
"""simple docstring"""




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
	import numpy as np
	import torch

if is_vision_available():
	from PIL import Image

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False     ):
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					SCREAMING_SNAKE_CASE           =      []
					for i in range(image_processor_tester.batch_size     ):
										if equal_resolution:
															SCREAMING_SNAKE_CASE           =      SCREAMING_SNAKE_CASE           =      image_processor_tester.max_resolution
										else:
															# To avoid getting image width/height 0
															SCREAMING_SNAKE_CASE           =      image_processor_tester.min_resolution
															if getattr(SCREAMING_SNAKE_CASE_,	'size_divisor',	SCREAMING_SNAKE_CASE_     ):
																				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																				SCREAMING_SNAKE_CASE           =      max(image_processor_tester.size_divisor,	SCREAMING_SNAKE_CASE_     )
															SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      np.random.choice(np.arange(SCREAMING_SNAKE_CASE_,	image_processor_tester.max_resolution     ),	2     )
										image_inputs.append(
										    np.random.randint(2_5_5,	size=(image_processor_tester.num_channels, width, height),	dtype=np.uinta     )     )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										SCREAMING_SNAKE_CASE           =      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,	0,	-1     )     ) for image in image_inputs]

					if torchify:
										SCREAMING_SNAKE_CASE           =      [torch.from_numpy(SCREAMING_SNAKE_CASE_     ) for image in image_inputs]

					return image_inputs

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=1_0,	SCREAMING_SNAKE_CASE_=1_0,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False     ):
					SCREAMING_SNAKE_CASE           =      []
					for i in range(image_processor_tester.num_frames     ):
										video.append(np.random.randint(2_5_5,	size=(image_processor_tester.num_channels, width, height),	dtype=np.uinta     )     )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										SCREAMING_SNAKE_CASE           =      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,	0,	-1     )     ) for frame in video]

					if torchify:
										SCREAMING_SNAKE_CASE           =      [torch.from_numpy(SCREAMING_SNAKE_CASE_     ) for frame in video]

					return video

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False     ):
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					SCREAMING_SNAKE_CASE           =      []
					for i in range(image_processor_tester.batch_size     ):
										if equal_resolution:
															SCREAMING_SNAKE_CASE           =      SCREAMING_SNAKE_CASE           =      image_processor_tester.max_resolution
										else:
															SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      np.random.choice(
															    np.arange(image_processor_tester.min_resolution,	image_processor_tester.max_resolution     ),	2     )
															SCREAMING_SNAKE_CASE           =      prepare_video(
															    image_processor_tester=SCREAMING_SNAKE_CASE_,	width=SCREAMING_SNAKE_CASE_,	height=SCREAMING_SNAKE_CASE_,	numpify=SCREAMING_SNAKE_CASE_,	torchify=SCREAMING_SNAKE_CASE_,	)
										video_inputs.append(SCREAMING_SNAKE_CASE_     )

					return video_inputs




class 	UpperCamelCase      :

				"""simple docstring"""



				UpperCAmelCase_							: Union[str, Any]		    =							None


				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.image_processing_class(**self.image_processor_dict     )
									SCREAMING_SNAKE_CASE           =      json.loads(image_processor.to_json_string()     )
									for key, value in self.image_processor_dict.items():
														self.assertEqual(obj[key]						,      lowercase__     )


				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.image_processing_class(**self.image_processor_dict     )

									with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE           =      os.path.join(lowercase__						,      'image_processor.json'     )
														image_processor_first.to_json_file(lowercase__     )
														SCREAMING_SNAKE_CASE           =      self.image_processing_class.from_json_file(lowercase__     )

									self.assertEqual(image_processor_second.to_dict()						,      image_processor_first.to_dict()     )


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.image_processing_class(**self.image_processor_dict     )

									with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE           =      image_processor_first.save_pretrained(lowercase__     )[0]
														check_json_file_has_correct_format(lowercase__     )
														SCREAMING_SNAKE_CASE           =      self.image_processing_class.from_pretrained(lowercase__     )

									self.assertEqual(image_processor_second.to_dict()						,      image_processor_first.to_dict()     )


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.image_processing_class()
									self.assertIsNotNone(lowercase__     )




				@require_torch
				@require_vision
				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									if self.test_cast_dtype is not None:
														# Initialize image_processor
														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=lowercase__						,      torchify=lowercase__     )

														SCREAMING_SNAKE_CASE           =      image_processor(lowercase__						,      return_tensors='pt'     )
														# for layoutLM compatiblity
														self.assertEqual(encoding.pixel_values.device						,      torch.device('cpu'     )     )
														self.assertEqual(encoding.pixel_values.dtype						,      torch.floataa     )

														SCREAMING_SNAKE_CASE           =      image_processor(lowercase__						,      return_tensors='pt'     ).to(torch.floataa     )
														self.assertEqual(encoding.pixel_values.device						,      torch.device('cpu'     )     )
														self.assertEqual(encoding.pixel_values.dtype						,      torch.floataa     )

														SCREAMING_SNAKE_CASE           =      image_processor(lowercase__						,      return_tensors='pt'     ).to('cpu'						,      torch.bfloataa     )
														self.assertEqual(encoding.pixel_values.device						,      torch.device('cpu'     )     )
														self.assertEqual(encoding.pixel_values.dtype						,      torch.bfloataa     )

														with self.assertRaises(lowercase__     ):
																			SCREAMING_SNAKE_CASE           =      image_processor(lowercase__						,      return_tensors='pt'     ).to(torch.bfloataa						,      'cpu'     )

														# Try with text + image feature
														SCREAMING_SNAKE_CASE           =      image_processor(lowercase__						,      return_tensors='pt'     )
														encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]     )}     )
														SCREAMING_SNAKE_CASE           =      encoding.to(torch.floataa     )

														self.assertEqual(encoding.pixel_values.device						,      torch.device('cpu'     )     )
														self.assertEqual(encoding.pixel_values.dtype						,      torch.floataa     )
														self.assertEqual(encoding.input_ids.dtype						,      torch.long     )


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					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
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 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

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

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


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