





"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__UpperCamelCase     :						List[Any]						       =  logging.get_logger(__name__)







class       a							(   a__  ):
 snake_case__             =       ['''pixel_values''']



 def __init__(     self				,       _snake_case = True				,       _snake_case = None				,       _snake_case = None				,       _snake_case = PILImageResampling.BILINEAR				,       _snake_case = True				,       _snake_case = 1 / 2_55				,       _snake_case = True				,       _snake_case = None				,       _snake_case = None				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )
  lowerCAmelCase    				= size if size is not None else {'shortest_edge': 3_84}
  lowerCAmelCase    				= get_size_dict(_snake_case				,       default_to_square=_snake_case     )

  lowerCAmelCase    				= do_resize
  lowerCAmelCase    				= size
  # Default value set here for backwards compatibility where the value in config is None
  lowerCAmelCase    				= crop_pct if crop_pct is not None else 2_24 / 2_56
  lowerCAmelCase    				= resample
  lowerCAmelCase    				= do_rescale
  lowerCAmelCase    				= rescale_factor
  lowerCAmelCase    				= do_normalize
  lowerCAmelCase    				= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  lowerCAmelCase    				= image_std if image_std is not None else IMAGENET_STANDARD_STD



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case = PILImageResampling.BICUBIC				,       _snake_case = None				,       **_snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= get_size_dict(_snake_case				,       default_to_square=_snake_case     )
  if "shortest_edge" not in size:
   raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'     )
  lowerCAmelCase    				= size['shortest_edge']

  if shortest_edge < 3_84:
   # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
   lowerCAmelCase    				= int(shortest_edge / crop_pct     )
   lowerCAmelCase    				= get_resize_output_image_size(_snake_case				,       size=_snake_case				,       default_to_square=_snake_case     )
   lowerCAmelCase    				= resize(image=_snake_case				,       size=_snake_case				,       resample=_snake_case				,       data_format=_snake_case				,       **_snake_case     )
   # then crop to (shortest_edge, shortest_edge)
   return center_crop(image=_snake_case				,       size=(shortest_edge, shortest_edge)				,       data_format=_snake_case				,       **_snake_case     )
  else:
   # warping (no cropping) when evaluated at 384 or larger
   return resize(
       _snake_case				,       size=(shortest_edge, shortest_edge)				,       resample=_snake_case				,       data_format=_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case = None				,       **_snake_case				,       ):







  """simple docstring"""






  return rescale(_snake_case				,       scale=_snake_case				,       data_format=_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case = None				,       **_snake_case				,       ):







  """simple docstring"""






  return normalize(_snake_case				,       mean=_snake_case				,       std=_snake_case				,       data_format=_snake_case				,       **_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = None				,       _snake_case = ChannelDimension.FIRST				,       **_snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= do_resize if do_resize is not None else self.do_resize
  lowerCAmelCase    				= crop_pct if crop_pct is not None else self.crop_pct
  lowerCAmelCase    				= resample if resample is not None else self.resample
  lowerCAmelCase    				= do_rescale if do_rescale is not None else self.do_rescale
  lowerCAmelCase    				= rescale_factor if rescale_factor is not None else self.rescale_factor
  lowerCAmelCase    				= do_normalize if do_normalize is not None else self.do_normalize
  lowerCAmelCase    				= image_mean if image_mean is not None else self.image_mean
  lowerCAmelCase    				= image_std if image_std is not None else self.image_std

  lowerCAmelCase    				= size if size is not None else self.size
  lowerCAmelCase    				= get_size_dict(_snake_case				,       default_to_square=_snake_case     )

  lowerCAmelCase    				= make_list_of_images(_snake_case     )

  if not valid_images(_snake_case     ):
   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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
   raise ValueError('crop_pct must be specified if size < 384.'     )

  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.'     )

  # All transformations expect numpy arrays.
  lowerCAmelCase    				= [to_numpy_array(_snake_case     ) for image in images]

  if do_resize:
   lowerCAmelCase    				= [self.resize(image=_snake_case				,       size=_snake_case				,       crop_pct=_snake_case				,       resample=_snake_case     ) for image in images]

  if do_rescale:
   lowerCAmelCase    				= [self.rescale(image=_snake_case				,       scale=_snake_case     ) for image in images]

  if do_normalize:
   lowerCAmelCase    				= [self.normalize(image=_snake_case				,       mean=_snake_case				,       std=_snake_case     ) for image in images]

  lowerCAmelCase    				= [to_channel_dimension_format(_snake_case				,       _snake_case     ) for image in images]

  lowerCAmelCase    				= {'pixel_values': images}
  return BatchFeature(data=_snake_case				,       tensor_type=_snake_case     )












"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase     :						Optional[Any]						       =  tuple[int, int]







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vertices
  lowerCAmelCase    				= {
      (min(_snake_case     ), max(_snake_case     )): weight for edge, weight in edges.items()
  }



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.vertices.add(edge[0]     )
  self.vertices.add(edge[1]     )
  lowerCAmelCase    				= weight



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Graph({min(self.vertices     )}				,       {}     )
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42

  while len(subgraph.vertices     ) < len(self.vertices     ):
   lowerCAmelCase    				= 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:
      lowerCAmelCase    				= edge
      lowerCAmelCase    				= weight

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

 lowerCAmelCase    				= [line.split(','			) for line in data]

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

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

 lowerCAmelCase    				= sum(graph.edges.values()			)
 lowerCAmelCase    				= sum(subgraph.edges.values()			)

 return initial_total - optimal_total


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





