
"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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




class 			A__	(     __lowerCAmelCase):

  def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
   super().__init__()
   self.register_modules(unet=lowerCAmelCase_	,		scheduler=lowerCAmelCase_			)







  @torch.no_grad()
  def __call__(				self	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = 1_00	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = True	,		):

   if audio_length_in_s is None:
    __lowerCAmelCase      :						Union[str, Any]						      =				self.unet.config.sample_size / self.unet.config.sample_rate

   __lowerCAmelCase      :						int						      =				audio_length_in_s * self.unet.config.sample_rate

   __lowerCAmelCase      :						Any						      =				2 ** len(self.unet.up_blocks			)
   if sample_size < 3 * down_scale_factor:
    raise ValueError(
        f"{audio_length_in_s} is too small. Make sure it\'s bigger or equal to"
        f" {3 * down_scale_factor / self.unet.config.sample_rate}."			)

   __lowerCAmelCase      :						List[str]						      =				int(lowerCAmelCase_			)
   if sample_size % down_scale_factor != 0:
    __lowerCAmelCase      :						Tuple						      =				(
        (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
    ) * down_scale_factor
    logger.info(
        f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
        f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
        ' process.'			)
   __lowerCAmelCase      :						Optional[Any]						      =				int(lowerCAmelCase_			)

   __lowerCAmelCase      :						Union[str, Any]						      =				next(iter(self.unet.parameters()			)			).dtype
   __lowerCAmelCase      :						List[str]						      =				(batch_size, self.unet.config.in_channels, sample_size)
   if isinstance(lowerCAmelCase_	,		lowerCAmelCase_			) and len(lowerCAmelCase_			) != batch_size:
    raise ValueError(
        f"You have passed a list of generators of length {len(lowerCAmelCase_			)}, but requested an effective batch"
        f" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

   __lowerCAmelCase      :						List[str]						      =				randn_tensor(lowerCAmelCase_	,		generator=lowerCAmelCase_	,		device=self.device	,		dtype=lowerCAmelCase_			)

   # set step values
   self.scheduler.set_timesteps(lowerCAmelCase_	,		device=audio.device			)
   __lowerCAmelCase      :						List[Any]						      =				self.scheduler.timesteps.to(lowerCAmelCase_			)

   for t in self.progress_bar(self.scheduler.timesteps			):
    # 1. predict noise model_output
    __lowerCAmelCase      :						str						      =				self.unet(lowerCAmelCase_	,		lowerCAmelCase_			).sample

    # 2. compute previous image: x_t -> t_t-1
    __lowerCAmelCase      :						str						      =				self.scheduler.step(lowerCAmelCase_	,		lowerCAmelCase_	,		lowerCAmelCase_			).prev_sample

   __lowerCAmelCase      :						List[str]						      =				audio.clamp(-1	,		1			).float().cpu().numpy()

   __lowerCAmelCase      :						Dict						      =				audio[:, :, :original_sample_size]

   if not return_dict:
    return (audio,)

   return AudioPipelineOutput(audios=lowerCAmelCase_			)

"""simple docstring"""







def    __lowerCAmelCase    (_UpperCamelCase		):
	if len(_UpperCamelCase		) <= 1:
		return lst

	__lowerCAmelCase      :						str						      =				1

	while i < len(_UpperCamelCase		):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				__lowerCAmelCase      :						int						      =				1

	return lst


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