






import math






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5	,     int(math.sqrt(lowerCamelCase_       ) + 1       )	,     6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True






def 						a    (		lowerCamelCase_ = 0.1       ):






				'''simple docstring'''
				lowercase__     				=    3
				lowercase__     				=    3

				while primes / (2 * j - 1) >= ratio:
								for i in range(j * j + j + 1	,     (j + 2) * (j + 2)	,     j + 1       ):
												primes += is_prime(lowerCamelCase_       )
								j += 2
				return j


if __name__ == "__main__":
							import doctest

							doctest.testmod()












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    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

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




				def        lowercase__     (  self		:					Any						):

								'''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    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    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}						)

								lowercase__     				=    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        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    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
								lowercase__     				=    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
								lowercase__     				=    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    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
								lowercase__     				=    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
								lowercase__     				=    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




