





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

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

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										super().__init__()
										self.register_modules(vqvae=_lowerCAmelCase	,  unet=_lowerCAmelCase	,  scheduler=_lowerCAmelCase	)






					@torch.no_grad()
					def __call__( self	,  _lowerCAmelCase = 1	,  _lowerCAmelCase = None	,  _lowerCAmelCase = 0.0	,  _lowerCAmelCase = 50	,  _lowerCAmelCase = "pil"	,  _lowerCAmelCase = True	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__       =       randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,  generator=_lowerCAmelCase	,  )
										lowerCamelCase__       =       latents.to(self.device	)

										# scale the initial noise by the standard deviation required by the scheduler
										lowerCamelCase__       =       latents * self.scheduler.init_noise_sigma

										self.scheduler.set_timesteps(_lowerCAmelCase	)

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										lowerCamelCase__       =       "eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

										lowerCamelCase__       =       {}
										if accepts_eta:
															lowerCamelCase__       =       eta

										for t in self.progress_bar(self.scheduler.timesteps	):
															lowerCamelCase__       =       self.scheduler.scale_model_input(_lowerCAmelCase	,  _lowerCAmelCase	)
															# predict the noise residual
															lowerCamelCase__       =       self.unet(_lowerCAmelCase	,  _lowerCAmelCase	).sample
															# compute the previous noisy sample x_t -> x_t-1
															lowerCamelCase__       =       self.scheduler.step(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	).prev_sample

										# decode the image latents with the VAE
										lowerCamelCase__       =       self.vqvae.decode(_lowerCAmelCase	).sample

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

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=_lowerCAmelCase	)








def    __UpperCamelCase      (    a) ->float:
					return 10 - x * x





def    __UpperCamelCase      (    a,     a) ->float:
					# Bolzano theory in order to find if there is a root between a and b
					if equation(a) * equation(a) >= 0:
										raise ValueError("Wrong space!")

					lowerCamelCase__       =       a
					while (b - a) >= 0.0_1:
										# Find middle point
										lowerCamelCase__       =       (a + b) / 2
										# Check if middle point is root
										if equation(a) == 0.0:
															break
										# Decide the side to repeat the steps
										if equation(a) * equation(a) < 0:
															lowerCamelCase__       =       c
										else:
															lowerCamelCase__       =       c
					return c


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						print(bisection(-2, 5))
						print(bisection(0, 6))

