

from __future__ import annotations

from math import gcd
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__ = 2,    UpperCAmelCase__ = 1,    UpperCAmelCase__ = 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(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			) -> int:
              return (pow(UpperCAmelCase__,    2			) + step) % modulus

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

              while True:
                     # At each iteration, the tortoise moves one step and the hare moves two.
                     A_		   = rand_fn(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)
                     A_		   = rand_fn(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)
                     A_		   = 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``.
                     A_		   = 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.
              A_		   = 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}""")



'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class    A__ ( unittest.TestCase     ):



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





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = 1
           A_		   = 3
           A_		   = (32, 32)

           A_		   = floats_tensor((batch_size, num_channels) + sizes						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           return image



    @property
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = UNetaDConditionModel(
               block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=4						,			out_channels=4						,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,			cross_attention_dim=32						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = AutoencoderKL(
               block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,			latent_channels=4						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = RobertaSeriesConfig(
               hidden_size=32						,			project_dim=32						,			intermediate_size=37						,			layer_norm_eps=1e-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=5006						,			)
           return RobertaSeriesModelWithTransformation(UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           def extract(*UpperCamelCase__						,			**UpperCamelCase__    ):


                  class    A__ :



                      def __init__(   self    )  -> Dict:





                             '''simple docstring'''
                             A_		   = torch.ones([0]    )







                      def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





                             '''simple docstring'''
                             self.pixel_values.to(UpperCamelCase__    )
                             return self

                  return Out()

           return extract



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





           '''simple docstring'''
           A_		   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )
           A_		   = init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			)

           A_		   = output.images

           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[0]

           A_		   = image[0, -3:, -3:, -1]
           A_		   = image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_		   = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3



    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )

           # put models in fp16
           A_		   = unet.half()
           A_		   = vae.half()
           A_		   = bert.half()

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			).images

           assert image.shape == (1, 32, 32, 3)







    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           # resize to resolution that is divisible by 8 but not 16 or 32
           A_		   = init_image.resize((760, 504)    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           A_		   = image[255:258, 383:386, -1]

           assert image.shape == (504, 760, 3)
           A_		   = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2


@slow
@require_torch_gpu
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







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





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           A_		   = init_image.resize((768, 512)    )
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           assert image.shape == (512, 768, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image    ).max() < 1e-2
