

class       __magic_name__       :





			def __init__(						self   )  ->					Tuple:




						'''simple docstring'''
						__a     =0
						__a     =0
						__a     ={}





			def 						__magic_name__       (						self   ,    __snake_case   )  ->					List[Any]:




						'''simple docstring'''
						if vertex not in self.adjacency:
									__a     ={}
									self.num_vertices += 1





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					Dict:




						'''simple docstring'''
						self.add_vertex(__a   )
						self.add_vertex(__a   )

						if head == tail:
									return

						__a     =weight
						__a     =weight





			def 						__magic_name__       (						self   )  ->					Optional[int]:




						'''simple docstring'''
						__a     =self.get_edges()
						for edge in edges:
									__a     =edge
									edges.remove((tail, head, weight)   )
						for i in range(len(__a   )   ):
									__a     =list(edges[i]   )

						edges.sort(key=lambda __snake_case   : e[2]   )
						for i in range(len(__a   ) - 1   ):
									if edges[i][2] >= edges[i + 1][2]:
												__a     =edges[i][2] + 1
						for edge in edges:
									__a     =edge
									__a     =weight
									__a     =weight





			def __str__(						self   )  ->					List[str]:




						'''simple docstring'''
						__a     =""
						for tail in self.adjacency:
									for head in self.adjacency[tail]:
												__a     =self.adjacency[head][tail]
												string += f'{head} -> {tail} == {weight}\n'
						return string.rstrip('\n'   )





			def 						__magic_name__       (						self   )  ->					Tuple:




						'''simple docstring'''
						__a     =[]
						for tail in self.adjacency:
									for head in self.adjacency[tail]:
												output.append((tail, head, self.adjacency[head][tail])   )
						return output





			def 						__magic_name__       (						self   )  ->					List[Any]:




						'''simple docstring'''
						return self.adjacency.keys()





			@staticmethod
			def 						__magic_name__       (						__snake_case=None   ,    __snake_case=None   )  ->					List[Any]:




						'''simple docstring'''
						__a     =Graph()
						if vertices is None:
									__a     =[]
						if edges is None:
									__a     =[]
						for vertex in vertices:
									g.add_vertex(__a   )
						for edge in edges:
									g.add_edge(*__a   )
						return g






			class       __magic_name__       :





						def __init__(						self   )  ->					Any:




									'''simple docstring'''
									__a     ={}
									__a     ={}





						def __len__(						self   )  ->					Optional[int]:




									'''simple docstring'''
									return len(self.parent   )





						def 						__magic_name__       (						self   ,    __snake_case   )  ->					Any:




									'''simple docstring'''
									if item in self.parent:
												return self.find(__a   )

									__a     =item
									__a     =0
									return item





						def 						__magic_name__       (						self   ,    __snake_case   )  ->					Tuple:




									'''simple docstring'''
									if item not in self.parent:
												return self.make_set(__a   )
									if item != self.parent[item]:
												__a     =self.find(self.parent[item]   )
									return self.parent[item]





						def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case   )  ->					List[Any]:




									'''simple docstring'''
									__a     =self.find(__a   )
									__a     =self.find(__a   )

									if roota == roota:
												return roota

									if self.rank[roota] > self.rank[roota]:
												__a     =roota
												return roota

									if self.rank[roota] < self.rank[roota]:
												__a     =roota
												return roota

									if self.rank[roota] == self.rank[roota]:
												self.rank[roota] += 1
												__a     =roota
												return roota
									return None





			@staticmethod
			def 						__magic_name__       (						__snake_case   )  ->					str:




						'''simple docstring'''
						__a     =graph.num_vertices

						__a     =Graph.UnionFind()
						__a     =[]
						while num_components > 1:
									__a     ={}
									for vertex in graph.get_vertices():
												__a     =-1

									__a     =graph.get_edges()
									for edge in edges:
												__a     =edge
												edges.remove((tail, head, weight)   )
									for edge in edges:
												__a     =edge
												__a     =union_find.find(__a   )
												__a     =union_find.find(__a   )
												if seta != seta:
															if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																		__a     =[head, tail, weight]

															if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																		__a     =[head, tail, weight]
									for vertex in cheap_edge:
												if cheap_edge[vertex] != -1:
															__a     =cheap_edge[vertex]
															if union_find.find(__a   ) != union_find.find(__a   ):
																		union_find.union(__a   ,    __a   )
																		mst_edges.append(cheap_edge[vertex]   )
																		__a     =num_components - 1
						__a     =Graph.build(edges=__a   )
						return mst













"""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   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							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  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							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  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							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						)
