'''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 ViTImageProcessor






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=1_3     ,			_lowerCamelCase=3     ,			_lowerCamelCase=2_2_4     ,			_lowerCamelCase=3_0     ,			_lowerCamelCase=4_0_0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=[0.5, 0.5, 0.5]     ,			_lowerCamelCase=[0.5, 0.5, 0.5]     ,			):
         __UpperCamelCase	:	List[Any]		    =  size if size is not None else {'height': 1_8, 'width': 1_8}
         __UpperCamelCase	:	List[Any]		    =  parent
         __UpperCamelCase	:	int		    =  batch_size
         __UpperCamelCase	:	Dict		    =  num_channels
         __UpperCamelCase	:	Any		    =  image_size
         __UpperCamelCase	:	str		    =  min_resolution
         __UpperCamelCase	:	str		    =  max_resolution
         __UpperCamelCase	:	Optional[int]		    =  do_resize
         __UpperCamelCase	:	str		    =  size
         __UpperCamelCase	:	Optional[int]		    =  do_normalize
         __UpperCamelCase	:	Optional[int]		    =  image_mean
         __UpperCamelCase	:	Optional[int]		    =  image_std







     def lowerCAmelCase     (		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 			__lowercase		(						__lowercase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						ViTImageProcessor if is_vision_available() else None







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  EfficientFormerImageProcessorTester(self    )







     @property
     def lowerCAmelCase     (		self    ):
         return self.image_proc_tester.prepare_image_processor_dict()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__A     ,			'image_mean'    )    )
         self.assertTrue(hasattr(__A     ,			'image_std'    )    )
         self.assertTrue(hasattr(__A     ,			'do_normalize'    )    )
         self.assertTrue(hasattr(__A     ,			'do_resize'    )    )
         self.assertTrue(hasattr(__A     ,			'size'    )    )







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         # Initialize image_processor
         __UpperCamelCase	:	Tuple		    =  self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         __UpperCamelCase	:	Dict		    =  prepare_image_inputs(self.image_proc_tester     ,			equal_resolution=__A    )
         for image in image_inputs:
             self.assertIsInstance(__A     ,			Image.Image    )

         # Test not batched input
         __UpperCamelCase	:	List[str]		    =  image_processor(image_inputs[0]     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,			(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['height'],
                 self.image_proc_tester.size['width'],
             )     ,			)

         # Test batched
         __UpperCamelCase	:	Optional[Any]		    =  image_processor(__A     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,			(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['height'],
                 self.image_proc_tester.size['width'],
             )     ,			)







     def lowerCAmelCase     (		self    ):
         # Initialize image_processor
         __UpperCamelCase	:	Optional[Any]		    =  self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         __UpperCamelCase	:	Dict		    =  prepare_image_inputs(self.image_proc_tester     ,			equal_resolution=__A     ,			numpify=__A    )
         for image in image_inputs:
             self.assertIsInstance(__A     ,			np.ndarray    )

         # Test not batched input
         __UpperCamelCase	:	Union[str, Any]		    =  image_processor(image_inputs[0]     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,			(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['height'],
                 self.image_proc_tester.size['width'],
             )     ,			)

         # Test batched
         __UpperCamelCase	:	Tuple		    =  image_processor(__A     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,			(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['height'],
                 self.image_proc_tester.size['width'],
             )     ,			)







     def lowerCAmelCase     (		self    ):
         # Initialize image_processor
         __UpperCamelCase	:	Optional[int]		    =  self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         __UpperCamelCase	:	List[Any]		    =  prepare_image_inputs(self.image_proc_tester     ,			equal_resolution=__A     ,			torchify=__A    )
         for image in image_inputs:
             self.assertIsInstance(__A     ,			torch.Tensor    )

         # Test not batched input
         __UpperCamelCase	:	Tuple		    =  image_processor(image_inputs[0]     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,			(
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['height'],
                 self.image_proc_tester.size['width'],
             )     ,			)

