

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_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ViltImageProcessor



class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a=7 , __a=3 , __a=18 , __a=30 , __a=400 , __a=True , __a=None , __a=2 , __a=True , __a=[0.5, 0.5, 0.5] , __a=[0.5, 0.5, 0.5] , )				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       size if size is not None else {'shortest_edge': 30}
  UpperCAmelCase__           =       parent
  UpperCAmelCase__           =       batch_size
  UpperCAmelCase__           =       num_channels
  UpperCAmelCase__           =       image_size
  UpperCAmelCase__           =       min_resolution
  UpperCAmelCase__           =       max_resolution
  UpperCAmelCase__           =       do_resize
  UpperCAmelCase__           =       size
  UpperCAmelCase__           =       size_divisor
  UpperCAmelCase__           =       do_normalize
  UpperCAmelCase__           =       image_mean
  UpperCAmelCase__           =       image_std




 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  return {
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_normalize": self.do_normalize,
      "do_resize": self.do_resize,
      "size": self.size,
      "size_divisor": self.size_divisor,
  }




 def 						UpperCamelCase__							(self , __a , __a=False				)				->      int:






  """simple docstring"""





  if not batched:
   UpperCAmelCase__           =       self.size['shortest_edge']
   UpperCAmelCase__           =       image_inputs[0]
   if isinstance(__a , Image.Image				):
    UpperCAmelCase__       ,					UpperCAmelCase__           =       image.size
   else:
    UpperCAmelCase__       ,					UpperCAmelCase__           =       image.shape[1], image.shape[2]
   UpperCAmelCase__           =       size / min(__a , __a				)
   if h < w:
    UpperCAmelCase__       ,					UpperCAmelCase__           =       size, scale * w
   else:
    UpperCAmelCase__       ,					UpperCAmelCase__           =       scale * h, size

   UpperCAmelCase__           =       int((1333 / 800) * size				)
   if max(__a , __a				) > max_size:
    UpperCAmelCase__           =       max_size / max(__a , __a				)
    UpperCAmelCase__           =       newh * scale
    UpperCAmelCase__           =       neww * scale

   UpperCAmelCase__       ,					UpperCAmelCase__           =       int(newh + 0.5				), int(neww + 0.5				)
   UpperCAmelCase__       ,					UpperCAmelCase__           =       (
       newh // self.size_divisor * self.size_divisor,
       neww // self.size_divisor * self.size_divisor,
   )

  else:
   UpperCAmelCase__           =       []
   for image in image_inputs:
    UpperCAmelCase__       ,					UpperCAmelCase__           =       self.get_expected_values([image]				)
    expected_values.append((expected_height, expected_width)				)
   UpperCAmelCase__           =       max(__a , key=lambda __a				: item[0]				)[0]
   UpperCAmelCase__           =       max(__a , key=lambda __a				: item[1]				)[1]

  return expected_height, expected_width







@require_torch
@require_vision
class 					lowercase		( _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       ViltImageProcessor if is_vision_available() else None




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       ViltImageProcessingTester(self				)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       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 , 'size'				)				)
  self.assertTrue(hasattr(__a , 'size_divisor'				)				)




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class.from_dict(self.image_processor_dict				)
  self.assertEqual(image_processor.size , {'shortest_edge': 30}				)

  UpperCAmelCase__           =       self.image_processing_class.from_dict(self.image_processor_dict , size=42				)
  self.assertEqual(image_processor.size , {'shortest_edge': 42}				)




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random PIL images
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , Image.Image				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_values(__a				)
  self.assertEqual(
      encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_values(__a , batched=__a				)
  self.assertEqual(
      encoded_images.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random numpy tensors
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , np.ndarray				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_values(__a				)
  self.assertEqual(
      encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_values(__a , batched=__a				)
  self.assertEqual(
      encoded_images.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random PyTorch tensors
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , torch.Tensor				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_values(__a				)
  self.assertEqual(
      encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				).pixel_values

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_values(__a , batched=__a				)
  self.assertEqual(
      encoded_images.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )






from __future__ import annotations



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       vertices
  UpperCAmelCase__           =       [[0] * vertices for _ in range(__a				)]




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  print('Vertex \t Distance from Source'				)
  for vertex in range(self.vertices				):
   print(__a , '\t\t' , distances_from_source[vertex]				)




 def 						UpperCamelCase__							(self , __a , __a				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       1E7
  UpperCAmelCase__           =       0

  # Search not nearest vertex not in the shortest path tree
  for vertex in range(self.vertices				):
   if distances_from_source[vertex] < minimum and visited[vertex] is False:
    UpperCAmelCase__           =       distances_from_source[vertex]
    UpperCAmelCase__           =       vertex
  return min_index




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       [int(1E7				)] * self.vertices  # distances from the source
  UpperCAmelCase__           =       0
  UpperCAmelCase__           =       [False] * self.vertices

  for _ in range(self.vertices				):
   UpperCAmelCase__           =       self.minimum_distance(__a , __a				)
   UpperCAmelCase__           =       True

   # Update dist value of the adjacent vertices
   # of the picked vertex only if the current
   # distance is greater than new distance and
   # the vertex in not in the shortest path tree
   for v in range(self.vertices				):
    if (
        self.graph[u][v] > 0
        and visited[v] is False
        and distances[v] > distances[u] + self.graph[u][v]
    ):
     UpperCAmelCase__           =       distances[u] + self.graph[u][v]

  self.print_solution(__a				)


if __name__ == "__main__":
 _UpperCamelCase     =    Graph(9)
 _UpperCamelCase     =    [
     [0, 4, 0, 0, 0, 0, 0, 8, 0],
     [4, 0, 8, 0, 0, 0, 0, 11, 0],
     [0, 8, 0, 7, 0, 4, 0, 0, 2],
     [0, 0, 7, 0, 9, 14, 0, 0, 0],
     [0, 0, 0, 9, 0, 10, 0, 0, 0],
     [0, 0, 4, 14, 10, 0, 2, 0, 0],
     [0, 0, 0, 0, 0, 2, 0, 1, 6],
     [8, 11, 0, 0, 0, 0, 1, 0, 7],
     [0, 0, 2, 0, 0, 0, 6, 7, 0],
 ]
 graph.dijkstra(0)



