

"""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_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def __init__(     self: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: Union[str, Any]=7    ,						UpperCamelCase: Optional[Any]=3    ,						UpperCamelCase: int=18    ,						UpperCamelCase: int=30    ,						UpperCamelCase: List[str]=4_00    ,						UpperCamelCase: Optional[int]=True    ,						UpperCamelCase: str=None    ,						UpperCamelCase: str=True    ,						UpperCamelCase: Union[str, Any]=None    ,						):






         """simple docstring"""


         A__										=  size if size is not None else {"""shortest_edge""": 20}
         A__										=  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         A__										=  parent
         A__										=  batch_size
         A__										=  num_channels
         A__										=  image_size
         A__										=  min_resolution
         A__										=  max_resolution
         A__										=  do_resize
         A__										=  size
         A__										=  do_center_crop
         A__										=  crop_size





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






         """simple docstring"""


         return {
             "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 					a						(       _lowerCamelCase,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MobileNetVaImageProcessor if is_vision_available() else None

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  MobileNetVaImageProcessingTester(self       )

    @property
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         return self.image_processor_tester.prepare_image_processor_dict()

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(UpperCamelCase    ,						"""do_resize"""       )       )
         self.assertTrue(hasattr(UpperCamelCase    ,						"""size"""       )       )
         self.assertTrue(hasattr(UpperCamelCase    ,						"""do_center_crop"""       )       )
         self.assertTrue(hasattr(UpperCamelCase    ,						"""crop_size"""       )       )

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         A__										=  self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size    ,						{"""shortest_edge""": 20}       )
         self.assertEqual(image_processor.crop_size    ,						{"""height""": 18, """width""": 18}       )

         A__										=  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 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         pass

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


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

         # Test not batched input
         A__										=  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
         A__										=  image_processing(UpperCamelCase    ,						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 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


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

         # Test not batched input
         A__										=  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
         A__										=  image_processing(UpperCamelCase    ,						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 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


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

         # Test not batched input
         A__										=  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
         A__										=  image_processing(UpperCamelCase    ,						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"""],
             )    ,						)









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [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:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

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

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

     print(max(UpperCAmelCase_			)			)


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

longest_distance(graph)






