

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 LevitImageProcessor







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=7   ,     snake_case=3   ,     snake_case=18   ,     snake_case=30   ,     snake_case=400   ,     snake_case=True   ,     snake_case=None   ,     snake_case=True   ,     snake_case=None   ,     snake_case=True   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=[0.5, 0.5, 0.5]   ,     ):
											snake_case_           =					size if size is not None else {'shortest_edge': 18}
											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
											snake_case_           =					do_normalize
											snake_case_           =					image_mean
											snake_case_           =					image_std




				def 					a							(      self ):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "do_center_crop": self.do_center_crop,
											    "size": self.size,
											    "crop_size": self.crop_size,
											}







@require_torch
@require_vision
class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				str     =    LevitImageProcessor if is_vision_available() else None




				def 					a							(      self ):
											snake_case_           =					LevitImageProcessingTester(self )




				@property
				def 					a							(      self ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class(**self.image_processor_dict )
											self.assertTrue(hasattr(snake_case   ,     'image_mean' ) )
											self.assertTrue(hasattr(snake_case   ,     'image_std' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_normalize' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_resize' ) )
											self.assertTrue(hasattr(snake_case   ,     'do_center_crop' ) )
											self.assertTrue(hasattr(snake_case   ,     'size' ) )




				def 					a							(      self ):
											snake_case_           =					self.image_processing_class.from_dict(self.image_processor_dict )
											self.assertEqual(image_processor.size   ,     {'shortest_edge': 18} )
											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 					a							(      self ):
											pass




				def 					a							(      self ):
											# Initialize image_processing
											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=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     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(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 					a							(      self ):
											# Initialize image_processing
											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=snake_case   ,     numpify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     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(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 					a							(      self ):
											# Initialize image_processing
											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=snake_case   ,     torchify=snake_case )
											for image in image_inputs:
																		self.assertIsInstance(snake_case   ,     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(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			(				UpperCamelCase__ = 50000000   ):







							'''simple docstring'''






							snake_case_           =					set()
							snake_case_           =					int((limit - 24) ** (1 / 2)   )

							snake_case_           =					set(range(3     ,							prime_square_limit + 1     ,							2   )   )
							primes.add(2   )
							for p in range(3     ,							prime_square_limit + 1     ,							2   ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p     ,							prime_square_limit + 1     ,							UpperCamelCase__   )   )   )

							for primea in primes:
														snake_case_           =					primea * primea
														for primea in primes:
																					snake_case_           =					primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												snake_case_           =					primea * primea * primea * primea
																												snake_case_           =					square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(UpperCamelCase__   )

							return len(UpperCamelCase__   )


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




