





"""simple docstring"""
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__A       =		logging.get_logger(__name__)






class  _SCREAMING_SNAKE_CASE(       _UpperCamelCase  ):
       SCREAMING_SNAKE_CASE_       :				Any     						=  'linear'
       SCREAMING_SNAKE_CASE_       :				int     						=  'cosine'
       SCREAMING_SNAKE_CASE_       :				str     						=  'cosine_with_restarts'
       SCREAMING_SNAKE_CASE_       :				int     						=  'polynomial'
       SCREAMING_SNAKE_CASE_       :				str     						=  'constant'
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  'constant_with_warmup'
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  'piecewise_constant'




def 					__lowerCamelCase       (							a_					:		Optimizer			,       a_					:		int = -1							)      ->				Any:
      return LambdaLR(_UpperCamelCase			,       lambda a_							: 1			,       last_epoch=_UpperCamelCase							)



def 					__lowerCamelCase       (							a_					:		Optimizer			,       a_					:		int			,       a_					:		int = -1							)      ->				List[str]:

      def lr_lambda(a_					:		int							):
            if current_step < num_warmup_steps:
                  return float(_UpperCamelCase							) / float(max(1.0			,       _UpperCamelCase							)							)
            return 1.0

      return LambdaLR(_UpperCamelCase			,       _UpperCamelCase			,       last_epoch=_UpperCamelCase							)



def 					__lowerCamelCase       (							a_					:		Optimizer			,       a_					:		str			,       a_					:		int = -1							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  {}
      __SCREAMING_SNAKE_CASE       :Dict		    =  step_rules.split(''','''							)
      for rule_str in rule_list[:-1]:
            __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  rule_str.split(''':'''							)
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  int(_UpperCamelCase							)
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  float(_UpperCamelCase							)
            __SCREAMING_SNAKE_CASE       :str		    =  value
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  float(rule_list[-1]							)

      def create_rules_function(a_					:		Union[str, Any]			,       a_					:		Any							):
            def rule_func(a_					:		int							) -> float:
                  __SCREAMING_SNAKE_CASE       :str		    =  sorted(rules_dict.keys()							)
                  for i, sorted_step in enumerate(_UpperCamelCase							):
                        if steps < sorted_step:
                              return rules_dict[sorted_steps[i]]
                  return last_lr_multiple

            return rule_func

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  create_rules_function(_UpperCamelCase			,       _UpperCamelCase							)

      return LambdaLR(_UpperCamelCase			,       _UpperCamelCase			,       last_epoch=_UpperCamelCase							)



def 					__lowerCamelCase       (							a_					:		Any			,       a_					:		Optional[Any]			,       a_					:		Any			,       a_					:		Optional[int]=-1							)      ->				int:

      def lr_lambda(a_					:		int							):
            if current_step < num_warmup_steps:
                  return float(_UpperCamelCase							) / float(max(1			,       _UpperCamelCase							)							)
            return max(
                0.0			,       float(num_training_steps - current_step							) / float(max(1			,       num_training_steps - num_warmup_steps							)							)							)

      return LambdaLR(_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase							)



def 					__lowerCamelCase       (							a_					:		Optimizer			,       a_					:		int			,       a_					:		int			,       a_					:		float = 0.5			,       a_					:		int = -1							)      ->				str:

      def lr_lambda(a_					:		Optional[int]							):
            if current_step < num_warmup_steps:
                  return float(_UpperCamelCase							) / float(max(1			,       _UpperCamelCase							)							)
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  float(current_step - num_warmup_steps							) / float(max(1			,       num_training_steps - num_warmup_steps							)							)
            return max(0.0			,       0.5 * (1.0 + math.cos(math.pi * float(_UpperCamelCase							) * 2.0 * progress							))							)

      return LambdaLR(_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase							)



def 					__lowerCamelCase       (							a_					:		Optimizer			,       a_					:		int			,       a_					:		int			,       a_					:		int = 1			,       a_					:		int = -1							)      ->				Any:

      def lr_lambda(a_					:		List[Any]							):
            if current_step < num_warmup_steps:
                  return float(_UpperCamelCase							) / float(max(1			,       _UpperCamelCase							)							)
            __SCREAMING_SNAKE_CASE       :str		    =  float(current_step - num_warmup_steps							) / float(max(1			,       num_training_steps - num_warmup_steps							)							)
            if progress >= 1.0:
                  return 0.0
            return max(0.0			,       0.5 * (1.0 + math.cos(math.pi * ((float(_UpperCamelCase							) * progress) % 1.0)							))							)

