


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 		_snake_case  (      unittest.TestCase							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=7					,  _lowerCamelCase=3					,  _lowerCamelCase=18					,  _lowerCamelCase=30					,  _lowerCamelCase=400					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  _lowerCamelCase=True					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  ):
       a						:Optional[int] 							=       size if size is not None else {'''height''': 18, '''width''': 18}
       a						:int 							=       parent
       a						:str 							=       batch_size
       a						:Optional[int] 							=       num_channels
       a						:int 							=       image_size
       a						:Tuple 							=       min_resolution
       a						:Dict 							=       max_resolution
       a						:Optional[Any] 							=       do_resize
       a						:Dict 							=       size
       a						:int 							=       do_normalize
       a						:Any 							=       image_mean
       a						:Optional[int] 							=       image_std




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       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 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       DPTImageProcessor if is_vision_available() else None




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




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_mean'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_std'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_normalize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_resize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''size'''    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}    )

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




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

       # Test not batched input
       a						:Tuple 							=       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
       a						:Union[str, Any] 							=       image_processing(_lowerCamelCase					,  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    ):
       # Initialize image_processing
       a						:Union[str, Any] 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       a						:Optional[int] 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase					,  numpify=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  np.ndarray    )

       # Test not batched input
       a						: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
       a						:Any 							=       image_processing(_lowerCamelCase					,  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    ):
       # Initialize image_processing
       a						:Any 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       a						:Any 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase					,  torchify=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  torch.Tensor    )

       # Test not batched input
       a						: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
       a						:Dict 							=       image_processing(_lowerCamelCase					,  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'''],
           )					,  )









# using dfs for finding eulerian path traversal




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




def 	a_      (    ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


