





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 OwlViTImageProcessor

class 				A    (unittest.TestCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=True     ,  lowercase_=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,  lowercase_=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,  lowercase_=True     ,  )      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						parent
             _snake_case      :							Any     				=						batch_size
             _snake_case      :							List[str]     				=						num_channels
             _snake_case      :							Optional[Any]     				=						image_size
             _snake_case      :							Tuple     				=						min_resolution
             _snake_case      :							Any     				=						max_resolution
             _snake_case      :							Dict     				=						do_resize
             _snake_case      :							Dict     				=						size if size is not None else {'''height''': 18, '''width''': 18}
             _snake_case      :							Dict     				=						do_center_crop
             _snake_case      :							Optional[int]     				=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
             _snake_case      :							int     				=						do_normalize
             _snake_case      :							List[str]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std
             _snake_case      :							Optional[Any]     				=						do_convert_rgb



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_convert_rgb": self.do_convert_rgb,
             }


@require_torch
@require_vision
class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							OwlViTImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Any     				=						OwlViTImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_resize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''size'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_center_crop'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''center_crop'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_normalize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_mean'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_std'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_convert_rgb'''					)					)



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



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''height''': 18, '''width''': 18}					)
             self.assertEqual(image_processor.crop_size     ,  {'''height''': 18, '''width''': 18}					)

             _snake_case      :							str     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42     ,  crop_size=84					)
             self.assertEqual(image_processor.size     ,  {'''height''': 42, '''width''': 42}					)
             self.assertEqual(image_processor.crop_size     ,  {'''height''': 84, '''width''': 84}					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)

             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

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

             # Test batched
             _snake_case      :							Any     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )



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



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

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

             # Test batched
             _snake_case      :							List[Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							List[Any]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

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

             # Test batched
             _snake_case      :							Any     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )













from __future__ import annotations

class 				A    :



      def __init__(					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						vertices
             _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

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



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



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						1E7
             _snake_case      :							Tuple     				=						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:
                           _snake_case      :							Dict     				=						distances_from_source[vertex]
                           _snake_case      :							int     				=						vertex
             return min_index



      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
             _snake_case      :							List[Any]     				=						0
             _snake_case      :							List[str]     				=						[False] * self.vertices

             for _ in range(self.vertices					):
                    _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                    _snake_case      :							List[str]     				=						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]
                           ):
                                  _snake_case      :							Any     				=						distances[u] + self.graph[u][v]

             self.print_solution(lowercase_					)


if __name__ == "__main__":
   lowerCAmelCase_   =   Graph(9)
   lowerCAmelCase_   =   [
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
       [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
       [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)






