





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,
)


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

snake_case 				=       """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :List[str]			,							snake_case__  :Any=8     )      ->						Any:
  _lowercase							= height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowercase							= width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	List[str]						,__A	:	UNetaDConditionModel						,__A	:	DDPMScheduler						,__A	:	VQModel						,)						->					Tuple:
         super().__init__()

         self.register_modules(
             unet=__A						,scheduler=__A						,movq=__A						,)
         _lowercase							= 2 ** (len(self.movq.config.block_out_channels  ) - 1)







       def    __UpperCAmelCase (    self	:	str						,__A	:	List[str]						,__A	:	Dict						,__A	:	List[str]						,__A	:	List[Any]						,__A	:	Optional[int]						,__A	:	str  )						->					Tuple:
         if latents is None:
           _lowercase							= 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}"""  )
           _lowercase							= latents.to(__A  )

         _lowercase							= latents * scheduler.init_noise_sigma
         return latents







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]=0  )						->					List[str]:
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError('Please install accelerate via `pip install accelerate`'  )

         _lowercase							= torch.device(F"""cuda:{gpu_id}"""  )

         _lowercase							= [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(__A						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	List[str]=0  )						->					Optional[int]:
         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.'  )

         _lowercase							= 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)

         _lowercase							= None
         for cpu_offloaded_model in [self.unet, self.movq]:
           _lowercase						,		_lowercase							= cpu_offload_with_hook(__A						,__A						,prev_module_hook=__A  )

         # We'll offload the last model manually.
         _lowercase							= hook







       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def    __UpperCAmelCase (    self	:	Any  )						->					List[Any]:
         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	:	Any						,__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						,)						->					Optional[int]:
         _lowercase							= self._execution_device

         _lowercase							= guidance_scale > 1.0

         if isinstance(__A						,__A  ):
           _lowercase							= torch.cat(__A						,dim=0  )
         _lowercase							= image_embeds.shape[0] * num_images_per_prompt
         if isinstance(__A						,__A  ):
           _lowercase							= torch.cat(__A						,dim=0  )

         if do_classifier_free_guidance:
           _lowercase							= image_embeds.repeat_interleave(__A						,dim=0  )
           _lowercase							= negative_image_embeds.repeat_interleave(__A						,dim=0  )

         _lowercase							= torch.cat([negative_image_embeds, image_embeds]						,dim=0  ).to(dtype=self.unet.dtype						,device=__A  )

         self.scheduler.set_timesteps(__A						,device=__A  )
         _lowercase							= self.scheduler.timesteps

         _lowercase							= self.unet.config.in_channels

         _lowercase						,		_lowercase							= downscale_height_and_width(__A						,__A						,self.movq_scale_factor  )

         # create initial latent
         _lowercase							= 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
           _lowercase							= torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

           _lowercase							= {'image_embeds': image_embeds}
           _lowercase							= self.unet(
               sample=__A						,timestep=__A						,encoder_hidden_states=__A						,added_cond_kwargs=__A						,return_dict=__A						,)[0]

           if do_classifier_free_guidance:
             _lowercase						,		_lowercase							= noise_pred.split(latents.shape[1]						,dim=1  )
             _lowercase						,		_lowercase							= noise_pred.chunk(2  )
             _lowercase						,		_lowercase							= variance_pred.chunk(2  )
             _lowercase							= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             _lowercase							= 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"]
           ):
             _lowercase						,		_lowercase							= noise_pred.split(latents.shape[1]						,dim=1  )

           # compute the previous noisy sample x_t -> x_t-1
           _lowercase							= self.scheduler.step(
               __A						,__A						,__A						,generator=__A						,)[0]
         # post-processing
         _lowercase							= 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"]:
           _lowercase							= image * 0.5 + 0.5
           _lowercase							= image.clamp(0						,1  )
           _lowercase							= image.cpu().permute(0						,2						,3						,1  ).float().numpy()

         if output_type == "pil":
           _lowercase							= self.numpy_to_pil(__A  )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=__A  )






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Dict:
  _lowercase							= len(snake_case__     )
  while cur > 1:
    # Find the maximum number in arr
    _lowercase							= arr.index(max(arr[0:cur]     )     )
    # Reverse from 0 to mi
    _lowercase							= arr[mi::-1] + arr[mi + 1 : len(snake_case__     )]
    # Reverse whole list
    _lowercase							= arr[cur - 1 :: -1] + arr[cur : len(snake_case__     )]
    cur -= 1
  return arr


if __name__ == "__main__":
      snake_case 				=       input("""Enter numbers separated by a comma:\n""").strip()
      snake_case 				=       [int(item) for item in user_input.split(""",""")]
      print(pancake_sort(unsorted))