


'''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 GLPNImageProcessor

class     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=7   ,   __lowercase=3   ,   __lowercase=18   ,   __lowercase=30   ,   __lowercase=400   ,   __lowercase=True   ,   __lowercase=32   ,   __lowercase=True   ,   ):

  """simple docstring"""







  __A	:		Optional[int]	  =      parent
  __A	:		int	  =      batch_size
  __A	:		int	  =      num_channels
  __A	:		Optional[int]	  =      image_size
  __A	:		Any	  =      min_resolution
  __A	:		int	  =      max_resolution
  __A	:		int	  =      do_resize
  __A	:		int	  =      size_divisor
  __A	:		int	  =      do_rescale







 def 			snake_case__ (   self    ):

  """simple docstring"""







  return {
      "do_resize": self.do_resize,
      "size_divisor": self.size_divisor,
      "do_rescale": self.do_rescale,
  }


@require_torch
@require_vision
class     _lowerCamelCase       (     snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    GLPNImageProcessor if is_vision_available() else None


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      GLPNImageProcessingTester(self    )


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.image_processor_tester.prepare_image_processor_dict()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.image_processing_class(**self.image_processor_dict    )
  self.assertTrue(hasattr(__lowercase   ,   'do_resize'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'size_divisor'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'resample'    )    )
  self.assertTrue(hasattr(__lowercase   ,   'do_rescale'    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  pass


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.image_processing_class(**self.image_processor_dict    )
  # create random PIL images
  __A	:		List[str]	  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=__lowercase    )
  for image in image_inputs:
   self.assertIsInstance(__lowercase   ,   Image.Image    )

  # Test not batched input (GLPNImageProcessor doesn't support batching)
  __A	:		List[str]	  =      image_processing(image_inputs[0]   ,   return_tensors='pt'    ).pixel_values
  self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
  self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  # Test not batched input (GLPNImageProcessor doesn't support batching)
  __A	:		Union[str, Any]	  =      image_processing(image_inputs[0]   ,   return_tensors='pt'    ).pixel_values
  self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
  self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  # Test not batched input (GLPNImageProcessor doesn't support batching)
  __A	:		str	  =      image_processing(image_inputs[0]   ,   return_tensors='pt'    ).pixel_values
  self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
  self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )










'''simple docstring'''
def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Dict	  =      set()
 # edges = list of graph's edges
 __A	:		Any	  =      get_edges(UpperCamelCase__      )

 # While there are still elements in edges list, take an arbitrary edge
 # (from_node, to_node) and add his extremity to chosen_vertices and then
 # remove all arcs adjacent to the from_node and to_node
 while edges:
  __A  ,__A	:		List[Any]	  =      edges.pop()
  chosen_vertices.add(UpperCamelCase__      )
  chosen_vertices.add(UpperCamelCase__      )
  for edge in edges.copy():
   if from_node in edge or to_node in edge:
    edges.discard(UpperCamelCase__      )
 return chosen_vertices


def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Any	  =      set()
 for from_node, to_nodes in graph.items():
  for to_node in to_nodes:
   edges.add((from_node, to_node)      )
 return edges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")






