






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 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def __init__(  self:   Optional[Any]		,     __A:   Dict		,     __A:   List[str]=13		,     __A:   Any=3		,     __A:   List[Any]=2_24		,     __A:   List[str]=30		,     __A:   List[str]=4_00		,     __A:   Optional[int]=True		,     __A:   Optional[Any]=None		,     __A:   Union[str, Any]=True		,     __A:   List[str]=[0.5, 0.5, 0.5]		,     __A:   Optional[Any]=[0.5, 0.5, 0.5]		,     )    ->				Optional[int]:
												_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       __A (  self:   Any   )    ->				Optional[int]:
												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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					ViTImageProcessor if is_vision_available() else None

							def       __A (  self:   List[Any]   )    ->				Optional[int]:
												_A								= EfficientFormerImageProcessorTester(self   )

							@property
							def       __A (  self:   int   )    ->				Tuple:
												return self.image_proc_tester.prepare_image_processor_dict()

							def       __A (  self:   Union[str, Any]   )    ->				str:
												_A								= 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       __A (  self:   List[Any]   )    ->				int:
												pass

							def       __A (  self:   Optional[Any]   )    ->				Tuple:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_proc_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     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(__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       __A (  self:   List[Any]   )    ->				Optional[int]:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												_A								= 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
												_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(__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       __A (  self:   List[Any]   )    ->				Any:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												_A								= 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
												_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(__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'''],
												    )		,     )











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



