






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 LevitImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Dict=7					, UpperCAmelCase_       :	Any=3					, UpperCAmelCase_       :	Dict=18					, UpperCAmelCase_       :	List[str]=30					, UpperCAmelCase_       :	List[str]=400					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Optional[int]=None					, UpperCAmelCase_       :	List[str]=True					, UpperCAmelCase_       :	Dict=None					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Tuple=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	List[Any]=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    size if size is not None else {"shortest_edge": 18}
       SCREAMING_SNAKE_CASE       :      List[str]							=    crop_size if crop_size is not None else {"height": 18, "width": 18}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    parent
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    batch_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Any							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size
       SCREAMING_SNAKE_CASE       :      Any							=    do_center_crop
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    crop_size
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_mean
       SCREAMING_SNAKE_CASE       :      Dict							=    image_std






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





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	LevitImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    LevitImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Optional[Any]      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_center_crop"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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       :      str							=    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       :	Tuple      ):
       pass


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

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Optional[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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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 				_A       (				self       :	str      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    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
       SCREAMING_SNAKE_CASE       :      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
       SCREAMING_SNAKE_CASE       :      str							=    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 				_A       (				self       :	Any      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      List[str]							=    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
       SCREAMING_SNAKE_CASE       :      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
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    [0] * len(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    []
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
  SCREAMING_SNAKE_CASE       :      Any							=    0

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

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

  while queue:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    queue.pop(0				)
    cnt += 1
    topo.append(lowercase				)
    for x in graph[vertex]:
      indegree[x] -= 1
      if indegree[x] == 0:
        queue.append(lowercase				)

  if cnt != len(lowercase				):
    print("Cycle exists"				)
  else:
    print(lowercase				)


# Adjacency List of Graph
snake_case    				=		{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)






