


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin

@flax.struct.dataclass
class 		__a				:
		# setable values
		_a     :  Optional[int]           =     None
		_a     :  Optional[jnp.ndarray]           =     None
		_a     :  Optional[jnp.ndarray]           =     None  # sigma(t_i)
		@classmethod
		def      UpperCAmelCase__    (      cls						)			->	Union[str, Any]:




									"""simple docstring"""





									return cls()



@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  jnp.ndarray
		_a     :  jnp.ndarray
		_a     :  KarrasVeSchedulerState



class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase      ):
		@property
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									return True
		@register_to_config
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = 0.02      ,   _SCREAMING_SNAKE_CASE = 100      ,   _SCREAMING_SNAKE_CASE = 1.007      ,   _SCREAMING_SNAKE_CASE = 80      ,   _SCREAMING_SNAKE_CASE = 0.05      ,   _SCREAMING_SNAKE_CASE = 50      ,   )			->	Dict:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									return KarrasVeSchedulerState.create()
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = ()						)			->	KarrasVeSchedulerState:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.arange(0      ,   _SCREAMING_SNAKE_CASE						)[::-1].copy()
									_UpperCAmelCase					=      [
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=_SCREAMING_SNAKE_CASE      ,   schedule=jnp.array(_SCREAMING_SNAKE_CASE      ,   dtype=jnp.floataa						)      ,   timesteps=_SCREAMING_SNAKE_CASE      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )			->	Tuple[jnp.ndarray, float]:




									"""simple docstring"""





									if self.config.s_min <= sigma <= self.config.s_max:
																_UpperCAmelCase					=      min(self.config.s_churn / state.num_inference_steps      ,   2**0.5 - 1						)
									else:
																_UpperCAmelCase					=      0

									# sample eps ~ N(0, S_noise^2 * I)
									_UpperCAmelCase					=      random.split(_SCREAMING_SNAKE_CASE      ,   num=1						)
									_UpperCAmelCase					=      self.config.s_noise * random.normal(key=_SCREAMING_SNAKE_CASE      ,   shape=sample.shape						)
									_UpperCAmelCase					=      sigma + gamma * sigma
									_UpperCAmelCase					=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = True      ,   )			->	Union[FlaxKarrasVeOutput, Tuple]:




									"""simple docstring"""





									_UpperCAmelCase					=      sample_hat + sigma_hat * model_output
									_UpperCAmelCase					=      (sample_hat - pred_original_sample) / sigma_hat
									_UpperCAmelCase					=      sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
																return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=_SCREAMING_SNAKE_CASE      ,   derivative=_SCREAMING_SNAKE_CASE      ,   state=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = True      ,   )			->	Union[FlaxKarrasVeOutput, Tuple]:




									"""simple docstring"""





									_UpperCAmelCase					=      sample_prev + sigma_prev * model_output
									_UpperCAmelCase					=      (sample_prev - pred_original_sample) / sigma_prev
									_UpperCAmelCase					=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
																return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=_SCREAMING_SNAKE_CASE      ,   derivative=_SCREAMING_SNAKE_CASE      ,   state=_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									raise NotImplementedError()




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))
