



'''simple docstring'''
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 SegformerImageProcessor



class _A  (    unittest.TestCase    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=7 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=30 ,       __UpperCAmelCase=400 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       __UpperCAmelCase=True ,       __UpperCAmelCase=[0.5, 0.5, 0.5] ,       __UpperCAmelCase=[0.5, 0.5, 0.5] ,       __UpperCAmelCase=False ,       )       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     size if size is not None else {"""height""": 30, """width""": 30}
					__UpperCAmelCase   :      Optional[Any]										=     parent
					__UpperCAmelCase   :      Optional[int]										=     batch_size
					__UpperCAmelCase   :      Optional[Any]										=     num_channels
					__UpperCAmelCase   :      int										=     min_resolution
					__UpperCAmelCase   :      str										=     max_resolution
					__UpperCAmelCase   :      List[Any]										=     do_resize
					__UpperCAmelCase   :      Tuple										=     size
					__UpperCAmelCase   :      Any										=     do_normalize
					__UpperCAmelCase   :      Union[str, Any]										=     image_mean
					__UpperCAmelCase   :      List[str]										=     image_std
					__UpperCAmelCase   :      Any										=     do_reduce_labels





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_reduce_labels": self.do_reduce_labels,
					}


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      Any										=     load_dataset("""hf-internal-testing/fixtures_ade20k"""			,							split="""test""" )

			__UpperCAmelCase   :      str										=     Image.open(dataset[0]["""file"""] )
			__UpperCAmelCase   :      Tuple										=     Image.open(dataset[1]["""file"""] )

			return image, map


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      int										=     load_dataset("""hf-internal-testing/fixtures_ade20k"""			,							split="""test""" )

			__UpperCAmelCase   :      str										=     Image.open(dataset[0]["""file"""] )
			__UpperCAmelCase   :      str										=     Image.open(dataset[1]["""file"""] )
			__UpperCAmelCase   :      Optional[Any]										=     Image.open(dataset[2]["""file"""] )
			__UpperCAmelCase   :      Any										=     Image.open(dataset[3]["""file"""] )

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






@require_torch
@require_vision
class _A  (    _UpperCAmelCase	,				unittest.TestCase    ):
		_SCREAMING_SNAKE_CASE					:    List[Any]						      =			SegformerImageProcessor if is_vision_available() else None





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     SegformerImageProcessingTester(self				)





		@property
		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					return self.image_processor_tester.prepare_image_processor_dict()





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     self.image_processing_class(**self.image_processor_dict				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_resize"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """size"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_normalize"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """image_mean"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """image_std"""				)				)
					self.assertTrue(hasattr(__UpperCAmelCase ,       """do_reduce_labels"""				)				)





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

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.image_processing_class.from_dict(self.image_processor_dict				)
					self.assertEqual(image_processor.size ,       {"""height""": 30, """width""": 30}				)
					self.assertEqual(image_processor.do_reduce_labels ,       __UpperCAmelCase				)

					__UpperCAmelCase   :      Any										=     self.image_processing_class.from_dict(self.image_processor_dict ,       size=42 ,       reduce_labels=__UpperCAmelCase				)
					self.assertEqual(image_processor.size ,       {"""height""": 42, """width""": 42}				)
					self.assertEqual(image_processor.do_reduce_labels ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					pass





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PIL images
					__UpperCAmelCase   :      List[str]										=     prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__UpperCAmelCase				)
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase ,       Image.Image				)

					# Test not batched input
					__UpperCAmelCase   :      Union[str, Any]										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )

					# Test batched
					__UpperCAmelCase   :      str										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     self.image_processing_class(**self.image_processor_dict				)
					# create random numpy tensors
					__UpperCAmelCase   :      Any										=     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
					__UpperCAmelCase   :      List[Any]										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )

					# Test batched
					__UpperCAmelCase   :      Dict										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )





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

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PyTorch tensors
					__UpperCAmelCase   :      List[str]										=     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
					__UpperCAmelCase   :      Optional[Any]										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )

					# Test batched
					__UpperCAmelCase   :      Any										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.image_processing_class(**self.image_processor_dict				)
					# create random PyTorch tensors
					__UpperCAmelCase   :      List[Any]										=     prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__UpperCAmelCase ,       torchify=__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     []
					for image in image_inputs:
								self.assertIsInstance(__UpperCAmelCase ,       torch.Tensor				)
								maps.append(torch.zeros(image.shape[-2:]				).long()				)

					# Test not batched input
					__UpperCAmelCase   :      Dict										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )
					self.assertEqual(
					    encoding["""labels"""].shape ,       (
					        1,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.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
					__UpperCAmelCase   :      List[Any]										=     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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )
					self.assertEqual(
					    encoding["""labels"""].shape ,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.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)
					__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     prepare_semantic_single_inputs()

					__UpperCAmelCase   :      Any										=     image_processing(__UpperCAmelCase ,       __UpperCAmelCase ,       return_tensors="""pt"""				)
					self.assertEqual(
					    encoding["""pixel_values"""].shape ,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    ) ,       )
					self.assertEqual(
					    encoding["""labels"""].shape ,       (
					        1,
					        self.image_processor_tester.size["""height"""],
					        self.image_processor_tester.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)
					__UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     prepare_semantic_batch_inputs()

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





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     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
					__UpperCAmelCase				,  __UpperCAmelCase   :      int										=     prepare_semantic_single_inputs()
					__UpperCAmelCase   :      Dict										=     image_processing(__UpperCAmelCase ,       __UpperCAmelCase ,       return_tensors="""pt"""				)
					self.assertTrue(encoding["""labels"""].min().item() >= 0				)
					self.assertTrue(encoding["""labels"""].max().item() <= 150				)

					__UpperCAmelCase   :      Dict										=     True
					__UpperCAmelCase   :      List[str]										=     image_processing(__UpperCAmelCase ,       __UpperCAmelCase ,       return_tensors="""pt"""				)
					self.assertTrue(encoding["""labels"""].min().item() >= 0				)
					self.assertTrue(encoding["""labels"""].max().item() <= 255				)









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

					if not self.min_threshold < threshold < self.max_threshold:
								__UpperCAmelCase   :      str										=     f'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error				)
											self.error_table[y][x + 2] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x] += int(8 / 32 * current_error				)
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					_UpperCamelCase											=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 126, 130, 140)
					]

					for burkes in burkes_instances:
										burkes.process()

					for burkes in burkes_instances:
										imshow(
										    F'Original image with dithering threshold: {burkes.threshold}',
										    burkes.output_img,
										)

					waitKey(0)
					destroyAllWindows()




