



"""simple docstring"""







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def __init__(      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=7    ,			SCREAMING_SNAKE_CASE_				:      str=3    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=1_8    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3_0    ,			SCREAMING_SNAKE_CASE_				:      str=4_0_0    ,			SCREAMING_SNAKE_CASE_				:      int=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=[0.5, 0.5, 0.5]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=[0.5, 0.5, 0.5]    ,			):
        lowerCAmelCase_   :				Optional[Any]								= size if size is not None else {'height': 1_8, 'width': 1_8}
        lowerCAmelCase_   :				Tuple								= parent
        lowerCAmelCase_   :				int								= batch_size
        lowerCAmelCase_   :				int								= num_channels
        lowerCAmelCase_   :				str								= image_size
        lowerCAmelCase_   :				Tuple								= min_resolution
        lowerCAmelCase_   :				List[Any]								= max_resolution
        lowerCAmelCase_   :				Dict								= do_resize
        lowerCAmelCase_   :				Union[str, Any]								= size
        lowerCAmelCase_   :				Tuple								= do_normalize
        lowerCAmelCase_   :				Any								= image_mean
        lowerCAmelCase_   :				List[Any]								= image_std





      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }







@require_torch
@require_vision
class       UpperCamelCase__   (	lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     DPTImageProcessor if is_vision_available() else None



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Optional[Any]								= DPTImageProcessingTester(self       )



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        return self.image_processor_tester.prepare_image_processor_dict()



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Any								= self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'image_mean'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'image_std'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'do_normalize'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'do_resize'       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,			'size'       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Tuple								= self.image_processing_class.from_dict(self.image_processor_dict       )
        self.assertEqual(image_processor.size    ,			{'height': 1_8, 'width': 1_8}       )

        lowerCAmelCase_   :				Optional[int]								= self.image_processing_class.from_dict(self.image_processor_dict    ,			size=4_2       )
        self.assertEqual(image_processor.size    ,			{'height': 4_2, 'width': 4_2}       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        # Initialize image_processing
        lowerCAmelCase_   :				Union[str, Any]								= self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        lowerCAmelCase_   :				Optional[int]								= prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			Image.Image       )

        # Test not batched input
        lowerCAmelCase_   :				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.size['height'],
                self.image_processor_tester.size['width'],
            )    ,			)

        # Test batched
        lowerCAmelCase_   :				Tuple								= image_processing(SCREAMING_SNAKE_CASE_    ,			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.size['height'],
                self.image_processor_tester.size['width'],
            )    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        # Initialize image_processing
        lowerCAmelCase_   :				List[Any]								= self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        lowerCAmelCase_   :				int								= prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=SCREAMING_SNAKE_CASE_    ,			numpify=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			np.ndarray       )

        # Test not batched input
        lowerCAmelCase_   :				Any								= 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.size['height'],
                self.image_processor_tester.size['width'],
            )    ,			)

        # Test batched
        lowerCAmelCase_   :				Optional[Any]								= image_processing(SCREAMING_SNAKE_CASE_    ,			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.size['height'],
                self.image_processor_tester.size['width'],
            )    ,			)





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        # Initialize image_processing
        lowerCAmelCase_   :				Optional[int]								= self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        lowerCAmelCase_   :				Optional[int]								= prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=SCREAMING_SNAKE_CASE_    ,			torchify=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			torch.Tensor       )

        # Test not batched input
        lowerCAmelCase_   :				List[Any]								= 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.size['height'],
                self.image_processor_tester.size['width'],
            )    ,			)

        # Test batched
        lowerCAmelCase_   :				Any								= image_processing(SCREAMING_SNAKE_CASE_    ,			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.size['height'],
                self.image_processor_tester.size['width'],
            )    ,			)










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

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

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

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

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

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





