





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def __init__( self : List[str]						,				_A : Optional[int]						,				_A : Union[str, Any]=7						,				_A : Dict=3						,				_A : Dict=30						,				_A : List[str]=400						,				_A : int=True						,				_A : Any=None						,				_A : int=True						,				_A : Optional[Any]=1 / 255						,				_A : Tuple=True						,				_A : Any=[0.5, 0.5, 0.5]						,				_A : str=[0.5, 0.5, 0.5]						,				_A : Any=True						,				)							->      str:







							"""simple docstring"""


							snake_case_						:     List[str]     =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							snake_case_						:     str     =		parent
							snake_case_						:     int     =		batch_size
							snake_case_						:     Dict     =		num_channels
							snake_case_						:     List[Any]     =		min_resolution
							snake_case_						:     Optional[Any]     =		max_resolution
							snake_case_						:     Optional[Any]     =		do_resize
							snake_case_						:     Optional[int]     =		size
							snake_case_						:     Union[str, Any]     =		do_rescale
							snake_case_						:     List[Any]     =		rescale_factor
							snake_case_						:     Optional[Any]     =		do_normalize
							snake_case_						:     List[str]     =		image_mean
							snake_case_						:     Dict     =		image_std
							snake_case_						:     Optional[Any]     =		do_pad






						def UpperCAmelCase_			( self : Dict		)							->      Any:







							"""simple docstring"""


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_pad": self.do_pad,
							}






						def UpperCAmelCase_			( self : int						,				_A : Dict						,				_A : int=False		)							->      List[str]:







							"""simple docstring"""


							if not batched:
								snake_case_						:     List[str]     =		image_inputs[0]
								if isinstance(A__						,				Image.Image		):
									snake_case_  ,snake_case_						:     Tuple     =		image.size
								else:
									snake_case_  ,snake_case_						:     str     =		image.shape[1], image.shape[2]
								if w < h:
									snake_case_						:     int     =		int(self.size['shortest_edge'] * h / w		)
									snake_case_						:     Optional[int]     =		self.size['shortest_edge']
								elif w > h:
									snake_case_						:     Optional[Any]     =		self.size['shortest_edge']
									snake_case_						:     Optional[int]     =		int(self.size['shortest_edge'] * w / h		)
								else:
									snake_case_						:     Union[str, Any]     =		self.size['shortest_edge']
									snake_case_						:     Optional[int]     =		self.size['shortest_edge']

							else:
								snake_case_						:     Tuple     =		[]
								for image in image_inputs:
									snake_case_  ,snake_case_						:     int     =		self.get_expected_values([image]		)
									expected_values.append((expected_height, expected_width)		)
								snake_case_						:     Optional[Any]     =		max(A__						,				key=lambda _A		: item[0]		)[0]
								snake_case_						:     List[Any]     =		max(A__						,				key=lambda _A		: item[1]		)[1]

							return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_  (   SCREAMING_SNAKE_CASE__    ,			unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    DetrImageProcessor if is_vision_available() else None






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     str     =		DetrImageProcessingTester(self		)






						@property
						def UpperCAmelCase_			( self : Tuple		)							->      Union[str, Any]:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_			( self : int		)							->      str:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(A__						,				'image_mean'		)		)
							self.assertTrue(hasattr(A__						,				'image_std'		)		)
							self.assertTrue(hasattr(A__						,				'do_normalize'		)		)
							self.assertTrue(hasattr(A__						,				'do_rescale'		)		)
							self.assertTrue(hasattr(A__						,				'rescale_factor'		)		)
							self.assertTrue(hasattr(A__						,				'do_resize'		)		)
							self.assertTrue(hasattr(A__						,				'size'		)		)
							self.assertTrue(hasattr(A__						,				'do_pad'		)		)






						def UpperCAmelCase_			( self : Any		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.image_processing_class.from_dict(self.image_processor_dict		)
							self.assertEqual(image_processor.size						,				{'shortest_edge': 18, 'longest_edge': 1333}		)
							self.assertEqual(image_processor.do_pad						,				A__		)

							snake_case_						:     Any     =		self.image_processing_class.from_dict(
							    self.image_processor_dict						,				size=42						,				max_size=84						,				pad_and_return_pixel_mask=A__		)
							self.assertEqual(image_processor.size						,				{'shortest_edge': 42, 'longest_edge': 84}		)
							self.assertEqual(image_processor.do_pad						,				A__		)






						def UpperCAmelCase_			( self : int		)							->      List[str]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_			( self : Optional[int]		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Any     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							snake_case_						:     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
							snake_case_						:     List[Any]     =		image_processing(image_inputs[0]						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Any     =		self.image_processor_tester.get_expected_values(A__		)

							self.assertEqual(
							    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

							# Test batched
							snake_case_  ,snake_case_						:     Union[str, Any]     =		self.image_processor_tester.get_expected_values(A__						,				batched=A__		)

							snake_case_						:     List[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,
							        expected_height,
							        expected_width,
							    )						,				)






						def UpperCAmelCase_			( self : Tuple		)							->      int:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							snake_case_						:     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
							snake_case_						:     Tuple     =		image_processing(image_inputs[0]						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Optional[Any]     =		self.image_processor_tester.get_expected_values(A__		)

							self.assertEqual(
							    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

