






import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor



class 				__UpperCAmelCase		(unittest.TestCase   ):







			def __init__(     self:  Dict				,     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  Dict=7				,     UpperCAmelCase_:  Tuple=3				,     UpperCAmelCase_:  List[Any]=18				,     UpperCAmelCase_:  Dict=30				,     UpperCAmelCase_:  Dict=400				,     UpperCAmelCase_:  Any=True				,     UpperCAmelCase_:  List[Any]=None				,     UpperCAmelCase_:  int=True				,     UpperCAmelCase_:  List[Any]=None				,     UpperCAmelCase_:  Any=True				,     UpperCAmelCase_:  int=[0.5, 0.5, 0.5]				,     UpperCAmelCase_:  Optional[Any]=[0.5, 0.5, 0.5]				,     UpperCAmelCase_:  Optional[int]=False				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  size if size is not None else {"""height""": 20, """width""": 20}
									_SCREAMING_SNAKE_CASE     				=  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
									_SCREAMING_SNAKE_CASE     				=  parent
									_SCREAMING_SNAKE_CASE     				=  batch_size
									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  min_resolution
									_SCREAMING_SNAKE_CASE     				=  max_resolution
									_SCREAMING_SNAKE_CASE     				=  do_resize
									_SCREAMING_SNAKE_CASE     				=  size
									_SCREAMING_SNAKE_CASE     				=  do_center_crop
									_SCREAMING_SNAKE_CASE     				=  crop_size
									_SCREAMING_SNAKE_CASE     				=  do_normalize
									_SCREAMING_SNAKE_CASE     				=  image_mean
									_SCREAMING_SNAKE_CASE     				=  image_std
									_SCREAMING_SNAKE_CASE     				=  do_reduce_labels







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_reduce_labels": self.do_reduce_labels,
									}

def 	__lowerCamelCase      (		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  load_dataset("""hf-internal-testing/fixtures_ade20k"""	,split="""test"""		)

						_SCREAMING_SNAKE_CASE     				=  Image.open(dataset[0]["""file"""]		)
						_SCREAMING_SNAKE_CASE     				=  Image.open(dataset[1]["""file"""]		)

						return image, map

def 	__lowerCamelCase      (		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  load_dataset("""hf-internal-testing/fixtures_ade20k"""	,split="""test"""		)

						_SCREAMING_SNAKE_CASE     				=  Image.open(ds[0]["""file"""]		)
						_SCREAMING_SNAKE_CASE     				=  Image.open(ds[1]["""file"""]		)
						_SCREAMING_SNAKE_CASE     				=  Image.open(ds[2]["""file"""]		)
						_SCREAMING_SNAKE_CASE     				=  Image.open(ds[3]["""file"""]		)

						return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				List[Any]									=							BeitImageProcessor if is_vision_available() else None







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BeitImageProcessingTester(self					)







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									return self.image_processor_tester.prepare_image_processor_dict()







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """do_resize"""					)					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """size"""					)					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """do_center_crop"""					)					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """center_crop"""					)					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """do_normalize"""					)					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """image_mean"""					)					)
									self.assertTrue(hasattr(UpperCAmelCase_				,     """image_std"""					)					)







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class.from_dict(self.image_processor_dict					)
									self.assertEqual(image_processor.size				,     {"""height""": 20, """width""": 20}					)
									self.assertEqual(image_processor.crop_size				,     {"""height""": 18, """width""": 18}					)
									self.assertEqual(image_processor.do_reduce_labels				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class.from_dict(
									    self.image_processor_dict				,     size=42				,     crop_size=84				,     reduce_labels=UpperCAmelCase_					)
									self.assertEqual(image_processor.size				,     {"""height""": 42, """width""": 42}					)
									self.assertEqual(image_processor.crop_size				,     {"""height""": 84, """width""": 84}					)
									self.assertEqual(image_processor.do_reduce_labels				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									_SCREAMING_SNAKE_CASE     				=  prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_					)
									for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase_				,     Image.Image					)

