


'''simple docstring'''







import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=7   ,						snake_case__=3   ,						snake_case__=18   ,						snake_case__=30   ,						snake_case__=400   ,						snake_case__=True   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=[0.5, 0.5, 0.5]   ,						snake_case__=[0.5, 0.5, 0.5]   ,						snake_case__=False   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							size if size is not None else {'height': 20, 'width': 20}
            _lowerCAmelCase     :				Optional[Any]       =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
            _lowerCAmelCase     :				Tuple       =							parent
            _lowerCAmelCase     :				Tuple       =							batch_size
            _lowerCAmelCase     :				Union[str, Any]       =							num_channels
            _lowerCAmelCase     :				Any       =							image_size
            _lowerCAmelCase     :				Union[str, Any]       =							min_resolution
            _lowerCAmelCase     :				Any       =							max_resolution
            _lowerCAmelCase     :				List[Any]       =							do_resize
            _lowerCAmelCase     :				Optional[Any]       =							size
            _lowerCAmelCase     :				Union[str, Any]       =							do_center_crop
            _lowerCAmelCase     :				Optional[int]       =							crop_size
            _lowerCAmelCase     :				Optional[int]       =							do_normalize
            _lowerCAmelCase     :				str       =							image_mean
            _lowerCAmelCase     :				int       =							image_std
            _lowerCAmelCase     :				List[str]       =							do_reduce_labels







     def a						(			self ):

            '''simple docstring'''

            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_reduce_labels": self.do_reduce_labels,
            }






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							load_dataset('hf-internal-testing/fixtures_ade20k' ,    split='test'       )

       _lowerCAmelCase     :				str       =							Image.open(dataset[0]['file']       )
       _lowerCAmelCase     :				List[Any]       =							Image.open(dataset[1]['file']       )

       return image, map






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				int       =							load_dataset('hf-internal-testing/fixtures_ade20k' ,    split='test'       )

       _lowerCAmelCase     :				Optional[int]       =							Image.open(ds[0]['file']       )
       _lowerCAmelCase     :				Tuple       =							Image.open(ds[1]['file']       )
       _lowerCAmelCase     :				Optional[int]       =							Image.open(ds[2]['file']       )
       _lowerCAmelCase     :				List[str]       =							Image.open(ds[3]['file']       )

       return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     BeitImageProcessor if is_vision_available() else None







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							BeitImageProcessingTester(self )







     @property
     def a						(			self ):

            '''simple docstring'''

            return self.image_processor_tester.prepare_image_processor_dict()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(snake_case__   ,						'do_resize' ) )
            self.assertTrue(hasattr(snake_case__   ,						'size' ) )
            self.assertTrue(hasattr(snake_case__   ,						'do_center_crop' ) )
            self.assertTrue(hasattr(snake_case__   ,						'center_crop' ) )
            self.assertTrue(hasattr(snake_case__   ,						'do_normalize' ) )
            self.assertTrue(hasattr(snake_case__   ,						'image_mean' ) )
            self.assertTrue(hasattr(snake_case__   ,						'image_std' ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size   ,						{'height': 20, 'width': 20} )
            self.assertEqual(image_processor.crop_size   ,						{'height': 18, 'width': 18} )
            self.assertEqual(image_processor.do_reduce_labels   ,						snake_case__ )

            _lowerCAmelCase     :				List[str]       =							self.image_processing_class.from_dict(
                self.image_processor_dict   ,						size=42   ,						crop_size=84   ,						reduce_labels=snake_case__ )
            self.assertEqual(image_processor.size   ,						{'height': 42, 'width': 42} )
            self.assertEqual(image_processor.crop_size   ,						{'height': 84, 'width': 84} )
            self.assertEqual(image_processor.do_reduce_labels   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

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

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

            # Test batched
            _lowerCAmelCase     :				Any       =							image_processing(snake_case__   ,						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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            _lowerCAmelCase     :				int       =							prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=snake_case__   ,						numpify=snake_case__ )
            for image in image_inputs:
                   self.assertIsInstance(snake_case__   ,						np.ndarray )

            # Test not batched input
            _lowerCAmelCase     :				str       =							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
            _lowerCAmelCase     :				Optional[Any]       =							image_processing(snake_case__   ,						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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            _lowerCAmelCase     :				Optional[Any]       =							prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=snake_case__   ,						torchify=snake_case__ )
            for image in image_inputs:
                   self.assertIsInstance(snake_case__   ,						torch.Tensor )

