


from __future__ import annotations

from math import gcd





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			int = 2						,	UpperCAmelCase      :			int = 1						,	UpperCAmelCase      :			int = 3						,	):
						"""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(UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int      ) -> int:
												return (pow(UpperCAmelCase						,	2      ) + step) % modulus

						for _ in range(UpperCAmelCase      ):
												# These track the position within the cycle detection logic.
												__lowerCamelCase     :      str       						=						seed
												__lowerCamelCase     :      Tuple       						=						seed

												while True:
																		# At each iteration, the tortoise moves one step and the hare moves two.
																		__lowerCamelCase     :      Optional[Any]       						=						rand_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
																		__lowerCamelCase     :      Any       						=						rand_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
																		__lowerCamelCase     :      Dict       						=						rand_fn(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )

																		# 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``.
																		__lowerCamelCase     :      Tuple       						=						gcd(hare - tortoise						,	UpperCAmelCase      )

																		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.
												__lowerCamelCase     :      str       						=						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

				__UpperCamelCase    :						int     =							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',
				)
				__UpperCamelCase    :						str     =							parser.parse_args()

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







import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf



@require_vision
@require_torchvision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      Union[str, Any]       						=						SamImageProcessor()
         __lowerCamelCase     :      int       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Tuple       ,  **_lowerCamelCase  : Any							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      Optional[int]       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         __lowerCamelCase     :      Optional[int]       						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         __lowerCamelCase     :      Optional[Any]       						=						SamProcessor.from_pretrained(self.tmpdirname       ,  do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      List[str]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""np"""							)
         __lowerCamelCase     :      List[Any]       						=						processor(images=_lowerCamelCase       ,  return_tensors="""np"""							)

         input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop original_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2							)


   @require_torch
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.get_image_processor()

         __lowerCamelCase     :      Union[str, Any]       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						[torch.ones((1, 3, 5, 5)							)]

         __lowerCamelCase     :      Any       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      Any       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      Tuple       						=						processor.post_process_masks(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Union[str, Any]       						=						processor.post_process_masks(
             _lowerCamelCase       ,  torch.tensor(_lowerCamelCase							)       ,  torch.tensor(_lowerCamelCase							)							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         # should also work with np
         __lowerCamelCase     :      Any       						=						[np.ones((1, 3, 5, 5)							)]
         __lowerCamelCase     :      int       						=						processor.post_process_masks(_lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)							)

         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      List[str]       						=						[[1, 0], [0, 1]]
         with self.assertRaises(_lowerCamelCase							):
               __lowerCamelCase     :      Union[str, Any]       						=						processor.post_process_masks(_lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)							)



@require_vision
@require_tf
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      int       						=						SamImageProcessor()
         __lowerCamelCase     :      Dict       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : List[str]       ,  **_lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      str       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						SamProcessor(image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         __lowerCamelCase     :      Dict       						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         __lowerCamelCase     :      Tuple       						=						SamProcessor.from_pretrained(self.tmpdirname       ,  do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      List[Any]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""np"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						processor(images=_lowerCamelCase       ,  return_tensors="""np"""							)

         input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop reshaped_input_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2							)


   @require_tf
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      str       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						[tf.ones((1, 3, 5, 5)							)]

         __lowerCamelCase     :      Optional[Any]       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      List[str]       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      Optional[int]       						=						processor.post_process_masks(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""tf"""							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Optional[Any]       						=						processor.post_process_masks(
             _lowerCamelCase       ,  tf.convert_to_tensor(_lowerCamelCase							)       ,  tf.convert_to_tensor(_lowerCamelCase							)       ,  return_tensors="""tf"""       ,  )
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         # should also work with np
         __lowerCamelCase     :      Tuple       						=						[np.ones((1, 3, 5, 5)							)]
         __lowerCamelCase     :      str       						=						processor.post_process_masks(
             _lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)       ,  return_tensors="""tf"""							)

         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Union[str, Any]       						=						[[1, 0], [0, 1]]
         with self.assertRaises(tf.errors.InvalidArgumentError							):
               __lowerCamelCase     :      Optional[Any]       						=						processor.post_process_masks(
                   _lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)       ,  return_tensors="""tf"""							)



@require_vision
@require_torchvision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      Union[str, Any]       						=						SamImageProcessor()
         __lowerCamelCase     :      Dict       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Optional[int]       ,  **_lowerCamelCase  : Tuple							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      str       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   @is_pt_tf_cross_test
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						np.random.randint(0       ,  2       ,  size=(1, 3, 5, 5)							).astype(np.floataa							)
         __lowerCamelCase     :      Tuple       						=						[tf.convert_to_tensor(_lowerCamelCase							)]
         __lowerCamelCase     :      List[str]       						=						[torch.tensor(_lowerCamelCase							)]

         __lowerCamelCase     :      Optional[int]       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      Tuple       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      int       						=						processor.post_process_masks(
             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""tf"""							)
         __lowerCamelCase     :      Any       						=						processor.post_process_masks(
             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""pt"""							)

         self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()							)							)


   @is_pt_tf_cross_test
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						self.get_image_processor()

         __lowerCamelCase     :      int       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      Any       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      Any       						=						image_processor(_lowerCamelCase       ,  return_tensors="""pt"""							)["""pixel_values"""].numpy()
         __lowerCamelCase     :      Any       						=						processor(images=_lowerCamelCase       ,  return_tensors="""pt"""							)["""pixel_values"""].numpy()

         __lowerCamelCase     :      Dict       						=						image_processor(_lowerCamelCase       ,  return_tensors="""tf"""							)["""pixel_values"""].numpy()
         __lowerCamelCase     :      int       						=						processor(images=_lowerCamelCase       ,  return_tensors="""tf"""							)["""pixel_values"""].numpy()

         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)
         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)
         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)



