
"""simple docstring"""


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 DetaImageProcessor







class  lowerCAmelCase_ (					unittest.TestCase			):



						'''simple docstring'''


						def __init__(       self   :					Any     ,A_   :					List[str]     ,A_   :					List[str]=7     ,A_   :					List[str]=3     ,A_   :					Optional[Any]=30     ,A_   :					Any=400     ,A_   :					Optional[int]=True     ,A_   :					Optional[Any]=None     ,A_   :					Any=True     ,A_   :					int=[0.5, 0.5, 0.5]     ,A_   :					Tuple=[0.5, 0.5, 0.5]     ,A_   :					List[Any]=True     ,A_   :					str=1 / 255     ,A_   :					Optional[Any]=True     ,)   ->      Union[str, Any]:
							A        =      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							A        =      parent
							A        =      batch_size
							A        =      num_channels
							A        =      min_resolution
							A        =      max_resolution
							A        =      do_resize
							A        =      size
							A        =      do_normalize
							A        =      image_mean
							A        =      image_std
							A        =      do_rescale
							A        =      rescale_factor
							A        =      do_pad


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					Dict     ,A_   :					List[str]=False					)   ->      List[str]:





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

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

							return expected_height, expected_width







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



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			DetaImageProcessor if is_vision_available() else None


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Any:
							A        =      DetaImageProcessingTester(self					)


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      str:
							return self.image_processor_tester.prepare_image_processor_dict()


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      List[Any]:
							A        =      self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'image_mean'					)					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'image_std'					)					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'do_normalize'					)					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'do_resize'					)					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'do_rescale'					)					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'do_pad'					)					)
							self.assertTrue(hasattr(UpperCamelCase__     ,'size'					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      List[Any]:
							A        =      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     ,UpperCamelCase__					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Dict:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      List[str]:
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							A        =      prepare_image_inputs(self.image_processor_tester     ,equal_resolution=UpperCamelCase__					)
							for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__     ,Image.Image					)

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

							A        =      self.image_processor_tester.get_expected_values(UpperCamelCase__					)

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

							# Test batched
							A        =      self.image_processor_tester.get_expected_values(UpperCamelCase__     ,batched=UpperCamelCase__					)

							A        =      image_processing(UpperCamelCase__     ,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   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Union[str, Any]:
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							A        =      prepare_image_inputs(self.image_processor_tester     ,equal_resolution=UpperCamelCase__     ,numpify=UpperCamelCase__					)
							for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__     ,np.ndarray					)

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

							A        =      self.image_processor_tester.get_expected_values(UpperCamelCase__					)

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

							# Test batched
							A        =      image_processing(UpperCamelCase__     ,return_tensors='pt'					).pixel_values

							A        =      self.image_processor_tester.get_expected_values(UpperCamelCase__     ,batched=UpperCamelCase__					)

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      str:
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							A        =      prepare_image_inputs(self.image_processor_tester     ,equal_resolution=UpperCamelCase__     ,torchify=UpperCamelCase__					)
							for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__     ,torch.Tensor					)

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

							A        =      self.image_processor_tester.get_expected_values(UpperCamelCase__					)

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

							# Test batched
							A        =      image_processing(UpperCamelCase__     ,return_tensors='pt'					).pixel_values

							A        =      self.image_processor_tester.get_expected_values(UpperCamelCase__     ,batched=UpperCamelCase__					)

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


						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Union[str, Any]:
							A        =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,'r'					) as f:
								A        =      json.loads(f.read()					)

							A        =      {'''image_id''': 3_9769, '''annotations''': target}

							# encode them
							A        =      DetaImageProcessor()
							A        =      image_processing(images=UpperCamelCase__     ,annotations=UpperCamelCase__     ,return_tensors='pt'					)

							# verify pixel values
							A        =      torch.Size([1, 3, 800, 1066]					)
							self.assertEqual(encoding['pixel_values'].shape     ,UpperCamelCase__					)

							A        =      torch.tensor([0.27_96, 0.31_38, 0.34_81]					)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,UpperCamelCase__     ,atol=1e-4					)					)

							# verify area
							A        =      torch.tensor([58_87.96_00, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,UpperCamelCase__					)					)
							# verify boxes
							A        =      torch.Size([6, 4]					)
							self.assertEqual(encoding['labels'][0]['boxes'].shape     ,UpperCamelCase__					)
							A        =      torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,UpperCamelCase__     ,atol=1e-3					)					)
							# verify image_id
							A        =      torch.tensor([3_9769]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,UpperCamelCase__					)					)
							# verify is_crowd
							A        =      torch.tensor([0, 0, 0, 0, 0, 0]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,UpperCamelCase__					)					)
							# verify class_labels
							A        =      torch.tensor([75, 75, 63, 65, 17, 17]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,UpperCamelCase__					)					)
							# verify orig_size
							A        =      torch.tensor([480, 640]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,UpperCamelCase__					)					)
							# verify size
							A        =      torch.tensor([800, 1066]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,UpperCamelCase__					)					)




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Optional[Any]:
							A        =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,'r'					) as f:
								A        =      json.loads(f.read()					)

							A        =      {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

							# encode them
							A        =      DetaImageProcessor(format='coco_panoptic'					)
							A        =      image_processing(images=UpperCamelCase__     ,annotations=UpperCamelCase__     ,masks_path=UpperCamelCase__     ,return_tensors='pt'					)

							# verify pixel values
							A        =      torch.Size([1, 3, 800, 1066]					)
							self.assertEqual(encoding['pixel_values'].shape     ,UpperCamelCase__					)

							A        =      torch.tensor([0.27_96, 0.31_38, 0.34_81]					)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,UpperCamelCase__     ,atol=1e-4					)					)

							# verify area
							A        =      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 58_79.65_62, 76_34.11_47]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,UpperCamelCase__					)					)
							# verify boxes
							A        =      torch.Size([6, 4]					)
							self.assertEqual(encoding['labels'][0]['boxes'].shape     ,UpperCamelCase__					)
							A        =      torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,UpperCamelCase__     ,atol=1e-3					)					)
							# verify image_id
							A        =      torch.tensor([3_9769]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,UpperCamelCase__					)					)
							# verify is_crowd
							A        =      torch.tensor([0, 0, 0, 0, 0, 0]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,UpperCamelCase__					)					)
							# verify class_labels
							A        =      torch.tensor([17, 17, 63, 75, 75, 93]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,UpperCamelCase__					)					)
							# verify masks
							A        =      82_2873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,UpperCamelCase__					)
							# verify orig_size
							A        =      torch.tensor([480, 640]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,UpperCamelCase__					)					)
							# verify size
							A        =      torch.tensor([800, 1066]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,UpperCamelCase__					)					)
from math import ceil
def       A	(    _lowercase = 1_001						):
						SCREAMING_SNAKE_CASE							:    Any							=							1

						for i in range(1		,  int(ceil(n / 2.0						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							2 * i + 1
												SCREAMING_SNAKE_CASE							:    int							=							2 * i
												SCREAMING_SNAKE_CASE							:    List[str]							=							total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															__UpperCamelCase		:       Dict									=     int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')





