





"""simple docstring"""
import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__A       =		logging.get_logger(__name__)

__A       =		"T5Config"



def 					__lowerCamelCase       (							a_					:		jnp.array			,       a_					:		int			,       a_					:		int							)      ->				jnp.ndarray:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  jnp.zeros_like(a_							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
      __SCREAMING_SNAKE_CASE       :Any		    =  shifted_input_ids.at[:, 0].set(a_							)

      __SCREAMING_SNAKE_CASE       :int		    =  jnp.where(shifted_input_ids == -1_00			,       a_			,       a_							)
      return shifted_input_ids






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''mt5'''
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  MTaConfig






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''mt5'''
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  MTaConfig






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  '''mt5'''
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  MTaConfig






"""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))