



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 YolosImageProcessor

class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=7				,							_A=3				,							_A=30				,							_A=400				,							_A=True				,							_A=None				,							_A=True				,							_A=[0.5, 0.5, 0.5]				,							_A=[0.5, 0.5, 0.5]				,							_A=True				,							_A=1 / 255				,							_A=True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
													__SCREAMING_SNAKE_CASE									= parent
													__SCREAMING_SNAKE_CASE									= batch_size
													__SCREAMING_SNAKE_CASE									= num_channels
													__SCREAMING_SNAKE_CASE									= min_resolution
													__SCREAMING_SNAKE_CASE									= max_resolution
													__SCREAMING_SNAKE_CASE									= do_resize
													__SCREAMING_SNAKE_CASE									= size
													__SCREAMING_SNAKE_CASE									= do_normalize
													__SCREAMING_SNAKE_CASE									= image_mean
													__SCREAMING_SNAKE_CASE									= image_std
													__SCREAMING_SNAKE_CASE									= do_rescale
													__SCREAMING_SNAKE_CASE									= rescale_factor
													__SCREAMING_SNAKE_CASE									= do_pad






						def 	_A     (   self		):







													'''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_rescale": self.do_rescale,
													    "rescale_factor": self.rescale_factor,
													    "do_pad": self.do_pad,
													}






						def 	_A     (   self				,							_A				,							_A=False		):







													'''simple docstring'''

													if not batched:
																				__SCREAMING_SNAKE_CASE									= image_inputs[0]
																				if isinstance(_A				,							Image.Image		):
																											__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= image.size
																				else:
																											__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= image.shape[1], image.shape[2]
																				if w < h:
																											__SCREAMING_SNAKE_CASE									= int(self.size['shortest_edge'] * h / w		)
																											__SCREAMING_SNAKE_CASE									= self.size['shortest_edge']
																				elif w > h:
																											__SCREAMING_SNAKE_CASE									= self.size['shortest_edge']
																											__SCREAMING_SNAKE_CASE									= int(self.size['shortest_edge'] * w / h		)
																				else:
																											__SCREAMING_SNAKE_CASE									= self.size['shortest_edge']
																											__SCREAMING_SNAKE_CASE									= self.size['shortest_edge']

													else:
																				__SCREAMING_SNAKE_CASE									= []
																				for image in image_inputs:
																											__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.get_expected_values([image]		)
																											expected_values.append((expected_height, expected_width)		)
																				__SCREAMING_SNAKE_CASE									= max(_A				,							key=lambda _A		: item[0]		)[0]
																				__SCREAMING_SNAKE_CASE									= max(_A				,							key=lambda _A		: item[1]		)[1]

													return expected_height, expected_width







@require_torch
@require_vision
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  YolosImageProcessor if is_vision_available() else None






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= YolosImageProcessingTester(self		)






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.image_processor_tester.prepare_image_processor_dict()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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_resize'		)		)
													self.assertTrue(hasattr(_A				,							'size'		)		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.image_processing_class.from_dict(self.image_processor_dict		)
													self.assertEqual(image_processor.size				,							{'shortest_edge': 18, 'longest_edge': 1_333}		)
													self.assertEqual(image_processor.do_pad				,							_A		)

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													pass






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.image_processing_class(**self.image_processor_dict		)
													# create random PIL images
													__SCREAMING_SNAKE_CASE									= prepare_image_inputs(self.image_processor_tester				,							equal_resolution=_A		)
													for image in image_inputs:
																				self.assertIsInstance(_A				,							Image.Image		)

													# Test not batched input
													__SCREAMING_SNAKE_CASE									= image_processing(image_inputs[0]				,							return_tensors='pt'		).pixel_values

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.image_processor_tester.get_expected_values(_A				,							batched=_A		)

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.image_processing_class(**self.image_processor_dict		)
													# create random numpy tensors
													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= image_processing(image_inputs[0]				,							return_tensors='pt'		).pixel_values

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= image_processing(_A				,							return_tensors='pt'		).pixel_values

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= image_processing(_A				,							return_tensors='pt'		).pixel_values

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.image_processing_class(**self.image_processor_dict		)
													__SCREAMING_SNAKE_CASE									= self.image_processing_class(do_resize=_A				,							do_normalize=_A				,							do_rescale=_A		)
													# create random PyTorch tensors
													__SCREAMING_SNAKE_CASE									= prepare_image_inputs(self.image_processor_tester				,							equal_resolution=_A				,							torchify=_A		)
													for image in image_inputs:
																				self.assertIsInstance(_A				,							torch.Tensor		)

