


'''simple docstring'''


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__magic_name__			=      '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      int=8):
      A_						:  Tuple          =				height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      A_						:  List[Any]          =				width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor






class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       str	,_a :       UNetaDConditionModel	,_a :       DDPMScheduler	,_a :       VQModel	,):

        '''simple docstring'''




        super().__init__()

        self.register_modules(
            unet=_a	,scheduler=_a	,movq=_a	,)
        A_						:  Optional[int]          =				2 ** (len(self.movq.config.block_out_channels ) - 1)






  def        _a							(							self :       Dict	,_a :       Any	,_a :       List[Any]	,_a :       List[Any]	,_a :       Union[str, Any]	,_a :       List[str]	,_a :       Optional[int] ):

        '''simple docstring'''




        if latents is None:
              A_						:  Dict          =				randn_tensor(_a	,generator=_a	,device=_a	,dtype=_a )
        else:
              if latents.shape != shape:
                    raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
              A_						:  Dict          =				latents.to(_a )

        A_						:  int          =				latents * scheduler.init_noise_sigma
        return latents






  def        _a							(							self :       str	,_a :       Dict=0 ):

        '''simple docstring'''




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

        A_						:  Tuple          =				torch.device(f'cuda:{gpu_id}' )

        A_						:  List[Any]          =				[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                    cpu_offload(_a	,_a )






  def        _a							(							self :       int	,_a :       List[Any]=0 ):

        '''simple docstring'''




        if is_accelerate_available() and is_accelerate_version(""">="""	,"""0.17.0.dev0""" ):
              from accelerate import cpu_offload_with_hook
        else:
              raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )

        A_						:  List[Any]          =				torch.device(f'cuda:{gpu_id}' )

        if self.device.type != "cpu":
              self.to("""cpu"""	,silence_dtype_warnings=_a )
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        A_						:  Dict          =				None
        for cpu_offloaded_model in [self.unet, self.movq]:
              A_   ,					A_						:  Union[str, Any]          =				cpu_offload_with_hook(_a	,_a	,prev_module_hook=_a )

        # We'll offload the last model manually.
        A_						:  List[str]          =				hook






  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        if not hasattr(self.unet	,"""_hf_hook""" ):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(_a	,"""_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()
  @replace_example_docstring(_a )
  def __call__(							self :       str	,_a :       Union[torch.FloatTensor, List[torch.FloatTensor]]	,_a :       Union[torch.FloatTensor, List[torch.FloatTensor]]	,_a :       int = 512	,_a :       int = 512	,_a :       int = 100	,_a :       float = 4.0	,_a :       int = 1	,_a :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,_a :       Optional[torch.FloatTensor] = None	,_a :       Optional[str] = "pil"	,_a :       bool = True	,):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self._execution_device

        A_						:  str          =				guidance_scale > 1.0

        if isinstance(_a	,_a ):
              A_						:  str          =				torch.cat(_a	,dim=0 )
        A_						:  Optional[Any]          =				image_embeds.shape[0] * num_images_per_prompt
        if isinstance(_a	,_a ):
              A_						:  Dict          =				torch.cat(_a	,dim=0 )

        if do_classifier_free_guidance:
              A_						:  str          =				image_embeds.repeat_interleave(_a	,dim=0 )
              A_						:  Union[str, Any]          =				negative_image_embeds.repeat_interleave(_a	,dim=0 )

        A_						:  Tuple          =				torch.cat([negative_image_embeds, image_embeds]	,dim=0 ).to(dtype=self.unet.dtype	,device=_a )

        self.scheduler.set_timesteps(_a	,device=_a )
        A_						:  str          =				self.scheduler.timesteps

        A_						:  Optional[Any]          =				self.unet.config.in_channels

        A_   ,					A_						:  Any          =				downscale_height_and_width(_a	,_a	,self.movq_scale_factor )

        # create initial latent
        A_						:  int          =				self.prepare_latents(
            (batch_size, num_channels_latents, height, width)	,image_embeds.dtype	,_a	,_a	,_a	,self.scheduler	,)

        for i, t in enumerate(self.progress_bar(_a ) ):
              # expand the latents if we are doing classifier free guidance
              A_						:  List[Any]          =				torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

              A_						:  List[Any]          =				{"""image_embeds""": image_embeds}
              A_						:  int          =				self.unet(
                  sample=_a	,timestep=_a	,encoder_hidden_states=_a	,added_cond_kwargs=_a	,return_dict=_a	,)[0]

              if do_classifier_free_guidance:
                    A_   ,					A_						:  Any          =				noise_pred.split(latents.shape[1]	,dim=1 )
                    A_   ,					A_						:  List[Any]          =				noise_pred.chunk(2 )
                    A_   ,					A_						:  int          =				variance_pred.chunk(2 )
                    A_						:  List[str]          =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    A_						:  Any          =				torch.cat([noise_pred, variance_pred_text]	,dim=1 )

              if not (
                  hasattr(self.scheduler.config	,"""variance_type""" )
                  and self.scheduler.config.variance_type in ["learned", "learned_range"]
              ):
                    A_   ,					A_						:  Tuple          =				noise_pred.split(latents.shape[1]	,dim=1 )

              # compute the previous noisy sample x_t -> x_t-1
              A_						:  Any          =				self.scheduler.step(
                  _a	,_a	,_a	,generator=_a	,)[0]
        # post-processing
        A_						:  Tuple          =				self.movq.decode(_a	,force_not_quantize=_a )["""sample"""]

        if output_type not in ["pt", "np", "pil"]:
              raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )

        if output_type in ["np", "pil"]:
              A_						:  int          =				image * 0.5 + 0.5
              A_						:  int          =				image.clamp(0	,1 )
              A_						:  Any          =				image.cpu().permute(0	,2	,3	,1 ).float().numpy()

        if output_type == "pil":
              A_						:  List[str]          =				self.numpy_to_pil(_a )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=_a )




'''simple docstring'''


from __future__ import annotations

import math




def      lowerCamelCase					(      lowerCamelCase  :      int):

      if num <= 0:
            A_						:  List[Any]          =				F'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCamelCase)

      A_						:  str          =				[True] * (num + 1)
      A_						:  Tuple          =				[]
      A_						:  str          =				2
      A_						:  Any          =				int(math.sqrt(lowerCamelCase))

      while start <= end:
            # If start is a prime
            if sieve[start] is True:
                  prime.append(lowerCamelCase)

                  # Set multiples of start be False
                  for i in range(start * start							,						num + 1							,						lowerCamelCase):
                        if sieve[i] is True:
                              A_						:  Union[str, Any]          =				False

            start += 1

      for j in range(end + 1							,						num + 1):
            if sieve[j] is True:
                  prime.append(lowerCamelCase)

      return prime


if __name__ == "__main__":
     print(prime_sieve(int(input('Enter a positive integer: ').strip())))
