




from __future__ import annotations


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: Dict						     =    0
  _lowercase: str						     =    len(UpperCamelCase							) - 1
  while left <= right:
    _lowercase: List[str]						     =    (left + right) // 2
    if left == right:
      if sorted_list[middle] < item:
        _lowercase: List[str]						     =    middle + 1
      break
    elif sorted_list[middle] < item:
      _lowercase: Tuple						     =    middle + 1
    else:
      _lowercase: List[str]						     =    middle - 1
  sorted_list.insert(UpperCamelCase					,     UpperCamelCase							)
  return sorted_list


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: str						     =    []
  while left and right:
    if left[0][0] < right[0][0]:
      result.append(left.pop(0							)							)
    else:
      result.append(right.pop(0							)							)
  return result + left + right


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: List[str]						     =    len(UpperCamelCase							)
  if length <= 1:
    return list_ad
  _lowercase: Tuple						     =    length // 2
  return merge(sortlist_ad(list_ad[:middle]							)					,     sortlist_ad(list_ad[middle:]							)							)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		list[int]:





  """simple docstring"""





  if len(UpperCamelCase							) <= 1:
    return collection
  _lowercase: Union[str, Any]						     =    []
  _lowercase: int						     =    False
  for i in range(0					,     len(UpperCamelCase							)					,     2							):
    if i == len(UpperCamelCase							) - 1:
      _lowercase: List[str]						     =    True
    else:
      if collection[i] < collection[i + 1]:
        two_paired_list.append([collection[i], collection[i + 1]]							)
      else:
        two_paired_list.append([collection[i + 1], collection[i]]							)
  _lowercase: str						     =    sortlist_ad(UpperCamelCase							)
  _lowercase: Dict						     =    [i[0] for i in sorted_list_ad]
  result.append(sorted_list_ad[-1][1]							)
  if has_last_odd_item:
    _lowercase: Optional[Any]						     =    collection[-1]
    _lowercase: Optional[int]						     =    binary_search_insertion(UpperCamelCase					,     UpperCamelCase							)
  _lowercase: str						     =    False
  for i in range(len(UpperCamelCase							) - 1							):
    if result[i] == collection[-1] and has_last_odd_item:
      _lowercase: Optional[Any]						     =    True
    _lowercase: Any						     =    sorted_list_ad[i][1]
    # If last_odd_item is inserted before the item's index,
    # you should forward index one more.
    if is_last_odd_item_inserted_before_this_index:
      _lowercase: int						     =    result[: i + 2] + binary_search_insertion(result[i + 2 :]					,     UpperCamelCase							)
    else:
      _lowercase: str						     =    result[: i + 1] + binary_search_insertion(result[i + 1 :]					,     UpperCamelCase							)

  return result


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






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
