





'''simple docstring'''


def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any )							->     str:

       """simple docstring"""


       UpperCAmelCase  =						[0] * len(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						[]
       UpperCAmelCase  =						[]
       UpperCAmelCase  =						0

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

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

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

       if cnt != len(SCREAMING_SNAKE_CASE_ ):
              print('''Cycle exists''' )
       else:
              print(SCREAMING_SNAKE_CASE_ )


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

topological_sort(graph)









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor

class   lowerCAmelCase				(   unittest.TestCase     ):
 def __init__( self    : Any     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Dict=7     ,	UpperCAmelCase    : Union[str, Any]=3     ,	UpperCAmelCase    : List[str]=18     ,	UpperCAmelCase    : Dict=30     ,	UpperCAmelCase    : List[Any]=400     ,	UpperCAmelCase    : Optional[Any]=True     ,	UpperCAmelCase    : Tuple=None     ,	UpperCAmelCase    : str=True     ,	UpperCAmelCase    : Tuple=False     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Optional[int]=True     ,	UpperCAmelCase    : Optional[Any]=[0.5, 0.5, 0.5]     ,	UpperCAmelCase    : List[str]=[0.5, 0.5, 0.5]     ,	)     ->      List[str]:
     lowerCamelCase__       : List[Any]   					=						parent
     lowerCamelCase__       : str   					=						batch_size
     lowerCamelCase__       : List[str]   					=						num_channels
     lowerCamelCase__       : str   					=						image_size
     lowerCamelCase__       : List[Any]   					=						min_resolution
     lowerCamelCase__       : int   					=						max_resolution
     lowerCamelCase__       : int   					=						do_resize
     lowerCamelCase__       : int   					=						size if size is not None else {'height': 18, 'width': 20}
     lowerCamelCase__       : Tuple   					=						do_thumbnail
     lowerCamelCase__       : str   					=						do_align_axis
     lowerCamelCase__       : str   					=						do_pad
     lowerCamelCase__       : Optional[Any]   					=						do_normalize
     lowerCamelCase__       : List[str]   					=						image_mean
     lowerCamelCase__       : Dict   					=						image_std







 def 	A_      ( self    : str      )     ->      Any:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }


@require_torch
@require_vision
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						DonutImageProcessor if is_vision_available() else None
 def 	A_      ( self    : List[Any]      )     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						DonutImageProcessingTester(self      )
 @property
 def 	A_      ( self    : Dict      )     ->      Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def 	A_      ( self    : Dict      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_resize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'size'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_thumbnail'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_align_long_axis'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_pad'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_normalize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_mean'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_std'      )      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     lowerCamelCase__       : List[Any]   					=						self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size     ,	{'height': 18, 'width': 20}      )

     lowerCamelCase__       : int   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42      )
     self.assertEqual(image_processor.size     ,	{'height': 42, 'width': 42}      )

     # Previous config had dimensions in (width, height) order
     lowerCamelCase__       : Dict   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=(42, 84)      )
     self.assertEqual(image_processor.size     ,	{'height': 84, 'width': 42}      )
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     pass
 @is_flaky()
 def 	A_      ( self    : List[str]      )     ->      Any:
     # Initialize image_processing
     lowerCamelCase__       : str   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	Image.Image      )

     # 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__       : Tuple   					=						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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)
 @is_flaky()
 def 	A_      ( self    : int      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     lowerCamelCase__       : List[str]   					=						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
     lowerCamelCase__       : 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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)







 @is_flaky()
 def 	A_      ( self    : Any      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : int   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     lowerCamelCase__       : Tuple   					=						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
     lowerCamelCase__       : List[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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Dict   					=						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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)