      return LambdaLR(_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase							)



def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		Optional[int]			,       a_					:		List[Any]			,       a_					:		Dict=1e-7			,       a_					:		Dict=1.0			,       a_					:		Tuple=-1							)      ->				Optional[Any]:
      __SCREAMING_SNAKE_CASE       :str		    =  optimizer.defaults['''lr''']
      if not (lr_init > lr_end):
            raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''							)

      def lr_lambda(a_					:		int							):
            if current_step < num_warmup_steps:
                  return float(_UpperCamelCase							) / float(max(1			,       _UpperCamelCase							)							)
            elif current_step > num_training_steps:
                  return lr_end / lr_init  # as LambdaLR multiplies by lr_init
            else:
                  __SCREAMING_SNAKE_CASE       :Dict		    =  lr_init - lr_end
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_training_steps - num_warmup_steps
                  __SCREAMING_SNAKE_CASE       :Any		    =  1 - (current_step - num_warmup_steps) / decay_steps
                  __SCREAMING_SNAKE_CASE       :str		    =  lr_range * pct_remaining**power + lr_end
                  return decay / lr_init  # as LambdaLR multiplies by lr_init

      return LambdaLR(_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase							)


__A       =		{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 					__lowerCamelCase       (							a_					:		Union[str, SchedulerType]			,       a_					:		Optimizer			,       a_					:		Optional[str] = None			,       a_					:		Optional[int] = None			,       a_					:		Optional[int] = None			,       a_					:		int = 1			,       a_					:		float = 1.0			,       a_					:		int = -1			,       )      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :int		    =  SchedulerType(_UpperCamelCase							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  TYPE_TO_SCHEDULER_FUNCTION[name]
      if name == SchedulerType.CONSTANT:
            return schedule_func(_UpperCamelCase			,       last_epoch=_UpperCamelCase							)

      if name == SchedulerType.PIECEWISE_CONSTANT:
            return schedule_func(_UpperCamelCase			,       step_rules=_UpperCamelCase			,       last_epoch=_UpperCamelCase							)

      # All other schedulers require `num_warmup_steps`
      if num_warmup_steps is None:
            raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.'''							)

      if name == SchedulerType.CONSTANT_WITH_WARMUP:
            return schedule_func(_UpperCamelCase			,       num_warmup_steps=_UpperCamelCase			,       last_epoch=_UpperCamelCase							)

      # All other schedulers require `num_training_steps`
      if num_training_steps is None:
            raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.'''							)

      if name == SchedulerType.COSINE_WITH_RESTARTS:
            return schedule_func(
                _UpperCamelCase			,       num_warmup_steps=_UpperCamelCase			,       num_training_steps=_UpperCamelCase			,       num_cycles=_UpperCamelCase			,       last_epoch=_UpperCamelCase			,       )

      if name == SchedulerType.POLYNOMIAL:
            return schedule_func(
                _UpperCamelCase			,       num_warmup_steps=_UpperCamelCase			,       num_training_steps=_UpperCamelCase			,       power=_UpperCamelCase			,       last_epoch=_UpperCamelCase			,       )

      return schedule_func(
          _UpperCamelCase			,       num_warmup_steps=_UpperCamelCase			,       num_training_steps=_UpperCamelCase			,       last_epoch=_UpperCamelCase							)






"""simple docstring"""
import math
import random



def 					__lowerCamelCase       (							a_					:		float			,       a_					:		bool = False							)      ->				float:
      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
lowerCamelCase_       =		0.02



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :Any		    =  float(2 * (random.randint(1			,       1_00							)) - 1							)

      for _ in range(a_							):
            # Forward propagation
            __SCREAMING_SNAKE_CASE       :Any		    =  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            __SCREAMING_SNAKE_CASE       :Tuple		    =  (expected / 1_00) - layer_a
            # Error delta
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  layer_1_error * sigmoid_function(a_			,       a_							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 1_00


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase_       =		int(input("Expected value: "))
  lowerCamelCase_       =		int(input("Number of propagations: "))
  print(forward_propagation(expected, number_propagations))