

import heapq




def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				Optional[Any]							):





  '''simple docstring'''







  a__						=[(0, start)]  # cost from start node,end node
  a__						=set()
  while heap:
    ((a__),   (a__))						=heapq.heappop(_A							)
    if u in visited:
      continue
    visited.add(_A							)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      a__						=cost + c
      heapq.heappush(_A			,   (next_item, v)							)
  return -1


lowerCamelCase     =      {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
lowerCamelCase     =      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
lowerCamelCase     =      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

lowerCamelCase     =      dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCamelCase     =      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

lowerCamelCase     =      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
       import doctest

       doctest.testmod()



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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import TvltImageProcessor




def     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				List[Any]=10			,   _A   :				Optional[int]=10			,   _A   :				Dict=False			,   _A   :				Any=False							):





  '''simple docstring'''







  a__						=[]
  for i in range(image_processor_tester.num_frames							):
    video.append(np.random.randint(2_55			,   size=(image_processor_tester.num_channels, width, height)			,   dtype=np.uinta							)							)

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    a__						=[Image.fromarray(np.moveaxis(_A			,   0			,   -1							)							) for frame in video]

  if torchify:
    a__						=[torch.from_numpy(_A							) for frame in video]

  return video




def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[int]=False			,   _A   :				Dict=False			,   _A   :				Optional[Any]=False							):





  '''simple docstring'''







  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  a__						=[]
  for i in range(image_processor_tester.batch_size							):
    if equal_resolution:
      a__						=a__						=image_processor_tester.max_resolution
    else:
      a__,   a__						=np.random.choice(
          np.arange(image_processor_tester.min_resolution			,   image_processor_tester.max_resolution							)			,   2							)
      a__						=prepare_video(
          image_processor_tester=_A			,   width=_A			,   height=_A			,   numpify=_A			,   torchify=_A			,   )
    video_inputs.append(_A							)

  return video_inputs







class 		__magic_name__    (							unittest.TestCase							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=4,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					lowercase_=True,					lowercase_=None,					)							->					Optional[int]:






     """simple docstring"""






     a__						=size if size is not None else {'''shortest_edge''': 18}
     a__						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

     a__						=parent
     a__						=batch_size
     a__						=num_channels
     a__						=num_frames
     a__						=image_size
     a__						=min_resolution
     a__						=max_resolution
     a__						=do_resize
     a__						=size
     a__						=do_normalize
     a__						=image_mean
     a__						=image_std
     a__						=do_center_crop
     a__						=crop_size



   def 				_UpperCAmelCase				(       self )							->					Any:






     """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,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
     }







@require_torch
@require_vision
class 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			str												=     TvltImageProcessor if is_vision_available() else None



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






     """simple docstring"""






     a__						=TvltImageProcessorTester(self )



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






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



   def 				_UpperCAmelCase				(       self )							->					str:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_center_crop''' ) )
     self.assertTrue(hasattr(lowercase_,					'''size''' ) )



   def 				_UpperCAmelCase				(       self )							->					Tuple:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PIL videos
     a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_ )
     for video in video_inputs:
       self.assertIsInstance(lowercase_,					lowercase_ )
       self.assertIsInstance(video[0],					Image.Image )

     # Test not batched input
     a__						=image_processor(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
     a__						=image_processor(lowercase_,					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 				_UpperCAmelCase				(       self )							->					int:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					numpify=lowercase_ )
     for video in video_inputs:
       self.assertIsInstance(lowercase_,					lowercase_ )
       self.assertIsInstance(video[0],					np.ndarray )

     # Test not batched input
     a__						=image_processor(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
     a__						=image_processor(lowercase_,					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 				_UpperCAmelCase				(       self )							->					int:






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     a__						=prepare_video_inputs(self.image_processor_tester,					equal_resolution=lowercase_,					torchify=lowercase_ )
     for video in video_inputs:
       self.assertIsInstance(lowercase_,					lowercase_ )
       self.assertIsInstance(video[0],					torch.Tensor )

     # Test not batched input
     a__						=image_processor(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
     a__						=image_processor(lowercase_,					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'''],
         ),					)
