



'''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 ViTImageProcessor

class       __magic_name__		(       unittest.TestCase  ):







							def __init__(				self   :			Optional[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			List[Any]=13      ,_UpperCAmelCase   :			Tuple=3      ,_UpperCAmelCase   :			Any=224      ,_UpperCAmelCase   :			List[str]=30      ,_UpperCAmelCase   :			List[Any]=400      ,_UpperCAmelCase   :			List[Any]=True      ,_UpperCAmelCase   :			List[str]=None      ,_UpperCAmelCase   :			Any=True      ,_UpperCAmelCase   :			Optional[int]=[0.5, 0.5, 0.5]      ,_UpperCAmelCase   :			Tuple=[0.5, 0.5, 0.5]      ,):
									_a	: List[str]								=				size if size is not None else {'height': 18, 'width': 18}
									_a	: str								=				parent
									_a	: Optional[int]								=				batch_size
									_a	: Optional[int]								=				num_channels
									_a	: Any								=				image_size
									_a	: List[str]								=				min_resolution
									_a	: Union[str, Any]								=				max_resolution
									_a	: Dict								=				do_resize
									_a	: Dict								=				size
									_a	: Union[str, Any]								=				do_normalize
									_a	: str								=				image_mean
									_a	: int								=				image_std







							def 				__lowercase     (				self   :			str						):
									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       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       int  							=   ViTImageProcessor if is_vision_available() else None







							def 				__lowercase     (				self   :			int						):
									_a	: Dict								=				EfficientFormerImageProcessorTester(self						)







							@property
							def 				__lowercase     (				self   :			List[str]						):
									return self.image_proc_tester.prepare_image_processor_dict()







							def 				__lowercase     (				self   :			Dict						):
									_a	: Dict								=				self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'image_mean'						)						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'image_std'						)						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'do_normalize'						)						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'do_resize'						)						)
									self.assertTrue(hasattr(_UpperCAmelCase      ,'size'						)						)







							def 				__lowercase     (				self   :			int						):
									pass







							def 				__lowercase     (				self   :			Tuple						):
									# Initialize image_processor
									_a	: int								=				self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_a	: List[str]								=				prepare_image_inputs(self.image_proc_tester      ,equal_resolution=_UpperCAmelCase						)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase      ,Image.Image						)

									# Test not batched input
									_a	: List[Any]								=				image_processor(image_inputs[0]      ,return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,(
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['height'],
									        self.image_proc_tester.size['width'],
									    )      ,)

									# Test batched
									_a	: Optional[Any]								=				image_processor(_UpperCAmelCase      ,return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,(
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['height'],
									        self.image_proc_tester.size['width'],
									    )      ,)







							def 				__lowercase     (				self   :			str						):
									# Initialize image_processor
									_a	: Union[str, Any]								=				self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_a	: List[Any]								=				prepare_image_inputs(self.image_proc_tester      ,equal_resolution=_UpperCAmelCase      ,numpify=_UpperCAmelCase						)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase      ,np.ndarray						)

									# Test not batched input
									_a	: Any								=				image_processor(image_inputs[0]      ,return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,(
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['height'],
									        self.image_proc_tester.size['width'],
									    )      ,)

									# Test batched
									_a	: List[str]								=				image_processor(_UpperCAmelCase      ,return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,(
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['height'],
									        self.image_proc_tester.size['width'],
									    )      ,)







							def 				__lowercase     (				self   :			Optional[Any]						):
									# Initialize image_processor
									_a	: List[str]								=				self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_a	: Union[str, Any]								=				prepare_image_inputs(self.image_proc_tester      ,equal_resolution=_UpperCAmelCase      ,torchify=_UpperCAmelCase						)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase      ,torch.Tensor						)

									# Test not batched input
									_a	: Optional[int]								=				image_processor(image_inputs[0]      ,return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,(
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['height'],
									        self.image_proc_tester.size['width'],
									    )      ,)

									# Test batched
									_a	: Union[str, Any]								=				image_processor(_UpperCAmelCase      ,return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,(
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['height'],
									        self.image_proc_tester.size['width'],
									    )      ,)









'''simple docstring'''

from __future__ import annotations

from math import gcd



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 2		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 3		,     )     -> int | None:
		# A value less than 2 can cause an infinite loop in the algorithm.
		if num < 2:
				raise ValueError('The input value cannot be less than 2'    )

		# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
		# algorithm struggles to find factors that are divisible by two.
		# As a workaround, we specifically check for two and even inputs.
		#   See: https://math.stackexchange.com/a/2856214/165820
		if num > 2 and num % 2 == 0:
				return 2

		# Pollard's Rho algorithm requires a function that returns pseudorandom
		# values between 0 <= X < ``num``.  It doesn't need to be random in the
		# sense that the output value is cryptographically secure or difficult
		# to calculate, it only needs to be random in the sense that all output
		# values should be equally likely to appear.
		# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
		# However, the success of Pollard's algorithm isn't guaranteed and is
		# determined in part by the initial seed and the chosen random function.
		# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
		# where ``C`` is a value that we can modify between each attempt.
		def rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ) -> int:
				return (pow(lowerCAmelCase_		,     2    ) + step) % modulus

		for _ in range(lowerCAmelCase_    ):
				# These track the position within the cycle detection logic.
				_a	: Optional[int]								=				seed
				_a	: str								=				seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						_a	: str								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Tuple								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

						# At some point both the tortoise and the hare will enter a cycle whose
						# length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
						# the tortoise and hare will end up on the same value modulo ``p``.
						# We can detect when this happens because the position difference between
						# the tortoise and the hare will share a common divisor with ``num``.
						_a	: Optional[Any]								=				gcd(hare - tortoise		,     lowerCAmelCase_    )

						if divisor == 1:
								# No common divisor yet, just keep searching.
								continue
						else:
								# We found a common divisor!
								if divisor == num:
										# Unfortunately, the divisor is ``num`` itself and is useless.
										break
								else:
										# The divisor is a nontrivial factor of ``num``!
										return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
				_a	: Union[str, Any]								=				hare

				# The new step value for the random function can just be incremented.
				# At first the results will be similar to what the old function would
				# have produced, but the value will quickly diverge after a bit.
				step += 1

		# We haven't found a divisor within the requested number of attempts.
		# We were unlucky or ``num`` itself is actually prime.
		return None


if __name__ == "__main__":
							import argparse

							__lowerCAmelCase		     =  argparse.ArgumentParser()
							parser.add_argument(
							    '''num''',
							    type=int,
							    help='''The value to find a divisor of''',
							)
							parser.add_argument(
							    '''--attempts''',
							    type=int,
							    default=3,
							    help='''The number of attempts before giving up''',
							)
							__lowerCAmelCase		     =  parser.parse_args()

							__lowerCAmelCase		     =  pollard_rho(args.num, attempts=args.attempts)
							if divisor is None:
														print(f"""{args.num} is probably prime""")
							else:
														__lowerCAmelCase		     =  args.num // divisor
														print(f"""{args.num} = {divisor} * {quotient}""")