													# Test whether the method "pad" and calling the image processor return the same tensors
													__SCREAMING_SNAKE_CASE									= image_processing_a.pad(_A				,							return_tensors='pt'		)
													__SCREAMING_SNAKE_CASE									= image_processing_a(_A				,							return_tensors='pt'		)

													self.assertTrue(
													    torch.allclose(encoded_images_with_method['pixel_values']				,							encoded_images['pixel_values']				,							atol=1e-4		)		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
													with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,							'r'		) as f:
																				__SCREAMING_SNAKE_CASE									= json.loads(f.read()		)

													__SCREAMING_SNAKE_CASE									= {'image_id': 39_769, 'annotations': target}

													# encode them
													__SCREAMING_SNAKE_CASE									= YolosImageProcessor.from_pretrained('hustvl/yolos-small'		)
													__SCREAMING_SNAKE_CASE									= image_processing(images=_A				,							annotations=_A				,							return_tensors='pt'		)

													# verify pixel values
													__SCREAMING_SNAKE_CASE									= torch.Size([1, 3, 800, 1_066]		)
													self.assertEqual(encoding['pixel_values'].shape				,							_A		)

													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= torch.Size([6, 4]		)
													self.assertEqual(encoding['labels'][0]['boxes'].shape				,							_A		)
													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= torch.tensor([39_769]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,							_A		)		)
													# verify is_crowd
													__SCREAMING_SNAKE_CASE									= torch.tensor([0, 0, 0, 0, 0, 0]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,							_A		)		)
													# verify class_labels
													__SCREAMING_SNAKE_CASE									= torch.tensor([75, 75, 63, 65, 17, 17]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,							_A		)		)
													# verify orig_size
													__SCREAMING_SNAKE_CASE									= torch.tensor([480, 640]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,							_A		)		)
													# verify size
													__SCREAMING_SNAKE_CASE									= torch.tensor([800, 1_066]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,							_A		)		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
													with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,							'r'		) as f:
																				__SCREAMING_SNAKE_CASE									= json.loads(f.read()		)

													__SCREAMING_SNAKE_CASE									= {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

													__SCREAMING_SNAKE_CASE									= pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

													# encode them
													__SCREAMING_SNAKE_CASE									= YolosImageProcessor(format='coco_panoptic'		)
													__SCREAMING_SNAKE_CASE									= image_processing(images=_A				,							annotations=_A				,							masks_path=_A				,							return_tensors='pt'		)

													# verify pixel values
													__SCREAMING_SNAKE_CASE									= torch.Size([1, 3, 800, 1_066]		)
													self.assertEqual(encoding['pixel_values'].shape				,							_A		)

													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= torch.Size([6, 4]		)
													self.assertEqual(encoding['labels'][0]['boxes'].shape				,							_A		)
													__SCREAMING_SNAKE_CASE									= 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
													__SCREAMING_SNAKE_CASE									= torch.tensor([39_769]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,							_A		)		)
													# verify is_crowd
													__SCREAMING_SNAKE_CASE									= torch.tensor([0, 0, 0, 0, 0, 0]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,							_A		)		)
													# verify class_labels
													__SCREAMING_SNAKE_CASE									= torch.tensor([17, 17, 63, 75, 75, 93]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,							_A		)		)
													# verify masks
													__SCREAMING_SNAKE_CASE									= 822_873
													self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,							_A		)
													# verify orig_size
													__SCREAMING_SNAKE_CASE									= torch.tensor([480, 640]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,							_A		)		)
													# verify size
													__SCREAMING_SNAKE_CASE									= torch.tensor([800, 1_066]		)
													self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,							_A		)		)











def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= [[0 for _ in range(a__       )] for _ in range(m + 1       )]
							for i in range(m + 1       ):
														__SCREAMING_SNAKE_CASE									= 1

							for n in range(m + 1       ):
														for k in range(1							,       a__       ):
																					memo[n][k] += memo[n][k - 1]
																					if n - k > 0:
																												memo[n][k] += memo[n - k - 1][k]

							return memo[m][m - 1]


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						try:
									lowerCAmelCase__		:					Optional[Any]							=int(input('''Enter a number: ''').strip())
									print(partition(n))
						except ValueError:
									print('''Please enter a number.''')
			else:
						try:
									lowerCAmelCase__		:					str							=int(sys.argv[1])
									print(partition(n))
						except ValueError:
									print('''Please pass a number.''')






