



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VivitImageProcessor
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A=7       ,	A=3       ,	A=1_0       ,	A=1_8       ,	A=3_0       ,	A=4_0_0       ,	A=True       ,	A=None       ,	A=True       ,	A=[0.5, 0.5, 0.5]       ,	A=[0.5, 0.5, 0.5]       ,	A=None       ,	)							->					Dict:
         _UpperCAmelCase							:  Any											=    size if size is not None else {'''shortest_edge''': 1_8}
         _UpperCAmelCase							:  Optional[int]											=    crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

         _UpperCAmelCase							:  Tuple											=    parent
         _UpperCAmelCase							:  str											=    batch_size
         _UpperCAmelCase							:  Any											=    num_channels
         _UpperCAmelCase							:  Optional[Any]											=    num_frames
         _UpperCAmelCase							:  Dict											=    image_size
         _UpperCAmelCase							:  List[Any]											=    min_resolution
         _UpperCAmelCase							:  Dict											=    max_resolution
         _UpperCAmelCase							:  Any											=    do_resize
         _UpperCAmelCase							:  Tuple											=    size
         _UpperCAmelCase							:  Tuple											=    do_normalize
         _UpperCAmelCase							:  Optional[int]											=    image_mean
         _UpperCAmelCase							:  int											=    image_std
         _UpperCAmelCase							:  Dict											=    crop_size





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class 							_UpperCAmelCase							( a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=VivitImageProcessor if is_vision_available() else None





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  List[str]											=    VivitImageProcessingTester(self		)





     @property
     def  __lowerCAmelCase	(					self		)							->					Any:
         return self.image_processor_tester.prepare_image_processor_dict()





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  Dict											=    self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(A       ,	'''image_mean'''		)		)
         self.assertTrue(hasattr(A       ,	'''image_std'''		)		)
         self.assertTrue(hasattr(A       ,	'''do_normalize'''		)		)
         self.assertTrue(hasattr(A       ,	'''do_resize'''		)		)
         self.assertTrue(hasattr(A       ,	'''do_center_crop'''		)		)
         self.assertTrue(hasattr(A       ,	'''size'''		)		)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Dict											=    self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size       ,	{'''shortest_edge''': 1_8}		)
         self.assertEqual(image_processor.crop_size       ,	{'''height''': 1_8, '''width''': 1_8}		)

         _UpperCAmelCase							:  str											=    self.image_processing_class.from_dict(self.image_processor_dict       ,	size=4_2       ,	crop_size=8_4		)
         self.assertEqual(image_processor.size       ,	{'''shortest_edge''': 4_2}		)
         self.assertEqual(image_processor.crop_size       ,	{'''height''': 8_4, '''width''': 8_4}		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         # Initialize image_processing
         _UpperCAmelCase							:  List[Any]											=    self.image_processing_class(**self.image_processor_dict		)
         # create random PIL videos
         _UpperCAmelCase							:  Union[str, Any]											=    prepare_video_inputs(self.image_processor_tester       ,	equal_resolution=A		)
         for video in video_inputs:
             self.assertIsInstance(A       ,	A		)
             self.assertIsInstance(video[0]       ,	Image.Image		)

         # Test not batched input
         _UpperCAmelCase							:  List[str]											=    image_processing(video_inputs[0]       ,	return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,	(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)

         # Test batched
         _UpperCAmelCase							:  List[str]											=    image_processing(A       ,	return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         # Initialize image_processing
         _UpperCAmelCase							:  Optional[int]											=    self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         _UpperCAmelCase							:  Any											=    prepare_video_inputs(self.image_processor_tester       ,	equal_resolution=A       ,	numpify=A		)
         for video in video_inputs:
             self.assertIsInstance(A       ,	A		)
             self.assertIsInstance(video[0]       ,	np.ndarray		)

         # Test not batched input
         _UpperCAmelCase							:  List[Any]											=    image_processing(video_inputs[0]       ,	return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,	(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)

         # Test batched
         _UpperCAmelCase							:  str											=    image_processing(A       ,	return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         # Initialize image_processing
         _UpperCAmelCase							:  int											=    self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         _UpperCAmelCase							:  Dict											=    prepare_video_inputs(self.image_processor_tester       ,	equal_resolution=A       ,	torchify=A		)
         for video in video_inputs:
             self.assertIsInstance(A       ,	A		)
             self.assertIsInstance(video[0]       ,	torch.Tensor		)

         # Test not batched input
         _UpperCAmelCase							:  Optional[int]											=    image_processing(video_inputs[0]       ,	return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,	(
                 1,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)

         # Test batched
         _UpperCAmelCase							:  Any											=    image_processing(A       ,	return_tensors='''pt'''		).pixel_values
         self.assertEqual(
             encoded_videos.shape       ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_frames,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)







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

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  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:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

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

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

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

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

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

    return initial_total - optimal_total


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


