



def       lowerCamelCase_  (    _a       :						Any   ,		_a       :						Optional[int]   ,		_a       :						Optional[int]   ,		_a       :						List[Any]    ):



    '''simple docstring'''




    if start == end:
        return start if lst[start] > item else start + 1
    if start > end:
        return start

    UpperCAmelCase_     :       int  		=					(start + end) // 2
    if lst[mid] < item:
        return binary_search(_a   ,		_a   ,		mid + 1   ,		_a    )
    elif lst[mid] > item:
        return binary_search(_a   ,		_a   ,		_a   ,		mid - 1    )
    else:
        return mid







def       lowerCamelCase_  (    _a       :						Any    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[int]  		=					len(_a    )

    for index in range(1   ,		_a    ):
        UpperCAmelCase_     :       List[Any]  		=					lst[index]
        UpperCAmelCase_     :       List[str]  		=					binary_search(_a   ,		_a   ,		0   ,		index - 1    )
        UpperCAmelCase_     :       Any  		=					lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

    return lst







def       lowerCamelCase_  (    _a       :						int   ,		_a       :						Optional[Any]    ):



    '''simple docstring'''




    if not left:
        return right

    if not right:
        return left

    if left[0] < right[0]:
        return [left[0], *merge(left[1:]   ,		_a    )]

    return [right[0], *merge(_a   ,		right[1:]    )]







def       lowerCamelCase_  (    _a       :						Union[str, Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Tuple  		=					len(_a    )
    UpperCAmelCase_		, UpperCAmelCase_     :       Union[str, Any]  		=					[], []
    UpperCAmelCase_     :       Any  		=					[lst[0]]
    UpperCAmelCase_     :       List[Any]  		=					[]
    UpperCAmelCase_     :       Tuple  		=					1
    while i < length:
        if lst[i] < lst[i - 1]:
            runs.append(_a    )
            UpperCAmelCase_     :       Any  		=					[lst[i]]
        else:
            new_run.append(lst[i]    )
        i += 1
    runs.append(_a    )

    for run in runs:
        sorted_runs.append(insertion_sort(_a    )    )
    for run in sorted_runs:
        UpperCAmelCase_     :       int  		=					merge(_a   ,		_a    )

    return sorted_array







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					[5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
    UpperCAmelCase_     :       Tuple  		=					tim_sort(_a    )
    print(_a    )


if __name__ == "__main__":
      main()





"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

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

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

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





