





'''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,
)


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

_SCREAMING_SNAKE_CASE									=				"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"


def 						__a(SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       Tuple=8     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _lowerCAmelCase						      =   width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor





class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				Optional[int]:
     super().__init__()

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     if latents is None:
         _lowerCAmelCase						      =   randn_tensor(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  device=_lowerCAmelCase      ,  dtype=_lowerCAmelCase		)
     else:
         if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
         _lowerCAmelCase						      =   latents.to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   latents * scheduler.init_noise_sigma
     return latents




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				Optional[Any]:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError("Please install accelerate via `pip install accelerate`"		)

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				Tuple:
     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."		)

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

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

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

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




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 				_snake_case (						self		)     ->				int:
     if 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()
 @replace_example_docstring(_lowerCAmelCase		)
 def __call__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = 4.0      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = "pil"      ,  _lowerCAmelCase = True      ,  )     ->				List[str]:
     _lowerCAmelCase						      =   self._execution_device

     _lowerCAmelCase						      =   guidance_scale > 1.0

     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   torch.cat(_lowerCAmelCase      ,  dim=0		)
     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   torch.cat(_lowerCAmelCase      ,  dim=0		)
     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   torch.cat(_lowerCAmelCase      ,  dim=0		)

     _lowerCAmelCase						      =   image_embeds.shape[0] * num_images_per_prompt

     if do_classifier_free_guidance:
         _lowerCAmelCase						      =   image_embeds.repeat_interleave(_lowerCAmelCase      ,  dim=0		)
         _lowerCAmelCase						      =   negative_image_embeds.repeat_interleave(_lowerCAmelCase      ,  dim=0		)
         _lowerCAmelCase						      =   hint.repeat_interleave(_lowerCAmelCase      ,  dim=0		)

     _lowerCAmelCase						      =   torch.cat([negative_image_embeds, image_embeds]      ,  dim=0		).to(dtype=self.unet.dtype      ,  device=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.cat([hint, hint]      ,  dim=0		).to(dtype=self.unet.dtype      ,  device=_lowerCAmelCase		)
     self.scheduler.set_timesteps(_lowerCAmelCase      ,  device=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.scheduler.timesteps

     _lowerCAmelCase						      =   self.movq.config.latent_channels

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   downscale_height_and_width(_lowerCAmelCase      ,  _lowerCAmelCase      ,  self.movq_scale_factor		)

     # create initial latent
     _lowerCAmelCase						      =   self.prepare_latents(
         (batch_size, num_channels_latents, height, width)      ,  image_embeds.dtype      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  self.scheduler      ,  )

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

         _lowerCAmelCase						      =   {"image_embeds": image_embeds, "hint": hint}
         _lowerCAmelCase						      =   self.unet(
             sample=_lowerCAmelCase      ,  timestep=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  added_cond_kwargs=_lowerCAmelCase      ,  return_dict=_lowerCAmelCase      ,  )[0]

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

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

     if output_type == "pil":
         _lowerCAmelCase						      =   self.numpy_to_pil(_lowerCAmelCase		)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase		)




"""simple docstring"""




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  stooge(_UpperCamelCase			,					0			,					len(_UpperCamelCase    ) - 1    )
  return arr




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _lowercase			,					_lowercase:      Optional[Any]   			=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _lowercase:      Tuple   			=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )

    # Recursively sort last 2/3 elements
    stooge(_UpperCamelCase			,					i + t			,					(_UpperCamelCase)    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )


if __name__ == "__main__":
  A__ :       Dict				= input('Enter numbers separated by a comma:\n').strip()
  A__ :       Optional[Any]				= [int(item) for item in user_input.split(',')]
  print(stooge_sort(unsorted))
