


"""simple docstring"""






import unittest

import numpy as np

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 DeiTImageProcessor


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=7	,					lowerCamelCase__=3	,					lowerCamelCase__=18	,					lowerCamelCase__=30	,					lowerCamelCase__=400	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=True	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						int		=      size if size is not None else {'''height''': 20, '''width''': 20}
								snake_case__  :						Dict		=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

								snake_case__  :						Optional[int]		=      parent
								snake_case__  :						str		=      batch_size
								snake_case__  :						Dict		=      num_channels
								snake_case__  :						List[Any]		=      image_size
								snake_case__  :						int		=      min_resolution
								snake_case__  :						Optional[Any]		=      max_resolution
								snake_case__  :						List[Any]		=      do_resize
								snake_case__  :						List[str]		=      size
								snake_case__  :						Dict		=      do_center_crop
								snake_case__  :						List[Any]		=      crop_size
								snake_case__  :						Optional[Any]		=      do_normalize
								snake_case__  :						List[Any]		=      image_mean
								snake_case__  :						Optional[Any]		=      image_std







							def        UpperCAmelCase (						self)   ->	int:







								'''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,
								}


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




							'''simple docstring'''





							__magic_name__					:     str				  =  DeiTImageProcessor if is_vision_available() else None
							__magic_name__					:     Tuple				  =  True




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







								'''simple docstring'''




								snake_case__  :						int		=      DeiTImageProcessingTester(self)




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







								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




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







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowerCamelCase	,					"do_resize"))
								self.assertTrue(hasattr(_lowerCamelCase	,					"size"))
								self.assertTrue(hasattr(_lowerCamelCase	,					"do_center_crop"))
								self.assertTrue(hasattr(_lowerCamelCase	,					"center_crop"))
								self.assertTrue(hasattr(_lowerCamelCase	,					"do_normalize"))
								self.assertTrue(hasattr(_lowerCamelCase	,					"image_mean"))
								self.assertTrue(hasattr(_lowerCamelCase	,					"image_std"))




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      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})

								snake_case__  :						int		=      self.image_processing_class.from_dict(self.image_processor_dict	,					size=42	,					crop_size=84)
								self.assertEqual(image_processor.size	,					{"height": 42, "width": 42})
								self.assertEqual(image_processor.crop_size	,					{"height": 84, "width": 84})




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




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

								# Test not batched input
								snake_case__  :						int		=      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
								snake_case__  :						Optional[Any]		=      image_processing(_lowerCamelCase	,					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)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						str		=      self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								snake_case__  :						List[str]		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=_lowerCamelCase	,					numpify=_lowerCamelCase)
								for image in image_inputs:
									self.assertIsInstance(_lowerCamelCase	,					np.ndarray)

								# Test not batched input
								snake_case__  :						List[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
								snake_case__  :						List[Any]		=      image_processing(_lowerCamelCase	,					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)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						int		=      self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								snake_case__  :						List[Any]		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=_lowerCamelCase	,					torchify=_lowerCamelCase)
								for image in image_inputs:
									self.assertIsInstance(_lowerCamelCase	,					torch.Tensor)

								# Test not batched input
								snake_case__  :						List[str]		=      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
								snake_case__  :						Any		=      image_processing(_lowerCamelCase	,					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"],
								    )	,					)











'''simple docstring'''




from itertools import permutations




def     snake_case__     (			lowerCamelCase__					: int = 5      ) -> int:
					if gon_side < 3 or gon_side > 5:
										raise ValueError('''gon_side must be in the range [3, 5]'''      )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					A_			:  int			=				list(range(gon_side + 1      ,		0      ,		-1      )      )
					A_			:  List[Any]			=				list(range(gon_side + 2      ,		gon_side * 2 + 1      )      )

					for perm in permutations(small_numbers + big_numbers      ):
										A_			:  Optional[int]			=				generate_gon_ring(lowerCamelCase__      ,		list(lowerCamelCase__      )      )
										if is_magic_gon(lowerCamelCase__      ):
															return int(''''''.join(str(lowerCamelCase__      ) for n in numbers      )      )

					A_			:  Optional[Any]			=				f'Magic {gon_side}-gon ring is impossible'
					raise ValueError(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  List[Any]			=				[0] * (gon_side * 3)
					A_			:  Optional[Any]			=				perm[0:3]
					perm.append(perm[1]      )

					A_			:  Optional[Any]			=				1 if gon_side < 5 else 2

					for i in range(1      ,		len(lowerCamelCase__      ) // 3 + magic_number      ):
										A_			:  List[str]			=				perm[2 * i + 1]
										A_			:  str			=				result[3 * i - 1]
										A_			:  Union[str, Any]			=				perm[2 * i + 2]

					return result




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> bool:
					if len(lowerCamelCase__      ) % 3 != 0:
										raise ValueError('''a gon ring should have a length that is a multiple of 3'''      )

					if min(numbers[::3]      ) != numbers[0]:
										return False

					A_			:  Union[str, Any]			=				sum(numbers[:3]      )

					return all(sum(numbers[i : i + 3]      ) == total for i in range(3      ,		len(lowerCamelCase__      )      ,		3      )      )


if __name__ == "__main__":
							print(solution())



