






from __future__ import annotations

from math import gcd



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase = 2  ,  __UpperCamelCase = 1  ,  __UpperCamelCase = 3  ,  ):
     # 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_    :     str										=    seed
          a_    :     List[Any]										=    seed

          while True:
               # At each iteration, the tortoise moves one step and the hare moves two.
               a_    :     Any										=    rand_fn(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
               a_    :     Optional[int]										=    rand_fn(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
               a_    :     Optional[int]										=    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_    :     int										=    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_    :     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

       __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}''')







from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowerCamelCase        =     logging.get_logger(__name__)  # pylint: disable=invalid-name


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     CLIPSegForImageSegmentation      ,							lowerCamelCase_      :     CLIPSegProcessor      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     CLIPTextModel      ,							lowerCamelCase_      :     CLIPTokenizer      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,							lowerCamelCase_      :     StableDiffusionSafetyChecker      ,							lowerCamelCase_      :     CLIPImageProcessor      ,							):
            super().__init__()

            if hasattr(scheduler.config      ,							"""steps_offset"""    ) and scheduler.config.steps_offset != 1:
                 a_    :     Dict										=    (
                     F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                     F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                     """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
                     """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
                     """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
                     """ file"""
                 )
                 deprecate("""steps_offset!=1"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    dict(scheduler.config    )
                 a_    :     Optional[int]										=    1
                 a_    :     int										=    FrozenDict(lowerCamelCase_    )

            if hasattr(scheduler.config      ,							"""skip_prk_steps"""    ) and scheduler.config.skip_prk_steps is False:
                 a_    :     List[str]										=    (
                     F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
                     """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
                     """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
                     """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
                     """ Hub, it would be very nice if you could open a Pull request for the"""
                     """ `scheduler/scheduler_config.json` file"""
                 )
                 deprecate("""skip_prk_steps not set"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     List[str]										=    dict(scheduler.config    )
                 a_    :     List[str]										=    True
                 a_    :     str										=    FrozenDict(lowerCamelCase_    )

            if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                     """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                     """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                     """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                     """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

            self.register_modules(
                segmentation_model=lowerCamelCase_      ,							segmentation_processor=lowerCamelCase_      ,							vae=lowerCamelCase_      ,							text_encoder=lowerCamelCase_      ,							tokenizer=lowerCamelCase_      ,							unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[Union[str, int]] = "auto"    ):
            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 a_    :     Any										=    self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.enable_attention_slicing(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

            a_    :     Any										=    torch.device("""cuda"""    )

            for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
                 if cpu_offloaded_model is not None:
                      cpu_offload(lowerCamelCase_      ,							lowerCamelCase_    )

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						UpperCAmelCase(   self      :     int    ):
            if self.device != torch.device("""meta"""    ) or not hasattr(self.unet      ,							"""_hf_hook"""    ):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(lowerCamelCase_      ,							"""_hf_hook"""    )
                     and hasattr(module._hf_hook      ,							"""execution_device"""    )
                     and module._hf_hook.execution_device is not None
                 ):
                      return torch.device(module._hf_hook.execution_device    )
            return self.device

       @torch.no_grad()
       def __call__(   self      :     Any      ,							lowerCamelCase_      :     Union[str, List[str]]      ,							lowerCamelCase_      :     Union[torch.FloatTensor, PIL.Image.Image]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_0      ,							lowerCamelCase_      :     float = 7.5      ,							lowerCamelCase_      :     Optional[Union[str, List[str]]] = None      ,							lowerCamelCase_      :     Optional[int] = 1      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     Optional[torch.Generator] = None      ,							lowerCamelCase_      :     Optional[torch.FloatTensor] = None      ,							lowerCamelCase_      :     Optional[str] = "pil"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,							lowerCamelCase_      :     int = 1      ,							**lowerCamelCase_      :     Any      ,							):
            a_    :     str										=    self.segmentation_processor(
                text=[text]      ,							images=[image]      ,							padding="""max_length"""      ,							return_tensors="""pt"""    ).to(self.device    )
            a_    :     List[str]										=    self.segmentation_model(**lowerCamelCase_    )
            a_    :     str										=    torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
            a_    :     Optional[int]										=    self.numpy_to_pil(lowerCamelCase_    )[0].resize(image.size    )

            # Run inpainting pipeline with the generated mask
            a_    :     Any										=    StableDiffusionInpaintPipeline(
                vae=self.vae      ,							text_encoder=self.text_encoder      ,							tokenizer=self.tokenizer      ,							unet=self.unet      ,							scheduler=self.scheduler      ,							safety_checker=self.safety_checker      ,							feature_extractor=self.feature_extractor      ,							)
            return inpainting_pipeline(
                prompt=lowerCamelCase_      ,							image=lowerCamelCase_      ,							mask_image=lowerCamelCase_      ,							height=lowerCamelCase_      ,							width=lowerCamelCase_      ,							num_inference_steps=lowerCamelCase_      ,							guidance_scale=lowerCamelCase_      ,							negative_prompt=lowerCamelCase_      ,							num_images_per_prompt=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							latents=lowerCamelCase_      ,							output_type=lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							callback=lowerCamelCase_      ,							callback_steps=lowerCamelCase_      ,							)



