


"""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 ConvNextImageProcessor


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=7	,					lowerCamelCase__=3	,					lowerCamelCase__=18	,					lowerCamelCase__=30	,					lowerCamelCase__=400	,					lowerCamelCase__=True	,					lowerCamelCase__=None	,					lowerCamelCase__=0.8_75	,					lowerCamelCase__=True	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					lowerCamelCase__=[0.5, 0.5, 0.5]	,					)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      size if size is not None else {"shortest_edge": 20}
								snake_case__  :						List[Any]		=      parent
								snake_case__  :						Dict		=      batch_size
								snake_case__  :						Any		=      num_channels
								snake_case__  :						Optional[int]		=      image_size
								snake_case__  :						int		=      min_resolution
								snake_case__  :						Tuple		=      max_resolution
								snake_case__  :						int		=      do_resize
								snake_case__  :						Union[str, Any]		=      size
								snake_case__  :						Tuple		=      crop_pct
								snake_case__  :						int		=      do_normalize
								snake_case__  :						str		=      image_mean
								snake_case__  :						Union[str, Any]		=      image_std







							def        UpperCAmelCase (						self)   ->	Dict:







								'''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,
								    "crop_pct": self.crop_pct,
								}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  ConvNextImageProcessor if is_vision_available() else None




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      ConvNextImageProcessingTester(self)




							@property
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




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







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_resize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"size"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"crop_pct"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"do_normalize"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_mean"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"image_std"))




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 20})

								snake_case__  :						List[Any]		=      self.image_processing_class.from_dict(self.image_processor_dict	,					size=42)
								self.assertEqual(image_processor.size	,					{"shortest_edge": 42})




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								snake_case__  :						Union[str, Any]		=      prepare_image_inputs(self.image_processor_tester	,					equal_resolution=lowerCamelCase__)
								for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__	,					Image.Image)

								# Test not batched input
								snake_case__  :						List[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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								snake_case__  :						int		=      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
								snake_case__  :						Optional[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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								snake_case__  :						Any		=      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
								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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)

								# Test batched
								snake_case__  :						List[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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )	,					)









"""simple docstring"""






import math



def 			A__		(					_UpperCAmelCase    :      int	)							->	bool:

	'''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(_UpperCAmelCase	) + 1	)				,    6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True



def 			A__		(					_UpperCAmelCase    :      int = 60_08_51_47_51_43	)							->	int:

	'''simple docstring'''


	try:
		snake_case__  :						List[Any]		=      int(_UpperCAmelCase	)
	except (TypeError, ValueError):
		raise TypeError("Parameter n must be int or castable to int."	)
	if n <= 0:
		raise ValueError("Parameter n must be greater than or equal to one."	)
	snake_case__  :						int		=      0
	if is_prime(_UpperCAmelCase	):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(_UpperCAmelCase	):
		return n
	for i in range(3				,    int(math.sqrt(_UpperCAmelCase	)	) + 1				,    2	):
		if n % i == 0:
			if is_prime(n // i	):
				snake_case__  :						int		=      n // i
				break
			elif is_prime(_UpperCAmelCase	):
				snake_case__  :						List[Any]		=      i
	return max_number


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





