
"""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 BeitImageProcessor



class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=18   ,       SCREAMING_SNAKE_CASE__=30   ,       SCREAMING_SNAKE_CASE__=4_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]   ,       SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]   ,       SCREAMING_SNAKE_CASE__=False   ,       )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   size if size is not None else {"""height""": 20, """width""": 20}
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											SCREAMING_SNAKE_CASE__						:       int           =   parent
											SCREAMING_SNAKE_CASE__						:       Any           =   batch_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_channels
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   image_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   min_resolution
											SCREAMING_SNAKE_CASE__						:       int           =   max_resolution
											SCREAMING_SNAKE_CASE__						:       Dict           =   do_resize
											SCREAMING_SNAKE_CASE__						:       Any           =   size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   do_center_crop
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   crop_size
											SCREAMING_SNAKE_CASE__						:       Any           =   do_normalize
											SCREAMING_SNAKE_CASE__						:       str           =   image_mean
											SCREAMING_SNAKE_CASE__						:       str           =   image_std
											SCREAMING_SNAKE_CASE__						:       Any           =   do_reduce_labels



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



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

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   Image.open(dataset[0]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       int           =   Image.open(dataset[1]["""file"""]						)

							return image, map

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       str           =   load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,split="""test"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   Image.open(ds[0]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   Image.open(ds[1]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.open(ds[2]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   Image.open(ds[3]["""file"""]						)

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



@require_torch
@require_vision
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Union[str, Any]					       =   BeitImageProcessor if is_vision_available() else None



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   BeitImageProcessingTester(self    )



				@property
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_resize"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """size"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_center_crop"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """center_crop"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_normalize"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """image_mean"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """image_std"""    )    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   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   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.image_processing_class.from_dict(
											    self.image_processor_dict   ,       size=42   ,       crop_size=84   ,       reduce_labels=SCREAMING_SNAKE_CASE__    )
											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   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=SCREAMING_SNAKE_CASE__    )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       Image.Image    )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Tuple           =   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__						:       int           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=SCREAMING_SNAKE_CASE__   ,       numpify=SCREAMING_SNAKE_CASE__    )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       np.ndarray    )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Any           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Optional[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.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() <= 2_55    )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       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() <= 2_55    )

											# Test not batched input (PIL images)
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   prepare_semantic_single_inputs()

											SCREAMING_SNAKE_CASE__						:       Dict           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       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() <= 2_55    )

											# Test batched input (PIL images)
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   prepare_semantic_batch_inputs()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       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() <= 2_55    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       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
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   prepare_semantic_single_inputs()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 1_50    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   True
											SCREAMING_SNAKE_CASE__						:       int           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )









'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()