






from __future__ import annotations

import os
from collections.abc import Mapping

A 			=  tuple[int, int]




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						set[int]			       =  vertices
									__a :						dict[EdgeT, int]			       =  {
									    (min(_UpperCAmelCase     ), max(_UpperCAmelCase     )): weight for edge, weight in edges.items()
									}







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									self.vertices.add(edge[0]     )
									self.vertices.add(edge[1]     )
									__a :						Any			       =  weight







					def  _lowerCamelCase		(						self     ):
									__a :						Graph			       =  Graph({min(self.vertices     )}	,			{}     )
									__a :						EdgeT
									__a :						int
									__a :						EdgeT
									__a :						int

									while len(subgraph.vertices     ) < len(self.vertices     ):
													__a :						List[Any]			       =  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:
																									__a :						str			       =  edge
																									__a :						int			       =  weight

													subgraph.add_edge(_UpperCAmelCase	,			_UpperCAmelCase     )

									return subgraph






def     __A      (			a_						:str = "p107_network.txt")		->		int:
				__a :						str			       =  os.path.abspath(os.path.dirname(a_))
				__a :						str			       =  os.path.join(a_   ,       a_)
				__a :						dict[EdgeT, int]			       =  {}
				__a :						list[str]
				__a :						int
				__a :						int

				with open(a_) as f:
								__a :						Any			       =  f.read().strip().split('''\n''')

				__a :						Union[str, Any]			       =  [line.split(''',''') for line in data]

				for edgea in range(1   ,       len(a_)):
								for edgea in range(a_):
												if adjaceny_matrix[edgea][edgea] != "-":
																__a :						int			       =  int(adjaceny_matrix[edgea][edgea])

				__a :						Graph			       =  Graph(set(range(len(a_)))   ,       a_)

				__a :						Graph			       =  graph.prims_algorithm()

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

				return initial_total - optimal_total


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