





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_A :   List[Any]								=  'scheduler_config.json'


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  str			   =   1
       _UpperCAmelCase				:  Dict			   =   2
       _UpperCAmelCase				:  Optional[int]			   =   3
       _UpperCAmelCase				:  Dict			   =   4
       _UpperCAmelCase				:  List[str]			   =   5


@dataclass
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  jnp.ndarray


class        __SCREAMING_SNAKE_CASE      :
       _UpperCAmelCase				:  Optional[Any]			   =   SCHEDULER_CONFIG_NAME
       _UpperCAmelCase				:  Union[str, Any]			   =   ["dtype"]
       _UpperCAmelCase				:  List[Any]			   =   []
       _UpperCAmelCase				:  Optional[Any]			   =   True







       @classmethod
       def 						__lowerCamelCase   (				cls					:     List[Any]		, A					:     Dict[str, Any] = None		, A					:     Optional[str] = None		, A					:     Dict=False		, **A					:     Any		, )       ->Union[str, Any]:
          lowerCamelCase__       :						List[Any]        =							cls.load_config(
              pretrained_model_name_or_path=A		, subfolder=A		, return_unused_kwargs=A		, **A		, )
          lowerCamelCase__       :						int        =							cls.from_config(A		, return_unused_kwargs=A		, **A      )

          if hasattr(A		, '''create_state'''      ) and getattr(A		, '''has_state'''		, A      ):
             lowerCamelCase__       :						Union[str, Any]        =							scheduler.create_state()

          if return_unused_kwargs:
             return scheduler, state, unused_kwargs

          return scheduler, state







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Union[str, os.PathLike]		, A					:     bool = False		, **A					:     Dict      )       ->Optional[int]:
          self.save_config(save_directory=A		, push_to_hub=A		, **A      )







       @property
       def 						__lowerCamelCase   (				self					:     Any      )       ->Union[str, Any]:
          return self._get_compatibles()







       @classmethod
       def 						__lowerCamelCase   (				cls					:     List[str]      )       ->List[str]:
          lowerCamelCase__       :						Any        =							list(set([cls.__name__] + cls._compatibles      )      )
          lowerCamelCase__       :						Optional[Any]        =							importlib.import_module(__name__.split('''.'''      )[0]      )
          lowerCamelCase__       :						Optional[int]        =							[
              getattr(A		, A      ) for c in compatible_classes_str if hasattr(A		, A      )
          ]
          return compatible_classes



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    jnp.ndarray:







   """simple docstring"""
   assert len(UpperCAmelCase					) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCAmelCase					) - x.ndim)					)      ,  UpperCAmelCase					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=0.9_99      ,  UpperCAmelCase=jnp.floataa					)    ->    jnp.ndarray:







   """simple docstring"""

   def alpha_bar(UpperCAmelCase					):
      return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

   lowerCamelCase__       :						Optional[Any]        =							[]
   for i in range(UpperCAmelCase					):
      lowerCamelCase__       :						Dict        =							i / num_diffusion_timesteps
      lowerCamelCase__       :						Optional[Any]        =							(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(UpperCAmelCase					) / alpha_bar(UpperCAmelCase					)      ,  UpperCAmelCase					)					)
   return jnp.array(UpperCAmelCase      ,  dtype=UpperCAmelCase					)


@flax.struct.dataclass
class        __SCREAMING_SNAKE_CASE      :
       _UpperCAmelCase				:  jnp.ndarray
       _UpperCAmelCase				:  jnp.ndarray
       _UpperCAmelCase				:  jnp.ndarray







       @classmethod
       def 						__lowerCamelCase   (				cls					:     int		, A					:     Tuple      )       ->str:
          lowerCamelCase__       :						Union[str, Any]        =							scheduler.config

          if config.trained_betas is not None:
             lowerCamelCase__       :						Optional[int]        =							jnp.asarray(config.trained_betas		, dtype=scheduler.dtype      )
          elif config.beta_schedule == "linear":
             lowerCamelCase__       :						Optional[int]        =							jnp.linspace(config.beta_start		, config.beta_end		, config.num_train_timesteps		, dtype=scheduler.dtype      )
          elif config.beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             lowerCamelCase__       :						Union[str, Any]        =							(
                 jnp.linspace(
                     config.beta_start**0.5		, config.beta_end**0.5		, config.num_train_timesteps		, dtype=scheduler.dtype      )
                 ** 2
             )
          elif config.beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             lowerCamelCase__       :						Any        =							betas_for_alpha_bar(config.num_train_timesteps		, dtype=scheduler.dtype      )
          else:
             raise NotImplementedError(
                 F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"      )

          lowerCamelCase__       :						List[str]        =							1.0 - betas

          lowerCamelCase__       :						Optional[int]        =							jnp.cumprod(A		, axis=0      )

          return cls(
              alphas=A		, betas=A		, alphas_cumprod=A		, )



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Any:







   """simple docstring"""
   lowerCamelCase__       :						Optional[Any]        =							state.alphas_cumprod

   lowerCamelCase__       :						Dict        =							alphas_cumprod[timesteps] ** 0.5
   lowerCamelCase__       :						List[Any]        =							sqrt_alpha_prod.flatten()
   lowerCamelCase__       :						Dict        =							broadcast_to_shape_from_left(UpperCAmelCase      ,  original_samples.shape					)

   lowerCamelCase__       :						Any        =							(1 - alphas_cumprod[timesteps]) ** 0.5
   lowerCamelCase__       :						Dict        =							sqrt_one_minus_alpha_prod.flatten()
   lowerCamelCase__       :						List[Any]        =							broadcast_to_shape_from_left(UpperCAmelCase      ,  original_samples.shape					)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							get_sqrt_alpha_prod(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Optional[Any]        =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples




def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							get_sqrt_alpha_prod(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







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


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

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



