

'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					42
		SCREAMING_SNAKE_CASE_           =					42
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



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






		@torch.no_grad()
		def __call__(					self   ,			SCREAMING_SNAKE_CASE_ = 1   ,			SCREAMING_SNAKE_CASE_ = 2000   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = "pil"   ,			SCREAMING_SNAKE_CASE_ = True   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Union[ImagePipelineOutput, Tuple]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.unet.config.sample_size
							lowerCamelCase_  				=      (batch_size, 3, img_size, img_size)

							lowerCamelCase_  				=      self.unet

							lowerCamelCase_  				=      randn_tensor(SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_		) * self.scheduler.init_noise_sigma
							lowerCamelCase_  				=      sample.to(self.device		)

							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_		)
							self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE_		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
												lowerCamelCase_  				=      self.scheduler.sigmas[i] * torch.ones(shape[0]   ,			device=self.device		)

												# correction step
												for _ in range(self.scheduler.config.correct_steps		):
																	lowerCamelCase_  				=      self.unet(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		).sample
																	lowerCamelCase_  				=      self.scheduler.step_correct(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_		).prev_sample

												# prediction step
												lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		).sample
												lowerCamelCase_  				=      self.scheduler.step_pred(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_		)

												lowerCamelCase_				,lowerCamelCase_  				=      output.prev_sample, output.prev_sample_mean

							lowerCamelCase_  				=      sample_mean.clamp(0   ,			1		)
							lowerCamelCase_  				=      sample.cpu().permute(0   ,			2   ,			3   ,			1		).numpy()
							if output_type == "pil":
												lowerCamelCase_  				=      self.numpy_to_pil(SCREAMING_SNAKE_CASE_		)

							if not return_dict:
												return (sample,)

							return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_		)











from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


