


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 MobileViTImageProcessor







class 	snake_case_       (unittest.TestCase				):
					def __init__(				self       :Dict      ,__snake_case       :List[Any]      ,__snake_case       :Dict=7      ,__snake_case       :int=3      ,__snake_case       :Union[str, Any]=18      ,__snake_case       :Union[str, Any]=30      ,__snake_case       :Any=4_00      ,__snake_case       :List[Any]=True      ,__snake_case       :str=None      ,__snake_case       :List[Any]=True      ,__snake_case       :Any=None      ,__snake_case       :Optional[Any]=True      ,) ->   int:
												a__					=					size if size is not None else {'shortest_edge': 20}
												a__					=					crop_size if crop_size is not None else {'height': 18, 'width': 18}
												a__					=					parent
												a__					=					batch_size
												a__					=					num_channels
												a__					=					image_size
												a__					=					min_resolution
												a__					=					max_resolution
												a__					=					do_resize
												a__					=					size
												a__					=					do_center_crop
												a__					=					crop_size
												a__					=					do_flip_channel_order



					def 			lowerCamelCase__(				self       :Any				) ->   Any:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_flip_channel_order": self.do_flip_channel_order,
												}







@require_torch
@require_vision
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MobileViTImageProcessor if is_vision_available() else None
					def 			lowerCamelCase__(				self       :Any				) ->   Optional[Any]:
												a__					=					MobileViTImageProcessingTester(self				)
					@property
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Any:
												return self.image_processor_tester.prepare_image_processor_dict()
					def 			lowerCamelCase__(				self       :List[str]				) ->   Union[str, Any]:
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												self.assertTrue(hasattr(__snake_case      ,'do_resize'				)				)
												self.assertTrue(hasattr(__snake_case      ,'size'				)				)
												self.assertTrue(hasattr(__snake_case      ,'do_center_crop'				)				)
												self.assertTrue(hasattr(__snake_case      ,'center_crop'				)				)
												self.assertTrue(hasattr(__snake_case      ,'do_flip_channel_order'				)				)
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					self.image_processing_class.from_dict(self.image_processor_dict				)
												self.assertEqual(image_processor.size      ,{'shortest_edge': 20}				)
												self.assertEqual(image_processor.crop_size      ,{'height': 18, 'width': 18}				)

												a__					=					self.image_processing_class.from_dict(self.image_processor_dict      ,size=42      ,crop_size=84				)
												self.assertEqual(image_processor.size      ,{'shortest_edge': 42}				)
												self.assertEqual(image_processor.crop_size      ,{'height': 84, 'width': 84}				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												pass
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												a__					=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=__snake_case				)
												for image in image_inputs:
																			self.assertIsInstance(__snake_case      ,Image.Image				)

												# Test not batched input
												a__					=					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
												a__					=					image_processing(__snake_case      ,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 			lowerCamelCase__(				self       :int				) ->   List[Any]:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												a__					=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=__snake_case      ,numpify=__snake_case				)
												for image in image_inputs:
																			self.assertIsInstance(__snake_case      ,np.ndarray				)

												# Test not batched input
												a__					=					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
												a__					=					image_processing(__snake_case      ,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 			lowerCamelCase__(				self       :List[str]				) ->   Dict:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												a__					=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=__snake_case      ,torchify=__snake_case				)
												for image in image_inputs:
																			self.assertIsInstance(__snake_case      ,torch.Tensor				)

												# Test not batched input
												a__					=					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
												a__					=					image_processing(__snake_case      ,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'],
												    )      ,)








from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0       ):
							a__					=					0

							for outer_width in range(3 ,   (limit // 4) + 2       ):
														if outer_width**2 > limit:
																					a__					=					max(ceil(sqrt(outer_width**2 - limit       )       ) ,   1       )
														else:
																					a__					=					1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


if __name__ == "__main__":
						print(f"""{solution() = }""")




