





'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["pixel_values"]



     def __init__(   self							:			str   ,       __snake_case							:			bool = True   ,       __snake_case							:			Dict[str, int] = None   ,       __snake_case							:			PILImageResampling = PILImageResampling.BILINEAR   ,       __snake_case							:			bool = True   ,       __snake_case							:			Union[int, float] = 1 / 2_55   ,       __snake_case							:			bool = True   ,       __snake_case							:			Dict[str, int] = None   ,       __snake_case							:			bool = True   ,       **__snake_case							:			Tuple   ,       )->	None:
        super().__init__(**__snake_case						)
        snake_case 						=   size if size is not None else {"""shortest_edge""": 2_24}
        snake_case 						=   get_size_dict(__snake_case   ,       default_to_square=__snake_case						)
        snake_case 						=   crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
        snake_case 						=   get_size_dict(__snake_case   ,       param_name="""crop_size"""						)

        snake_case 						=   do_resize
        snake_case 						=   size
        snake_case 						=   resample
        snake_case 						=   do_rescale
        snake_case 						=   rescale_factor
        snake_case 						=   do_center_crop
        snake_case 						=   crop_size
        snake_case 						=   do_flip_channel_order



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Dict[str, int]   ,       __snake_case							:			PILImageResampling = PIL.Image.BILINEAR   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case							:			List[str]   ,       )->	np.ndarray:
        snake_case 						=   get_size_dict(__snake_case   ,       default_to_square=__snake_case						)
        if "shortest_edge" not in size:
           raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''						)
        snake_case 						=   get_resize_output_image_size(__snake_case   ,       size=size["""shortest_edge"""]   ,       default_to_square=__snake_case						)
        return resize(__snake_case   ,       size=__snake_case   ,       resample=__snake_case   ,       data_format=__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Dict[str, int]   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case							:			Optional[int]   ,       )->	np.ndarray:
        snake_case 						=   get_size_dict(__snake_case						)
        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()}'''						)
        return center_crop(__snake_case   ,       size=(size["""height"""], size["""width"""])   ,       data_format=__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Union[int, float]   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case							:			str   ,       )->	Optional[Any]:
        return rescale(__snake_case   ,       scale=__snake_case   ,       data_format=__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None						)->	np.ndarray:
        return flip_channel_order(__snake_case   ,       data_format=__snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			ImageInput   ,       __snake_case							:			bool = None   ,       __snake_case							:			Dict[str, int] = None   ,       __snake_case							:			PILImageResampling = None   ,       __snake_case							:			bool = None   ,       __snake_case							:			float = None   ,       __snake_case							:			bool = None   ,       __snake_case							:			Dict[str, int] = None   ,       __snake_case							:			bool = None   ,       __snake_case							:			Optional[Union[str, TensorType]] = None   ,       __snake_case							:			ChannelDimension = ChannelDimension.FIRST   ,       **__snake_case							:			List[str]   ,       )->	PIL.Image.Image:
        snake_case 						=   do_resize if do_resize is not None else self.do_resize
        snake_case 						=   resample if resample is not None else self.resample
        snake_case 						=   do_rescale if do_rescale is not None else self.do_rescale
        snake_case 						=   rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case 						=   do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case 						=   (
            do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
        )

        snake_case 						=   size if size is not None else self.size
        snake_case 						=   get_size_dict(__snake_case   ,       default_to_square=__snake_case						)
        snake_case 						=   crop_size if crop_size is not None else self.crop_size
        snake_case 						=   get_size_dict(__snake_case   ,       param_name="""crop_size"""						)

        snake_case 						=   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:
           raise ValueError("""Size 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_center_crop and crop_size is None:
           raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

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

        if do_resize:
           snake_case 						=   [self.resize(image=__snake_case   ,       size=__snake_case   ,       resample=__snake_case						) for image in images]

        if do_center_crop:
           snake_case 						=   [self.center_crop(image=__snake_case   ,       size=__snake_case						) for image in images]

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

        # the pretrained checkpoints assume images are BGR, not RGB
        if do_flip_channel_order:
           snake_case 						=   [self.flip_channel_order(image=__snake_case						) for image in images]

        snake_case 						=   [to_channel_dimension_format(__snake_case   ,       __snake_case						) for image in images]

        snake_case 						=   {"""pixel_values""": images}
        return BatchFeature(data=__snake_case   ,       tensor_type=__snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Any   ,       __snake_case							:			List[Tuple] = None						)->	Union[str, Any]:
        snake_case 						=   outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
           if len(__snake_case						) != len(__snake_case						):
              raise ValueError(
                  """Make sure that you pass in as many target sizes as the batch dimension of the logits"""						)

           if is_torch_tensor(__snake_case						):
              snake_case 						=   target_sizes.numpy()

           snake_case 						=   []

           for idx in range(len(__snake_case						)						):
              snake_case 						=   torch.nn.functional.interpolate(
                  logits[idx].unsqueeze(dim=0						)   ,       size=target_sizes[idx]   ,       mode="""bilinear"""   ,       align_corners=__snake_case						)
              snake_case 						=   resized_logits[0].argmax(dim=0						)
              semantic_segmentation.append(__snake_case						)
        else:
           snake_case 						=   logits.argmax(dim=1						)
           snake_case 						=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

        return semantic_segmentation








'''simple docstring'''




from __future__ import annotations

import os
from collections.abc import Mapping

_SCREAMING_SNAKE_CASE							     =			tuple[int, int]
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			List[Any]   ,       __snake_case							:			set[int]   ,       __snake_case							:			Mapping[EdgeT, int]						)->	None:
        snake_case 						=   vertices
        snake_case 						=   {
            (min(__snake_case						), max(__snake_case						)): weight for edge, weight in edges.items()
        }



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			EdgeT   ,       __snake_case							:			int						)->	None:
        self.vertices.add(edge[0]						)
        self.vertices.add(edge[1]						)
        snake_case 						=   weight



     def      lowerCAmelCase			(   self							:			str						)->	Graph:
        snake_case 						=   Graph({min(self.vertices						)}   ,       {}						)
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42
        snake_case 						=   42

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

           subgraph.add_edge(__snake_case   ,       __snake_case						)

        return subgraph





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "p107_network.txt"		)		->  int:
   snake_case 						=   os.path.abspath(os.path.dirname(__lowerCAmelCase		)		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {}
   snake_case 						=   42
   snake_case 						=   42
   snake_case 						=   42

   with open(__lowerCAmelCase		) as f:
      snake_case 						=   f.read().strip().split("""\n"""		)

   snake_case 						=   [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 						=   int(adjaceny_matrix[edgea][edgea]		)

   snake_case 						=   Graph(set(range(len(__lowerCAmelCase		)		)		)      ,    __lowerCAmelCase		)

   snake_case 						=   graph.prims_algorithm()

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

   return initial_total - optimal_total


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

