





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 	UpperCamelCase_     (     unittest.TestCase       ):



					def __init__(		self    :    Any					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Dict=7					,			lowerCamelCase    :    Tuple=3					,			lowerCamelCase    :    Dict=30					,			lowerCamelCase    :    Optional[int]=4_00					,			lowerCamelCase    :    Optional[int]=True					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    Tuple=True					,			lowerCamelCase    :    str=[0.5, 0.5, 0.5]					,			lowerCamelCase    :    List[Any]=[0.5, 0.5, 0.5]					,			lowerCamelCase    :    Union[str, Any]=False					,			):
							lowerCamelCase_  :    Any   	=			size if size is not None else {'height': 30, 'width': 30}
							lowerCamelCase_  :    int   	=			parent
							lowerCamelCase_  :    str   	=			batch_size
							lowerCamelCase_  :    Optional[int]   	=			num_channels
							lowerCamelCase_  :    Any   	=			min_resolution
							lowerCamelCase_  :    Tuple   	=			max_resolution
							lowerCamelCase_  :    Union[str, Any]   	=			do_resize
							lowerCamelCase_  :    int   	=			size
							lowerCamelCase_  :    Any   	=			do_normalize
							lowerCamelCase_  :    List[str]   	=			image_mean
							lowerCamelCase_  :    Union[str, Any]   	=			image_std
							lowerCamelCase_  :    List[str]   	=			do_reduce_labels





					def      __a    (		self    :    int	):
							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 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Optional[Any]   	=			load_dataset('hf-internal-testing/fixtures_ade20k'       ,split='test'	)

		lowerCamelCase_  :    Any   	=			Image.open(dataset[0]['file']	)
		lowerCamelCase_  :    Tuple   	=			Image.open(dataset[1]['file']	)

		return image, map


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Tuple   	=			load_dataset('hf-internal-testing/fixtures_ade20k'       ,split='test'	)

		lowerCamelCase_  :    Union[str, Any]   	=			Image.open(dataset[0]['file']	)
		lowerCamelCase_  :    Tuple   	=			Image.open(dataset[1]['file']	)
		lowerCamelCase_  :    List[Any]   	=			Image.open(dataset[2]['file']	)
		lowerCamelCase_  :    Optional[int]   	=			Image.open(dataset[3]['file']	)

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






@require_torch
@require_vision
class 	UpperCamelCase_     (     _UpperCAmelCase     ,    unittest.TestCase       ):
					_a			:	Optional[int]							=				SegformerImageProcessor if is_vision_available() else None



					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    List[str]   	=			SegformerImageProcessingTester(self	)



					@property
					def      __a    (		self    :    Dict	):
							return self.image_processor_tester.prepare_image_processor_dict()



					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    Tuple   	=			self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(lowerCamelCase					,			'do_resize'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'size'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'do_normalize'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'image_mean'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'image_std'	)	)
							self.assertTrue(hasattr(lowerCamelCase					,			'do_reduce_labels'	)	)



					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    int   	=			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					,			lowerCamelCase	)

							lowerCamelCase_  :    int   	=			self.image_processing_class.from_dict(self.image_processor_dict					,			size=42					,			reduce_labels=lowerCamelCase	)
							self.assertEqual(image_processor.size					,			{'height': 42, 'width': 42}	)
							self.assertEqual(image_processor.do_reduce_labels					,			lowerCamelCase	)



					def      __a    (		self    :    str	):
							pass



					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    Dict   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCamelCase_  :    str   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase	)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase					,			Image.Image	)

							# Test not batched input
							lowerCamelCase_  :    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
							lowerCamelCase_  :    Tuple   	=			image_processing(lowerCamelCase					,			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    :    Tuple	):
							lowerCamelCase_  :    Optional[int]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCamelCase_  :    Any   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			numpify=lowerCamelCase	)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase					,			np.ndarray	)

							# Test not batched input
							lowerCamelCase_  :    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
							lowerCamelCase_  :    Optional[Any]   	=			image_processing(lowerCamelCase					,			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]	):
							lowerCamelCase_  :    List[Any]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCamelCase_  :    Dict   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			torchify=lowerCamelCase	)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase					,			torch.Tensor	)

							# Test not batched input
							lowerCamelCase_  :    Dict   	=			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
							lowerCamelCase_  :    List[Any]   	=			image_processing(lowerCamelCase					,			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[Any]	):
							lowerCamelCase_  :    List[Any]   	=			self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCamelCase_  :    List[str]   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			torchify=lowerCamelCase	)
							lowerCamelCase_  :    List[Any]   	=			[]
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase					,			torch.Tensor	)
									maps.append(torch.zeros(image.shape[-2:]	).long()	)

							# Test not batched input
							lowerCamelCase_  :    Any   	=			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() <= 2_55	)

							# Test batched
							lowerCamelCase_  :    Union[str, Any]   	=			image_processing(lowerCamelCase					,			lowerCamelCase					,			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() <= 2_55	)

							# Test not batched input (PIL images)
							lowerCamelCase_	,			lowerCamelCase_  :    Any   	=			prepare_semantic_single_inputs()

							lowerCamelCase_  :    Optional[int]   	=			image_processing(lowerCamelCase					,			lowerCamelCase					,			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() <= 2_55	)

							# Test batched input (PIL images)
							lowerCamelCase_	,			lowerCamelCase_  :    int   	=			prepare_semantic_batch_inputs()

							lowerCamelCase_  :    int   	=			image_processing(lowerCamelCase					,			lowerCamelCase					,			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() <= 2_55	)





					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    Optional[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
							lowerCamelCase_	,			lowerCamelCase_  :    Union[str, Any]   	=			prepare_semantic_single_inputs()
							lowerCamelCase_  :    str   	=			image_processing(lowerCamelCase					,			lowerCamelCase					,			return_tensors='pt'	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 1_50	)

							lowerCamelCase_  :    List[Any]   	=			True
							lowerCamelCase_  :    str   	=			image_processing(lowerCamelCase					,			lowerCamelCase					,			return_tensors='pt'	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 2_55	)







def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		return (input_a, input_a).count(0	) % 2


def 		_SCREAMING_SNAKE_CASE    (   ):
		assert xor_gate(0       ,0	) == 0
		assert xor_gate(0       ,1	) == 1
		assert xor_gate(1       ,0	) == 1
		assert xor_gate(1       ,1	) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))
