



"""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 LevitImageProcessor
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							int						,     snake_case:							int						,     snake_case:							Tuple=7						,     snake_case:							Optional[int]=3						,     snake_case:							List[Any]=18						,     snake_case:							Dict=30						,     snake_case:							Optional[int]=400						,     snake_case:							int=True						,     snake_case:							Dict=None						,     snake_case:							Dict=True						,     snake_case:							Tuple=None						,     snake_case:							Optional[Any]=True						,     snake_case:							Dict=[0.5, 0.5, 0.5]						,     snake_case:							Union[str, Any]=[0.5, 0.5, 0.5]						,     )   ->	int:
											snake_case_ :Tuple						=			size if size is not None else {"""shortest_edge""": 18}
											snake_case_ :Dict						=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											snake_case_ :str						=			parent
											snake_case_ :int						=			batch_size
											snake_case_ :Tuple						=			num_channels
											snake_case_ :Any						=			image_size
											snake_case_ :int						=			min_resolution
											snake_case_ :Dict						=			max_resolution
											snake_case_ :Any						=			do_resize
											snake_case_ :Optional[int]						=			size
											snake_case_ :Optional[int]						=			do_center_crop
											snake_case_ :Dict						=			crop_size
											snake_case_ :int						=			do_normalize
											snake_case_ :Optional[Any]						=			image_mean
											snake_case_ :Optional[Any]						=			image_std






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	str:
											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 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     LevitImageProcessor if is_vision_available() else None






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	str:
											snake_case_ :List[str]						=			LevitImageProcessingTester(self						)






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											return self.image_processor_tester.prepare_image_processor_dict()






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Any:
											snake_case_ :Any						=			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    lowerCAmelCase_				(       self:							Dict						)   ->	List[str]:
											snake_case_ :List[Any]						=			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_ :Tuple						=			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]						)   ->	List[str]:
											pass






					def    lowerCAmelCase_				(       self:							int						)   ->	Optional[Any]:
											# Initialize image_processing
											snake_case_ :str						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_ :List[str]						=			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_ :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(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[Any]						)   ->	List[Any]:
											# Initialize image_processing
											snake_case_ :List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_ :Union[str, Any]						=			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_ :Union[str, 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[str]						=			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]						)   ->	int:
											# Initialize image_processing
											snake_case_ :Optional[int]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_ :Any						=			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_ :Dict						=			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[int]						=			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"""],
											    )						,     )




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				set(range(3  ,						lowerCAmelCase_  ,						2   )   )
					primes.add(2   )
					for p in range(3  ,						lowerCAmelCase_  ,						2   ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[float(lowerCAmelCase_   ) for n in range(limit + 1   )]

					for p in primes:
										for n in range(lowerCAmelCase_  ,						limit + 1  ,						lowerCAmelCase_   ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]   )   )


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