            # Test not batched input
            _lowerCAmelCase     :				List[str]       =							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
            _lowerCAmelCase     :				str       =							image_processing(snake_case__   ,						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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            _lowerCAmelCase     :				List[str]       =							prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=snake_case__   ,						torchify=snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							[]
            for image in image_inputs:
                   self.assertIsInstance(snake_case__   ,						torch.Tensor )
                   maps.append(torch.zeros(image.shape[-2:] ).long() )

            # Test not batched input
            _lowerCAmelCase     :				Union[str, Any]       =							image_processing(image_inputs[0]   ,						maps[0]   ,						return_tensors='pt' )
            self.assertEqual(
                encoding['pixel_values'].shape   ,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(
                encoding['labels'].shape   ,						(
                    1,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(encoding['labels'].dtype   ,						torch.long )
            self.assertTrue(encoding['labels'].min().item() >= 0 )
            self.assertTrue(encoding['labels'].max().item() <= 255 )

            # Test batched
            _lowerCAmelCase     :				Tuple       =							image_processing(snake_case__   ,						snake_case__   ,						return_tensors='pt' )
            self.assertEqual(
                encoding['pixel_values'].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'],
                )   ,						)
            self.assertEqual(
                encoding['labels'].shape   ,						(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(encoding['labels'].dtype   ,						torch.long )
            self.assertTrue(encoding['labels'].min().item() >= 0 )
            self.assertTrue(encoding['labels'].max().item() <= 255 )

            # Test not batched input (PIL images)
            _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							prepare_semantic_single_inputs()

            _lowerCAmelCase     :				Union[str, Any]       =							image_processing(snake_case__   ,						snake_case__   ,						return_tensors='pt' )
            self.assertEqual(
                encoding['pixel_values'].shape   ,						(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(
                encoding['labels'].shape   ,						(
                    1,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(encoding['labels'].dtype   ,						torch.long )
            self.assertTrue(encoding['labels'].min().item() >= 0 )
            self.assertTrue(encoding['labels'].max().item() <= 255 )

            # Test batched input (PIL images)
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Dict       =							prepare_semantic_batch_inputs()

            _lowerCAmelCase     :				Union[str, Any]       =							image_processing(snake_case__   ,						snake_case__   ,						return_tensors='pt' )
            self.assertEqual(
                encoding['pixel_values'].shape   ,						(
                    2,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(
                encoding['labels'].shape   ,						(
                    2,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )   ,						)
            self.assertEqual(encoding['labels'].dtype   ,						torch.long )
            self.assertTrue(encoding['labels'].min().item() >= 0 )
            self.assertTrue(encoding['labels'].max().item() <= 255 )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.image_processing_class(**self.image_processor_dict )

            # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							prepare_semantic_single_inputs()
            _lowerCAmelCase     :				Dict       =							image_processing(snake_case__   ,						snake_case__   ,						return_tensors='pt' )
            self.assertTrue(encoding['labels'].min().item() >= 0 )
            self.assertTrue(encoding['labels'].max().item() <= 150 )

            _lowerCAmelCase     :				Optional[int]       =							True
            _lowerCAmelCase     :				int       =							image_processing(snake_case__   ,						snake_case__   ,						return_tensors='pt' )
            self.assertTrue(encoding['labels'].min().item() >= 0 )
            self.assertTrue(encoding['labels'].max().item() <= 255 )





'''simple docstring'''







from __future__ import annotations

from math import gcd






def       lowercase					(_A ,    _A = 2 ,    _A = 1 ,    _A = 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(_A ,    _A ,    _A       ) -> int:
              return (pow(_A ,    2       ) + step) % modulus

       for _ in range(_A       ):
              # These track the position within the cycle detection logic.
              _lowerCAmelCase     :				Dict       =							seed
              _lowerCAmelCase     :				int       =							seed

              while True:
                     # At each iteration, the tortoise moves one step and the hare moves two.
                     _lowerCAmelCase     :				str       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Optional[int]       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Union[str, Any]       =							rand_fn(_A ,    _A ,    _A       )

                     # 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     :				Optional[int]       =							gcd(hare - tortoise ,    _A       )

                     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     :				Tuple       =							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     :  Tuple											=    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     :  List[str]											=    parser.parse_args()

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

