




"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor

class   UpperCAmelCase_    (			unittest.TestCase		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_=7				,						UpperCamelCase_=3				,						UpperCamelCase_=18				,						UpperCamelCase_=30				,						UpperCamelCase_=4_00				,						UpperCamelCase_=True				,						UpperCamelCase_=None				,						UpperCamelCase_=True				,						UpperCamelCase_=False				,						UpperCamelCase_=True				,						UpperCamelCase_=True				,						UpperCamelCase_=[0.5, 0.5, 0.5]				,						UpperCamelCase_=[0.5, 0.5, 0.5]				,						)						->       Union[str, Any]:
					__lowercase			:  Dict								=       parent
					__lowercase			:  int								=       batch_size
					__lowercase			:  Union[str, Any]								=       num_channels
					__lowercase			:  Optional[Any]								=       image_size
					__lowercase			:  Tuple								=       min_resolution
					__lowercase			:  Union[str, Any]								=       max_resolution
					__lowercase			:  Any								=       do_resize
					__lowercase			:  List[Any]								=       size if size is not None else {'''height''': 18, '''width''': 20}
					__lowercase			:  Any								=       do_thumbnail
					__lowercase			:  str								=       do_align_axis
					__lowercase			:  Optional[Any]								=       do_pad
					__lowercase			:  Optional[Any]								=       do_normalize
					__lowercase			:  Tuple								=       image_mean
					__lowercase			:  Tuple								=       image_std
		def 	_lowerCamelCase							(						self       )						->       int:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_thumbnail": self.do_thumbnail,
					    "do_align_long_axis": self.do_align_axis,
					    "do_pad": self.do_pad,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}


@require_torch
@require_vision
class   UpperCAmelCase_    (			snake_case   ,					unittest.TestCase		):
		UpperCamelCase							=DonutImageProcessor if is_vision_available() else None
		def 	_lowerCamelCase							(						self       )						->       Any:
					__lowercase			:  Dict								=       DonutImageProcessingTester(self       )
		@property
		def 	_lowerCamelCase							(						self       )						->       int:
					return self.image_processor_tester.prepare_image_processor_dict()
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  Dict								=       self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''do_resize'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''size'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''do_thumbnail'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''do_align_long_axis'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''do_pad'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''do_normalize'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''image_mean'''       )       )
					self.assertTrue(hasattr(UpperCamelCase_				,						'''image_std'''       )       )
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					__lowercase			:  int								=       self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size				,						{'''height''': 18, '''width''': 20}       )

					__lowercase			:  List[str]								=       self.image_processing_class.from_dict(self.image_processor_dict				,						size=42       )
					self.assertEqual(image_processor.size				,						{'''height''': 42, '''width''': 42}       )

					# Previous config had dimensions in (width, height) order
					__lowercase			:  Any								=       self.image_processing_class.from_dict(self.image_processor_dict				,						size=(42, 84)       )
					self.assertEqual(image_processor.size				,						{'''height''': 84, '''width''': 42}       )
		def 	_lowerCamelCase							(						self       )						->       Dict:
					pass
		@is_flaky()
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					# Initialize image_processing
					__lowercase			:  Optional[Any]								=       self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					__lowercase			:  Union[str, Any]								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCamelCase_       )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_				,						Image.Image       )

					# Test not batched input
					__lowercase			:  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
					__lowercase			:  List[str]								=       image_processing(UpperCamelCase_				,						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'''],
					    )				,						)
		@is_flaky()
		def 	_lowerCamelCase							(						self       )						->       int:
					# Initialize image_processing
					__lowercase			:  int								=       self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					__lowercase			:  Tuple								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCamelCase_				,						numpify=UpperCamelCase_       )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_				,						np.ndarray       )

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

					# Test batched
					__lowercase			:  List[Any]								=       image_processing(UpperCamelCase_				,						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'''],
					    )				,						)
		@is_flaky()
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					# Initialize image_processing
					__lowercase			:  Any								=       self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					__lowercase			:  Optional[Any]								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=UpperCamelCase_				,						torchify=UpperCamelCase_       )
					for image in image_inputs:
								self.assertIsInstance(UpperCamelCase_				,						torch.Tensor       )

					# Test not batched input
					__lowercase			:  Tuple								=       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
					__lowercase			:  List[Any]								=       image_processing(UpperCamelCase_				,						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'''],
					    )				,						)







from __future__ import annotations

from math import gcd



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int = 2		,    lowerCAmelCase_				:int = 1		,    lowerCAmelCase_				:int = 3		,    )->int | None:
   '''simple docstring'''






   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_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				) -> int:
      return (pow(lowerCAmelCase_		,    2				) + step) % modulus

   for _ in range(lowerCAmelCase_				):
      # These track the position within the cycle detection logic.
      snake_case_            =       seed
      snake_case_            =       seed

      while True:
         # At each iteration, the tortoise moves one step and the hare moves two.
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       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``.
         snake_case_            =       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.
      snake_case_            =       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

   SCREAMING_SNAKE_CASE						:Union[str, Any]			=  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''',
   )
   SCREAMING_SNAKE_CASE						:List[str]			=  parser.parse_args()

   SCREAMING_SNAKE_CASE						:List[Any]			=  pollard_rho(args.num, attempts=args.attempts)
   if divisor is None:
      print(F'''{args.num} is probably prime''')
   else:
      SCREAMING_SNAKE_CASE						:List[str]			=  args.num // divisor
      print(F'''{args.num} = {divisor} * {quotient}''')




