


from typing import List, Optional, Tuple, Union

import torch

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

class 		__a				(    UpperCAmelCase      ):
		_a     :  UNetaDModel
		_a     :  KarrasVeScheduler
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""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 = 50      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = "pil"      ,   _SCREAMING_SNAKE_CASE = True      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[Tuple, ImagePipelineOutput]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      self.unet

									# sample x_0 ~ N(0, sigma_0^2 * I)
									_UpperCAmelCase					=      randn_tensor(_SCREAMING_SNAKE_CASE      ,   generator=_SCREAMING_SNAKE_CASE      ,   device=self.device						) * self.scheduler.init_noise_sigma

									self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						)

									for t in self.progress_bar(self.scheduler.timesteps						):
																# here sigma_t == t_i from the paper
																_UpperCAmelCase					=      self.scheduler.schedule[t]
																_UpperCAmelCase					=      self.scheduler.schedule[t - 1] if t > 0 else 0

																# 1. Select temporarily increased noise level sigma_hat
																# 2. Add new noise to move from sample_i to sample_hat
																_UpperCAmelCase		,	_UpperCAmelCase					=      self.scheduler.add_noise_to_input(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   generator=_SCREAMING_SNAKE_CASE						)

																# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																# The model inputs and output are adjusted by following eq. (213) in [1].
																_UpperCAmelCase					=      (sigma_hat / 2) * model((sample_hat + 1) / 2      ,   sigma_hat / 2						).sample

																# 4. Evaluate dx/dt at sigma_hat
																# 5. Take Euler step from sigma to sigma_prev
																_UpperCAmelCase					=      self.scheduler.step(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																if sigma_prev != 0:
																							# 6. Apply 2nd order correction
																							# The model inputs and output are adjusted by following eq. (213) in [1].
																							_UpperCAmelCase					=      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,   sigma_prev / 2						).sample
																							_UpperCAmelCase					=      self.scheduler.step_correct(
																							    _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   step_output.prev_sample      ,   step_output['derivative']      ,   )
																_UpperCAmelCase					=      step_output.prev_sample

									_UpperCAmelCase					=      (sample / 2 + 0.5).clamp(0      ,   1						)
									_UpperCAmelCase					=      sample.cpu().permute(0      ,   2      ,   3      ,   1						).numpy()
									if output_type == "pil":
																_UpperCAmelCase					=      self.numpy_to_pil(_SCREAMING_SNAKE_CASE						)

									if not return_dict:
																return (image,)

									return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE						)




import math
import random

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		bool = False	)  ->		float:


							'''simple docstring'''





							if deriv:
														return value * (1 - value)
							return 1 / (1 + math.exp(-value	))


# Initial Value
lowerCAmelCase__  :Optional[Any]             =							0.02

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      float(2 * (random.randint(1      ,  1_0_0	)) - 1	)

							for _ in range(a__	):
														# Forward propagation
														_UpperCAmelCase					=      sigmoid_function(INITIAL_VALUE * weight	)
														# How much did we miss?
														_UpperCAmelCase					=      (expected / 1_0_0) - layer_a
														# Error delta
														_UpperCAmelCase					=      layer_1_error * sigmoid_function(a__      ,  a__	)
														# Update weight
														weight += INITIAL_VALUE * layer_1_delta

							return layer_a * 1_0_0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase__  :List[Any]             =							int(input('''Expected value: '''))
		lowerCAmelCase__  :Any             =							int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
