


'''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 		UpperCAmelCase_      (  unittest.TestCase		):





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





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[Any]:
        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 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Dict		=     ViTImageProcessor if is_vision_available() else None





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        lowerCAmelCase     =			EfficientFormerImageProcessorTester(self    )





     @property
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  str:
        return self.image_proc_tester.prepare_image_processor_dict()





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'image_mean'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'image_std'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_normalize'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_resize'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'size'    )    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        pass





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Optional[int]:
        # Initialize image_processor
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowerCAmelCase     =			prepare_image_inputs(self.image_proc_tester    ,      equal_resolution=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      Image.Image    )

        # Test not batched input
        lowerCAmelCase     =			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
        lowerCAmelCase     =			image_processor(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        # Initialize image_processor
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowerCAmelCase     =			prepare_image_inputs(self.image_proc_tester    ,      equal_resolution=UpperCAmelCase__    ,      numpify=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      np.ndarray    )

        # Test not batched input
        lowerCAmelCase     =			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
        lowerCAmelCase     =			image_processor(UpperCAmelCase__    ,      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 							__UpperCAmelCase      ( self       :      List[Any]    )				->  int:
        # Initialize image_processor
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowerCAmelCase     =			prepare_image_inputs(self.image_proc_tester    ,      equal_resolution=UpperCAmelCase__    ,      torchify=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      torch.Tensor    )

        # Test not batched input
        lowerCAmelCase     =			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
        lowerCAmelCase     =			image_processor(UpperCAmelCase__    ,      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							)		->   List[str]:
        _UpperCAmelCase  :						int			     = 0
        _UpperCAmelCase  :						Union[str, Any]			     = 0
        _UpperCAmelCase  :						Optional[int]			     = {}







 def _snake_case (							self  ,a_							)		->   Optional[Any]:
        if vertex not in self.adjacency:
               _UpperCAmelCase  :						int			     = {}
               self.num_vertices += 1







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   int:
        self.add_vertex(a_							)
        self.add_vertex(a_							)

        if head == tail:
               return

        _UpperCAmelCase  :						List[Any]			     = weight
        _UpperCAmelCase  :						Dict			     = weight







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Optional[int]			     = self.get_edges()
        for edge in edges:
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = edge
               edges.remove((tail, head, weight)							)
        for i in range(len(a_							)							):
               _UpperCAmelCase  :						str			     = list(edges[i]							)

        edges.sort(key=lambda a_							: e[2]							)
        for i in range(len(a_							) - 1							):
               if edges[i][2] >= edges[i + 1][2]:
                      _UpperCAmelCase  :						Optional[Any]			     = edges[i][2] + 1
        for edge in edges:
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = edge
               _UpperCAmelCase  :						str			     = weight
               _UpperCAmelCase  :						List[str]			     = weight







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







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						int			     = []
        for tail in self.adjacency:
               for head in self.adjacency[tail]:
                      output.append((tail, head, self.adjacency[head][tail])							)
        return output







 def _snake_case (							self							)		->   Optional[int]:
        return self.adjacency.keys()







 @staticmethod
 def _snake_case (							a_=None  ,a_=None							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = Graph()
        if vertices is None:
               _UpperCAmelCase  :						List[str]			     = []
        if edges is None:
               _UpperCAmelCase  :						Optional[Any]			     = []
        for vertex in vertices:
               g.add_vertex(a_							)
        for edge in edges:
               g.add_edge(*a_							)
        return g



 class 							lowercase       :


  """simple docstring"""


  def __init__(							self							)		->   int:
         _UpperCAmelCase  :						List[str]			     = {}
         _UpperCAmelCase  :						int			     = {}







  def __len__(							self							)		->   Tuple:
         return len(self.parent							)







  def _snake_case (							self  ,a_							)		->   str:
         if item in self.parent:
                return self.find(a_							)

         _UpperCAmelCase  :						Optional[Any]			     = item
         _UpperCAmelCase  :						List[Any]			     = 0
         return item







  def _snake_case (							self  ,a_							)		->   List[str]:
         if item not in self.parent:
                return self.make_set(a_							)
         if item != self.parent[item]:
                _UpperCAmelCase  :						List[Any]			     = self.find(self.parent[item]							)
         return self.parent[item]







  def _snake_case (							self  ,a_  ,a_							)		->   Union[str, Any]:
         _UpperCAmelCase  :						Any			     = self.find(a_							)
         _UpperCAmelCase  :						List[str]			     = self.find(a_							)

         if roota == roota:
                return roota

         if self.rank[roota] > self.rank[roota]:
                _UpperCAmelCase  :						Any			     = roota
                return roota

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

         if self.rank[roota] == self.rank[roota]:
                self.rank[roota] += 1
                _UpperCAmelCase  :						List[str]			     = roota
                return roota
         return None







 @staticmethod
 def _snake_case (							a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = graph.num_vertices

        _UpperCAmelCase  :						int			     = Graph.UnionFind()
        _UpperCAmelCase  :						Optional[int]			     = []
        while num_components > 1:
               _UpperCAmelCase  :						int			     = {}
               for vertex in graph.get_vertices():
                      _UpperCAmelCase  :						Union[str, Any]			     = -1

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

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



