





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 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=7     ,					_A=3     ,					_A=18     ,					_A=30     ,					_A=400     ,					_A=True     ,					_A=None     ,					_A=True     ,					_A=None     ,					_A=True     ,					_A=[0.5, 0.5, 0.5]     ,					_A=[0.5, 0.5, 0.5]     ,					_A=False     ,					):
									__A	:						List[str]       =				size if size is not None else {'height': 20, 'width': 20}
									__A	:						int       =				crop_size if crop_size is not None else {'height': 18, 'width': 18}
									__A	:						Optional[int]       =				parent
									__A	:						Union[str, Any]       =				batch_size
									__A	:						Tuple       =				num_channels
									__A	:						List[Any]       =				image_size
									__A	:						Tuple       =				min_resolution
									__A	:						str       =				max_resolution
									__A	:						Tuple       =				do_resize
									__A	:						List[Any]       =				size
									__A	:						Dict       =				do_center_crop
									__A	:						Optional[int]       =				crop_size
									__A	:						int       =				do_normalize
									__A	:						Optional[Any]       =				image_mean
									__A	:						List[Any]       =				image_std
									__A	:						List[Any]       =				do_reduce_labels





				def 					UpperCAmelCase_		(			self     ):
									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  _SCREAMING_SNAKE_CASE				(   )      ->      Dict:
					__A	:						str       =				load_dataset('hf-internal-testing/fixtures_ade20k'					,						split='test'  )

					__A	:						str       =				Image.open(dataset[0]['file']  )
					__A	:						Any       =				Image.open(dataset[1]['file']  )

					return image, map







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						List[Any]       =				load_dataset('hf-internal-testing/fixtures_ade20k'					,						split='test'  )

					__A	:						int       =				Image.open(ds[0]['file']  )
					__A	:						List[str]       =				Image.open(ds[1]['file']  )
					__A	:						Any       =				Image.open(ds[2]['file']  )
					__A	:						Dict       =				Image.open(ds[3]['file']  )

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





@require_torch
@require_vision
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  int					    =	BeitImageProcessor if is_vision_available() else None



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				BeitImageProcessingTester(self     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(_A     ,					'do_resize'     )     )
									self.assertTrue(hasattr(_A     ,					'size'     )     )
									self.assertTrue(hasattr(_A     ,					'do_center_crop'     )     )
									self.assertTrue(hasattr(_A     ,					'center_crop'     )     )
									self.assertTrue(hasattr(_A     ,					'do_normalize'     )     )
									self.assertTrue(hasattr(_A     ,					'image_mean'     )     )
									self.assertTrue(hasattr(_A     ,					'image_std'     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						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     ,					_A     )

									__A	:						int       =				self.image_processing_class.from_dict(
									    self.image_processor_dict     ,					size=42     ,					crop_size=84     ,					reduce_labels=_A     )
									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     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Union[str, Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									__A	:						Tuple       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					Image.Image     )

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

									# Test not batched input
									__A	:						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
									__A	:						Any       =				image_processing(_A     ,					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     ):
									# Initialize image_processing
									__A	:						Tuple       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						Any       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

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

									# Test batched
									__A	:						Any       =				image_processing(_A     ,					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     ):
									# Initialize image_processing
									__A	:						str       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						int       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									__A	:						Optional[int]       =				[]
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )
														maps.append(torch.zeros(image.shape[-2:]     ).long()     )

									# Test not batched input
									__A	:						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() <= 255     )

									# Test batched
									__A	:						str       =				image_processing(_A     ,					_A     ,					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)
									__A    ,				__A	:						Dict       =				prepare_semantic_single_inputs()

									__A	:						str       =				image_processing(_A     ,					_A     ,					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)
									__A    ,				__A	:						int       =				prepare_semantic_batch_inputs()

									__A	:						int       =				image_processing(_A     ,					_A     ,					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     ):
									# Initialize image_processing
									__A	:						List[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
									__A    ,				__A	:						Any       =				prepare_semantic_single_inputs()
									__A	:						Optional[int]       =				image_processing(_A     ,					_A     ,					return_tensors='pt'     )
									self.assertTrue(encoding['labels'].min().item() >= 0     )
									self.assertTrue(encoding['labels'].max().item() <= 150     )

									__A	:						Optional[int]       =				True
									__A	:						Dict       =				image_processing(_A     ,					_A     ,					return_tensors='pt'     )
									self.assertTrue(encoding['labels'].min().item() >= 0     )
									self.assertTrue(encoding['labels'].max().item() <= 255     )











def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




