




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			if not inplace:
						UpperCamelCase_     				= ma + ma
			else:
						ma += ma

			return ma




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= -1 * len(SCREAMING_SNAKE_CASE_					)
			UpperCamelCase_     				= list(range(len(tensor.shape[:zero_index]					)					)					)
			return tensor.permute(first_inds + [zero_index + i for i in inds]					)




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			return t.reshape(t.shape[:-no_dims] + (-1,)					)




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case = 1E-4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= mask.expand(*value.shape					)
			return torch.sum(mask * value     ,		dim=SCREAMING_SNAKE_CASE_					) / (eps + torch.sum(SCREAMING_SNAKE_CASE_     ,		dim=SCREAMING_SNAKE_CASE_					))




def  a_		(							__snake_case     ,		__snake_case = 2.31_25     ,		__snake_case = 21.68_75     ,		__snake_case = 6_4					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= torch.linspace(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		no_bins - 1     ,		device=pts.device					)
			UpperCamelCase_     				= torch.sqrt(torch.sum((pts.unsqueeze(-2					) - pts.unsqueeze(-3					)) ** 2     ,		dim=-1					)					)
			return torch.bucketize(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)




def  a_		(							__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			UpperCamelCase_     				= dicts[0]
			UpperCamelCase_     				= {}
			for k, v in first.items():
						UpperCamelCase_     				= [d[k] for d in dicts]
						if isinstance(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					):
									UpperCamelCase_     				= dict_multimap(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)
						else:
									UpperCamelCase_     				= fn(SCREAMING_SNAKE_CASE_					)

			return new_dict




def  a_		(							__snake_case     ,		__snake_case					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= v_bins.view(((1,) * len(x.shape					)) + (len(SCREAMING_SNAKE_CASE_					),)					)
			UpperCamelCase_     				= x[..., None] - reshaped_bins
			UpperCamelCase_     				= torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_					)     ,		dim=-1					)
			return nn.functional.one_hot(SCREAMING_SNAKE_CASE_     ,		num_classes=len(SCREAMING_SNAKE_CASE_					)					).float()




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case = 0     ,		__snake_case = 0					)	->			torch.Tensor:

			'''simple docstring'''


			UpperCamelCase_     				= []
			for i, s in enumerate(data.shape[:no_batch_dims]					):
						UpperCamelCase_     				= torch.arange(SCREAMING_SNAKE_CASE_					)
						UpperCamelCase_     				= r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape					) - i - 1)))					)
						ranges.append(SCREAMING_SNAKE_CASE_					)

			UpperCamelCase_     				= [slice(SCREAMING_SNAKE_CASE_					) for _ in range(len(data.shape					) - no_batch_dims					)]
			UpperCamelCase_     				= inds
			ranges.extend(SCREAMING_SNAKE_CASE_					)
			# Matt note: Editing this to get around the behaviour of using a list as an array index changing
			# in recent Numpy versions
			return data[tuple(SCREAMING_SNAKE_CASE_					)]


__a			:	List[str]							     =		TypeVar("""T""")




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Dict[Any, Union[dict, list, tuple, Any]]:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			for k, v in dic.items():
						if isinstance(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					):
									UpperCamelCase_     				= dict_map(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)
						else:
									UpperCamelCase_     				= tree_map(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)

			return new_dict




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			dict:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			list:

			'''simple docstring'''


			...




@overload
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			tuple:

			'''simple docstring'''


			...




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			List[Any]:

			'''simple docstring'''


			if isinstance(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					):
						return dict_map(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					)
			elif isinstance(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					):
						return [tree_map(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					) for x in tree]
			elif isinstance(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					):
						return tuple(tree_map(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					) for x in tree					)
			elif isinstance(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_					):
						return fn(SCREAMING_SNAKE_CASE_					)
			else:
						print(type(SCREAMING_SNAKE_CASE_					)					)
						raise ValueError('Not supported'					)





__a			:	List[Any]							     =		partial(tree_map, leaf_type=torch.Tensor)


from typing import List, Optional, Tuple, Union

import torch

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







class 	A__				(	__magic_name__      ):






				def __init__(		self   : Any       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a       ,	scheduler=a  )






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

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

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

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

							lowerCAmelCase__ : str   =						(image / 2 + 0.5).clamp(0       ,	1  )
							lowerCAmelCase__ : Optional[int]   =						image.cpu().permute(0       ,	2       ,	3       ,	1  ).numpy()
							if output_type == "pil":
										lowerCAmelCase__ : int   =						self.numpy_to_pil(a  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )