






"""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					:				Union[str, Any]					    =						logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE					:				Any					    =						"""
    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      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		Any=8 )      ->				Dict:
  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      a						(					a_		):






    def __init__(       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						)     ->		List[Any]:
      super().__init__()

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






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      if latents is None:
        lowerCamelCase_         =						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}'''					)
        lowerCamelCase_         =						latents.to(_a					)

      lowerCamelCase_         =						latents * scheduler.init_noise_sigma
      return latents






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]=0					)     ->		List[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(_a       ,						_a					)






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]=0					)     ->		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.'					)

      lowerCamelCase_         =						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)

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

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






    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    UpperCamelCase       (       self     :							str					)     ->		Tuple:
      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       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = 512       ,						__SCREAMING_SNAKE_CASE     :							str = 512       ,						__SCREAMING_SNAKE_CASE     :							str = 100       ,						__SCREAMING_SNAKE_CASE     :							Dict = 4.0       ,						__SCREAMING_SNAKE_CASE     :							List[Any] = 1       ,						__SCREAMING_SNAKE_CASE     :							List[str] = None       ,						__SCREAMING_SNAKE_CASE     :							List[str] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							List[Any] = True       ,						)     ->		int:
      lowerCamelCase_         =						self._execution_device

      lowerCamelCase_         =						guidance_scale > 1.0

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

      if do_classifier_free_guidance:
        lowerCamelCase_         =						image_embeds.repeat_interleave(_a       ,						dim=0					)
        lowerCamelCase_         =						negative_image_embeds.repeat_interleave(_a       ,						dim=0					)

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

      self.scheduler.set_timesteps(_a       ,						device=_a					)
      lowerCamelCase_         =						self.scheduler.timesteps

      lowerCamelCase_         =						self.unet.config.in_channels

      lowerCamelCase_         =						downscale_height_and_width(_a       ,						_a       ,						self.movq_scale_factor					)

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

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

        if do_classifier_free_guidance:
          lowerCamelCase_         =						noise_pred.split(latents.shape[1]       ,						dim=1					)
          lowerCamelCase_         =						noise_pred.chunk(2					)
          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_         =						noise_pred.split(latents.shape[1]       ,						dim=1					)

        # compute the previous noisy sample x_t -> x_t-1
        lowerCamelCase_         =						self.scheduler.step(
            _a       ,						_a       ,						_a       ,						generator=_a       ,						)[0]
      # post-processing
      lowerCamelCase_         =						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"]:
        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(_a					)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=_a					)






"""simple docstring"""




from __future__ import annotations


def   __magic_name__	(      __snake_case				:       list[int]  ) ->				list[int]:
       if len(__snake_case  ) == 0:
              return array

       lowercase		,    lowercase    :				Tuple       		=							min(__snake_case  ), max(__snake_case  )

       # Compute the variables
       lowercase    :				Optional[Any]       		=							_max - _min + 1
       lowercase		,    lowercase    :				List[str]       		=							[0] * holes_range, [0] * holes_range

       # Make the sorting.
       for i in array:
              lowercase    :				Tuple       		=							i - _min
              lowercase    :				str       		=							i
              holes_repeat[index] += 1

       # Makes the array back by replacing the numbers.
       lowercase    :				Union[str, Any]       		=							0
       for i in range(__snake_case  ):
              while holes_repeat[i] > 0:
                     lowercase    :				Tuple       		=							holes[i]
                     index += 1
                     holes_repeat[i] -= 1

    # Returns the sorted array.
       return array


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _A			:       str   		=       input("""Enter numbers separated by comma:\n""")
   _A			:       Optional[Any]   		=       [int(x) for x in user_input.split(""",""")]
   print(pigeon_sort(unsorted))