         # Test batched
         __UpperCamelCase	:	str		    =  image_processor(__A     ,			return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,			(
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['height'],
                 self.image_proc_tester.size['width'],
             )     ,			)




'''simple docstring'''
class 			__lowercase		:







     """simple docstring"""






     def __init__(		self    ):
         __UpperCamelCase	:	Any		    =  0
         __UpperCamelCase	:	Any		    =  0
         __UpperCamelCase	:	Any		    =  {}







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         if vertex not in self.adjacency:
             __UpperCamelCase	:	Tuple		    =  {}
             self.num_vertices += 1







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         self.add_vertex(_lowerCamelCase    )
         self.add_vertex(_lowerCamelCase    )

         if head == tail:
             return

         __UpperCamelCase	:	List[Any]		    =  weight
         __UpperCamelCase	:	Optional[int]		    =  weight







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  self.get_edges()
         for edge in edges:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  edge
             edges.remove((tail, head, weight)    )
         for i in range(len(_lowerCamelCase    )    ):
             __UpperCamelCase	:	Union[str, Any]		    =  list(edges[i]    )

         edges.sort(key=lambda _lowerCamelCase    : e[2]    )
         for i in range(len(_lowerCamelCase    ) - 1    ):
             if edges[i][2] >= edges[i + 1][2]:
                 __UpperCamelCase	:	Optional[int]		    =  edges[i][2] + 1
         for edge in edges:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  edge
             __UpperCamelCase	:	Tuple		    =  weight
             __UpperCamelCase	:	Optional[Any]		    =  weight







     def __str__(		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  ''
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 __UpperCamelCase	:	Tuple		    =  self.adjacency[head][tail]
                 string += f"""{head} -> {tail} == {weight}\n"""
         return string.rstrip('\n'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  []
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 output.append((tail, head, self.adjacency[head][tail])    )
         return output







     def lowerCAmelCase     (		self    ):
         return self.adjacency.keys()







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase=None     ,			_lowerCamelCase=None    ):
         __UpperCamelCase	:	Tuple		    =  Graph()
         if vertices is None:
             __UpperCamelCase	:	List[Any]		    =  []
         if edges is None:
             __UpperCamelCase	:	Optional[int]		    =  []
         for vertex in vertices:
             g.add_vertex(_lowerCamelCase    )
         for edge in edges:
             g.add_edge(*_lowerCamelCase    )
         return g






     class 			__lowercase		:







          """simple docstring"""






          def __init__(		self    ):
              __UpperCamelCase	:	Union[str, Any]		    =  {}
              __UpperCamelCase	:	int		    =  {}







          def __len__(		self    ):
              return len(self.parent    )







          def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
              if item in self.parent:
                  return self.find(_lowerCamelCase    )

              __UpperCamelCase	:	Optional[Any]		    =  item
              __UpperCamelCase	:	List[str]		    =  0
              return item







          def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
              if item not in self.parent:
                  return self.make_set(_lowerCamelCase    )
              if item != self.parent[item]:
                  __UpperCamelCase	:	int		    =  self.find(self.parent[item]    )
              return self.parent[item]







          def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
              __UpperCamelCase	:	Tuple		    =  self.find(_lowerCamelCase    )
              __UpperCamelCase	:	Any		    =  self.find(_lowerCamelCase    )

              if roota == roota:
                  return roota

              if self.rank[roota] > self.rank[roota]:
                  __UpperCamelCase	:	Dict		    =  roota
                  return roota

              if self.rank[roota] < self.rank[roota]:
                  __UpperCamelCase	:	Optional[Any]		    =  roota
                  return roota

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







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  graph.num_vertices

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

             __UpperCamelCase	:	int		    =  graph.get_edges()
             for edge in edges:
                 __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[str]		    =  edge
                 edges.remove((tail, head, weight)    )
             for edge in edges:
                 __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Dict		    =  edge
                 __UpperCamelCase	:	List[Any]		    =  union_find.find(_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  union_find.find(_lowerCamelCase    )
                 if seta != seta:
                     if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                         __UpperCamelCase	:	str		    =  [head, tail, weight]

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



