"""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 __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    int	,     _lowercase      :    Any	,     _lowercase      :    List[str]=7	,     _lowercase      :    Dict=3	,     _lowercase      :    Dict=10	,     _lowercase      :    Union[str, Any]=18	,     _lowercase      :    Dict=30	,     _lowercase      :    List[str]=4_00	,     _lowercase      :    Tuple=True	,     _lowercase      :    int=None	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Union[str, Any]=[0.5, 0.5, 0.5]	,     _lowercase      :    List[Any]=[0.5, 0.5, 0.5]	,     _lowercase      :    List[Any]=None	,     ):



     """simple docstring"""






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

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




   def      __a						(   self      :    Optional[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,
         "crop_size": self.crop_size,
     }







@require_torch
@require_vision
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							VivitImageProcessor if is_vision_available() else None




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				VivitImageProcessingTester(self				)




   @property
   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size	,     {"""shortest_edge""": 18}				)
     self.assertEqual(image_processor.crop_size	,     {"""height""": 18, """width""": 18}				)

     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict	,     size=42	,     crop_size=84				)
     self.assertEqual(image_processor.size	,     {"""shortest_edge""": 42}				)
     self.assertEqual(image_processor.crop_size	,     {"""height""": 84, """width""": 84}				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random PIL videos
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				image_processing(_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      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				image_processing(_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      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class(**self.image_processor_dict				)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				image_processing(_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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[0] * len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[1] * len(__UpperCamelCase     )

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(__UpperCamelCase     )     ):
    if indegree[i] == 0:
      queue.append(__UpperCamelCase     )

  while queue:
    SCREAMING_SNAKE_CASE__      							=				queue.pop(0     )
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        SCREAMING_SNAKE_CASE__      							=				long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(__UpperCamelCase     )

  print(max(__UpperCamelCase     )     )


# Adjacency list of Graph
__lowerCamelCase      :			int							       =		{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)
