



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor



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=[0.5, 0.5, 0.5]		,__lowerCamelCase=[0.5, 0.5, 0.5]		,)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					size if size is not None else {'''height''': 18, '''width''': 18}
						lowerCAmelCase__					:    Any												=					parent
						lowerCAmelCase__					:    List[str]												=					batch_size
						lowerCAmelCase__					:    Optional[Any]												=					num_channels
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    List[str]												=					min_resolution
						lowerCAmelCase__					:    Optional[int]												=					max_resolution
						lowerCAmelCase__					:    Optional[int]												=					do_resize
						lowerCAmelCase__					:    List[str]												=					size
						lowerCAmelCase__					:    Any												=					do_normalize
						lowerCAmelCase__					:    str												=					image_mean
						lowerCAmelCase__					:    Dict												=					image_std



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}



@require_torch
@require_vision
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DPTImageProcessor if is_vision_available() else None



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					DPTImageProcessingTester(self			)



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_mean'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_std'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_normalize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_resize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''size'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size		,{'''height''': 18, '''width''': 18}			)

						lowerCAmelCase__					:    Optional[Any]												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=42			)
						self.assertEqual(image_processor.size		,{'''height''': 42, '''width''': 42}			)



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						lowerCAmelCase__					:    Optional[int]												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,Image.Image			)

						# Test not batched input
						lowerCAmelCase__					:    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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    int												=					image_processing(__lowerCamelCase		,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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase__					:    Union[str, Any]												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,numpify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,np.ndarray			)

						# Test not batched input
						lowerCAmelCase__					:    Optional[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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Dict												=					image_processing(__lowerCamelCase		,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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase__					:    Dict												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,torchify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,torch.Tensor			)

						# Test not batched input
						lowerCAmelCase__					:    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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Union[str, Any]												=					image_processing(__lowerCamelCase		,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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					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_)))

				lowerCAmelCase__					:    int												=					[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() = }""")

