





'''simple docstring'''



class      A_			:  # Public class to implement a graph

			'''simple docstring'''





			def __init__(       self      ,  A_      ,  A_      ,  A_ ):
								_UpperCamelCase									= row
								_UpperCamelCase									= col
								_UpperCamelCase									= graph




			def    a						(       self      ,  A_      ,  A_      ,  A_ ):
								return (
								    0 <= i < self.ROW
								    and 0 <= j < self.COL
								    and not visited[i][j]
								    and self.graph[i][j]
								)




			def    a						(       self      ,  A_      ,  A_      ,  A_ ):
								_UpperCamelCase									= [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
								_UpperCamelCase									= [-1, 0, 1, -1, 1, -1, 0, 1]
								_UpperCamelCase									= True  # Make those cells visited
								for k in range(8 ):
													if self.is_safe(i + row_nbr[k]      ,  j + col_nbr[k]      ,  UpperCAmelCase__ ):
																		self.diffs(i + row_nbr[k]      ,  j + col_nbr[k]      ,  UpperCAmelCase__ )




			def    a						(       self ):  # And finally, count all islands.
								_UpperCamelCase									= [[False for j in range(self.COL )] for i in range(self.ROW )]
								_UpperCamelCase									= 0
								for i in range(self.ROW ):
													for j in range(self.COL ):
																		if visited[i][j] is False and self.graph[i][j] == 1:
																							self.diffs(UpperCAmelCase__      ,  UpperCAmelCase__      ,  UpperCAmelCase__ )
																							count += 1
								return count









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	):

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int=13   ,					UpperCAmelCase__ :      Union[str, Any]=3   ,					UpperCAmelCase__ :      str=224   ,					UpperCAmelCase__ :      str=30   ,					UpperCAmelCase__ :      Tuple=400   ,					UpperCAmelCase__ :      int=True   ,					UpperCAmelCase__ :      Dict=None   ,					UpperCAmelCase__ :      List[Any]=True   ,					UpperCAmelCase__ :      Union[str, Any]=[0.5, 0.5, 0.5]   ,					UpperCAmelCase__ :      Tuple=[0.5, 0.5, 0.5]   ,					)		->str:





           '''simple docstring'''

           A__ 					=       size if size is not None else {'''height''': 18, '''width''': 18}
           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       num_channels
           A__ 					=       image_size
           A__ 					=       min_resolution
           A__ 					=       max_resolution
           A__ 					=       do_resize
           A__ 					=       size
           A__ 					=       do_normalize
           A__ 					=       image_mean
           A__ 					=       image_std





    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Optional[int]:





           '''simple docstring'''

           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_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = ViTImageProcessor if is_vision_available() else None


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





           '''simple docstring'''

           A__ 					=       EfficientFormerImageProcessorTester(self)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           return self.image_proc_tester.prepare_image_processor_dict()


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Dict:





           '''simple docstring'''

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Dict:





           '''simple docstring'''

           pass


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					Image.Image)

           # Test not batched input
           A__ 					=       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
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__ 					=       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
           A__ 					=       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
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__ 					=       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
           A__ 					=       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
           A__ 					=       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'''],
               )   ,					)