							# Test batched
							snake_case_						:     Optional[Any]     =		image_processing(A__						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Optional[Any]     =		self.image_processor_tester.get_expected_values(A__						,				batched=A__		)

							self.assertEqual(
							    encoded_images.shape						,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,				)






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							snake_case_						:     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
							snake_case_						:     int     =		image_processing(image_inputs[0]						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Union[str, Any]     =		self.image_processor_tester.get_expected_values(A__		)

							self.assertEqual(
							    encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

							# Test batched
							snake_case_						:     Optional[int]     =		image_processing(A__						,				return_tensors='pt'		).pixel_values

							snake_case_  ,snake_case_						:     Dict     =		self.image_processor_tester.get_expected_values(A__						,				batched=A__		)

							self.assertEqual(
							    encoded_images.shape						,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )						,				)






						@slow
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Any     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,				'r'		) as f:
								snake_case_						:     List[str]     =		json.loads(f.read()		)

							snake_case_						:     Any     =		{'image_id': 39769, 'annotations': target}

							# encode them
							snake_case_						:     Dict     =		DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'		)
							snake_case_						:     Any     =		image_processing(images=A__						,				annotations=A__						,				return_tensors='pt'		)

							# verify pixel values
							snake_case_						:     Any     =		torch.Size([1, 3, 800, 1066]		)
							self.assertEqual(encoding['pixel_values'].shape						,				A__		)

							snake_case_						:     Tuple     =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,				A__						,				atol=1E-4		)		)

							# verify area
							snake_case_						:     Tuple     =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,				A__		)		)
							# verify boxes
							snake_case_						:     List[str]     =		torch.Size([6, 4]		)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,				A__		)
							snake_case_						:     str     =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,				A__						,				atol=1E-3		)		)
							# verify image_id
							snake_case_						:     str     =		torch.tensor([39769]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,				A__		)		)
							# verify is_crowd
							snake_case_						:     Any     =		torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,				A__		)		)
							# verify class_labels
							snake_case_						:     Dict     =		torch.tensor([75, 75, 63, 65, 17, 17]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,				A__		)		)
							# verify orig_size
							snake_case_						:     List[Any]     =		torch.tensor([480, 640]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,				A__		)		)
							# verify size
							snake_case_						:     Tuple     =		torch.tensor([800, 1066]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,				A__		)		)






						@slow
						def UpperCAmelCase_			( self : Optional[Any]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,				'r'		) as f:
								snake_case_						:     Any     =		json.loads(f.read()		)

							snake_case_						:     List[Any]     =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

							snake_case_						:     List[Any]     =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

							# encode them
							snake_case_						:     Any     =		DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'		)
							snake_case_						:     int     =		image_processing(images=A__						,				annotations=A__						,				masks_path=A__						,				return_tensors='pt'		)

							# verify pixel values
							snake_case_						:     Optional[Any]     =		torch.Size([1, 3, 800, 1066]		)
							self.assertEqual(encoding['pixel_values'].shape						,				A__		)

							snake_case_						:     int     =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,				A__						,				atol=1E-4		)		)

							# verify area
							snake_case_						:     Optional[int]     =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,				A__		)		)
							# verify boxes
							snake_case_						:     Optional[Any]     =		torch.Size([6, 4]		)
							self.assertEqual(encoding['labels'][0]['boxes'].shape						,				A__		)
							snake_case_						:     Dict     =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,				A__						,				atol=1E-3		)		)
							# verify image_id
							snake_case_						:     Union[str, Any]     =		torch.tensor([39769]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,				A__		)		)
							# verify is_crowd
							snake_case_						:     Optional[Any]     =		torch.tensor([0, 0, 0, 0, 0, 0]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,				A__		)		)
							# verify class_labels
							snake_case_						:     List[Any]     =		torch.tensor([17, 17, 63, 75, 75, 93]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,				A__		)		)
							# verify masks
							snake_case_						:     Dict     =		822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,				A__		)
							# verify orig_size
							snake_case_						:     Union[str, Any]     =		torch.tensor([480, 640]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,				A__		)		)
							# verify size
							snake_case_						:     List[Any]     =		torch.tensor([800, 1066]		)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,				A__		)		)













def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		int(__a				)

	# Initialize Result
	snake_case_						:     Tuple     =		[]

	# 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__":
						_SCREAMING_SNAKE_CASE 		=						[]
						_SCREAMING_SNAKE_CASE 		=						"""0"""

						if (
						    input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
						    == "y"
						):
												_SCREAMING_SNAKE_CASE 		=						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()))
												_SCREAMING_SNAKE_CASE 		=						input("""Enter the change you want to make in Indian Currency: """).strip()
						else:
												# All denominations of Indian Currency if user does not enter
												_SCREAMING_SNAKE_CASE 		=						[1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												_SCREAMING_SNAKE_CASE 		=						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}: ''')
												_SCREAMING_SNAKE_CASE 		=						find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=""" """)

