
from __future__ import annotations

import math

SCREAMING_SNAKE_CASE							:List[Any]										=    '2020.9.26'
SCREAMING_SNAKE_CASE							:List[Any]										=    'xcodz-dot, cclaus, dhruvmanila'


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     tuple[float, float]:







				"""simple docstring"""



				if not all(isinstance(a_		,       (float, int)						) for val in locals().values()						):
								__A             =			F'''Input values must either be float or int: {list(locals().values()						)}'''
								raise TypeError(a_						)
				__A             =			((x * distance) / (z + distance)) * scale
				__A             =			((y * distance) / (z + distance)) * scale
				return projected_x, projected_y


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     tuple[float, float, float]:







				"""simple docstring"""



				if not isinstance(a_		,       a_						):
								raise TypeError("Axis must be a str"						)
				__A             =			locals()
				del input_variables["axis"]
				if not all(isinstance(a_		,       (float, int)						) for val in input_variables.values()						):
								__A             =			(
								    "Input values except axis must either be float or int: "
								    F'''{list(input_variables.values()						)}'''
								)
								raise TypeError(a_						)
				__A             =			(angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
				if axis == "z":
								__A             =			x * math.cos(a_						) - y * math.sin(a_						)
								__A             =			y * math.cos(a_						) + x * math.sin(a_						)
								__A             =			z
				elif axis == "x":
								__A             =			y * math.cos(a_						) - z * math.sin(a_						)
								__A             =			z * math.cos(a_						) + y * math.sin(a_						)
								__A             =			x
				elif axis == "y":
								__A             =			x * math.cos(a_						) - z * math.sin(a_						)
								__A             =			z * math.cos(a_						) + x * math.sin(a_						)
								__A             =			y
				else:
								raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"						)

				return new_x, new_y, new_z


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
					print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')


from typing import List, Optional, Tuple, Union

import torch

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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

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

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).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
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

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

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