									# Test not batched input
									_SCREAMING_SNAKE_CASE     				=  image_processing(image_inputs[0]				,     return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )

									# Test batched
									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									_SCREAMING_SNAKE_CASE     				=  prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     numpify=UpperCAmelCase_					)
									for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase_				,     np.ndarray					)

									# Test not batched input
									_SCREAMING_SNAKE_CASE     				=  image_processing(image_inputs[0]				,     return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )

									# Test batched
									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									_SCREAMING_SNAKE_CASE     				=  prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     torchify=UpperCAmelCase_					)
									for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor					)

									# Test not batched input
									_SCREAMING_SNAKE_CASE     				=  image_processing(image_inputs[0]				,     return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )

									# Test batched
									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									_SCREAMING_SNAKE_CASE     				=  prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     torchify=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  []
									for image in image_inputs:
															self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor					)
															maps.append(torch.zeros(image.shape[-2:]					).long()					)

									# Test not batched input
									_SCREAMING_SNAKE_CASE     				=  image_processing(image_inputs[0]				,     maps[0]				,     return_tensors="""pt"""					)
									self.assertEqual(
									    encoding["""pixel_values"""].shape				,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(
									    encoding["""labels"""].shape				,     (
									        1,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(encoding["""labels"""].dtype				,     torch.long					)
									self.assertTrue(encoding["""labels"""].min().item() >= 0					)
									self.assertTrue(encoding["""labels"""].max().item() <= 255					)

									# Test batched
									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     UpperCAmelCase_				,     return_tensors="""pt"""					)
									self.assertEqual(
									    encoding["""pixel_values"""].shape				,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(
									    encoding["""labels"""].shape				,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(encoding["""labels"""].dtype				,     torch.long					)
									self.assertTrue(encoding["""labels"""].min().item() >= 0					)
									self.assertTrue(encoding["""labels"""].max().item() <= 255					)

									# Test not batched input (PIL images)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  prepare_semantic_single_inputs()

									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     UpperCAmelCase_				,     return_tensors="""pt"""					)
									self.assertEqual(
									    encoding["""pixel_values"""].shape				,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(
									    encoding["""labels"""].shape				,     (
									        1,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(encoding["""labels"""].dtype				,     torch.long					)
									self.assertTrue(encoding["""labels"""].min().item() >= 0					)
									self.assertTrue(encoding["""labels"""].max().item() <= 255					)

									# Test batched input (PIL images)
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  prepare_semantic_batch_inputs()

									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     UpperCAmelCase_				,     return_tensors="""pt"""					)
									self.assertEqual(
									    encoding["""pixel_values"""].shape				,     (
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(
									    encoding["""labels"""].shape				,     (
									        2,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,     )
									self.assertEqual(encoding["""labels"""].dtype				,     torch.long					)
									self.assertTrue(encoding["""labels"""].min().item() >= 0					)
									self.assertTrue(encoding["""labels"""].max().item() <= 255					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.image_processing_class(**self.image_processor_dict					)

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  prepare_semantic_single_inputs()
									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     UpperCAmelCase_				,     return_tensors="""pt"""					)
									self.assertTrue(encoding["""labels"""].min().item() >= 0					)
									self.assertTrue(encoding["""labels"""].max().item() <= 150					)

									_SCREAMING_SNAKE_CASE     				=  True
									_SCREAMING_SNAKE_CASE     				=  image_processing(UpperCAmelCase_				,     UpperCAmelCase_				,     return_tensors="""pt"""					)
									self.assertTrue(encoding["""labels"""].min().item() >= 0					)
									self.assertTrue(encoding["""labels"""].max().item() <= 255					)









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

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  list[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  []

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

						order.append(snake_case__		)

						return order

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  list[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  [vert]

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

						return component




def 	__lowerCamelCase      (		snake_case__		)							->  list[list[int]]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		) * [False]
						_SCREAMING_SNAKE_CASE     				=  {vert: [] for vert in range(len(snake_case__		)		)}

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

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

						_SCREAMING_SNAKE_CASE     				=  []
						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		) * [False]

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

						return components_list

