"""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 MobileNetVaImageProcessor



class      lowerCAmelCase_			(	unittest.TestCase    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase=7						,			lowerCAmelCase=3						,			lowerCAmelCase=18						,			lowerCAmelCase=30						,			lowerCAmelCase=4_00						,			lowerCAmelCase=True						,			lowerCAmelCase=None						,			lowerCAmelCase=True						,			lowerCAmelCase=None						,			):






										"""simple docstring"""

										snake_case	    =	size if size is not None else {'''shortest_edge''': 20}
										snake_case	    =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										snake_case	    =	parent
										snake_case	    =	batch_size
										snake_case	    =	num_channels
										snake_case	    =	image_size
										snake_case	    =	min_resolution
										snake_case	    =	max_resolution
										snake_case	    =	do_resize
										snake_case	    =	size
										snake_case	    =	do_center_crop
										snake_case	    =	crop_size

				def 						snake_case       ( self			):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}





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



				"""simple docstring"""
				_lowerCAmelCase   :			Union[str, Any]	     =  MobileNetVaImageProcessor if is_vision_available() else None

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	MobileNetVaImageProcessingTester(self			)

				@property
				def 						snake_case       ( self			):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__lowercase						,			'do_resize'			)			)
										self.assertTrue(hasattr(__lowercase						,			'size'			)			)
										self.assertTrue(hasattr(__lowercase						,			'do_center_crop'			)			)
										self.assertTrue(hasattr(__lowercase						,			'crop_size'			)			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	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}			)

										snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										pass

				def 						snake_case       ( self			):






										"""simple docstring"""

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

										# Test not batched input
										snake_case	    =	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	    =	image_processing(__lowercase						,			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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										snake_case	    =	prepare_image_inputs(self.image_processor_tester						,			equal_resolution=__lowercase						,			numpify=__lowercase			)
										for image in image_inputs:
																self.assertIsInstance(__lowercase						,			np.ndarray			)

										# Test not batched input
										snake_case	    =	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	    =	image_processing(__lowercase						,			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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										snake_case	    =	prepare_image_inputs(self.image_processor_tester						,			equal_resolution=__lowercase						,			torchify=__lowercase			)
										for image in image_inputs:
																self.assertIsInstance(__lowercase						,			torch.Tensor			)

										# Test not batched input
										snake_case	    =	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	    =	image_processing(__lowercase						,			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 			_lowerCAmelCase       (					__lowerCAmelCase = 1000000      )   ->					int:


				"""simple docstring"""

				snake_case__	:      Dict     		=	0

				for outer_width in range(3  ,  (limit // 4) + 2      ):
								if outer_width**2 > limit:
												snake_case__	:      Any     		=	max(ceil(sqrt(outer_width**2 - limit      )      )  ,  1      )
								else:
												snake_case__	:      int     		=	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() = }""")

