

'''simple docstring'''



import numpy







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       numpy.ndarray					,							__SCREAMING_SNAKE_CASE					:       numpy.ndarray			)  -> None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      input_array

      # Random initial weights are assigned where first argument is the
      # number of nodes in previous layer and second argument is the
      # number of nodes in the next layer.

      # Random initial weights are assigned.
      # self.input_array.shape[1] is used to represent number of nodes in input layer.
      # First hidden layer consists of 4 nodes.
      __SCREAMING_SNAKE_CASE     =      numpy.random.rand(
          self.input_array.shape[1]					,							4			)

      # Random initial values for the first hidden layer.
      # First hidden layer has 4 nodes.
      # Second hidden layer has 3 nodes.
      __SCREAMING_SNAKE_CASE     =      numpy.random.rand(
          4					,							3			)

      # Random initial values for the second hidden layer.
      # Second hidden layer has 3 nodes.
      # Output layer has 1 node.
      __SCREAMING_SNAKE_CASE     =      numpy.random.rand(3					,							1			)

      # Real output values provided.
      __SCREAMING_SNAKE_CASE     =      output_array

      # Predicted output values by the neural network.
      # Predicted_output array initially consists of zeroes.
      __SCREAMING_SNAKE_CASE     =      numpy.zeros(output_array.shape			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> numpy.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      sigmoid(
          numpy.dot(self.input_array					,							self.input_layer_and_first_hidden_layer_weights			)			)

      # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
      # connecting the first hidden set of nodes with the second hidden set of nodes.
      __SCREAMING_SNAKE_CASE     =      sigmoid(
          numpy.dot(
              self.layer_between_input_and_first_hidden_layer					,							self.first_hidden_layer_and_second_hidden_layer_weights					,							)			)

      # layer_between_second_hidden_layer_and_output is the layer connecting
      # second hidden layer with the output node.
      __SCREAMING_SNAKE_CASE     =      sigmoid(
          numpy.dot(
              self.layer_between_first_hidden_layer_and_second_hidden_layer					,							self.second_hidden_layer_and_output_layer_weights					,							)			)

      return self.layer_between_second_hidden_layer_and_output
 def       UpperCAmelCase__		(		self					:       int			)  -> None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      numpy.dot(
          self.layer_between_first_hidden_layer_and_second_hidden_layer.T					,							2
          * (self.output_array - self.predicted_output)
          * sigmoid_derivative(self.predicted_output			)					,							)
      __SCREAMING_SNAKE_CASE     =      numpy.dot(
          self.layer_between_input_and_first_hidden_layer.T					,							numpy.dot(
              2
              * (self.output_array - self.predicted_output)
              * sigmoid_derivative(self.predicted_output			)					,							self.second_hidden_layer_and_output_layer_weights.T					,							)
          * sigmoid_derivative(
              self.layer_between_first_hidden_layer_and_second_hidden_layer			)					,							)
      __SCREAMING_SNAKE_CASE     =      numpy.dot(
          self.input_array.T					,							numpy.dot(
              numpy.dot(
                  2
                  * (self.output_array - self.predicted_output)
                  * sigmoid_derivative(self.predicted_output			)					,							self.second_hidden_layer_and_output_layer_weights.T					,							)
              * sigmoid_derivative(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer			)					,							self.first_hidden_layer_and_second_hidden_layer_weights.T					,							)
          * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer			)					,							)

      self.input_layer_and_first_hidden_layer_weights += (
          updated_input_layer_and_first_hidden_layer_weights
      )
      self.first_hidden_layer_and_second_hidden_layer_weights += (
          updated_first_hidden_layer_and_second_hidden_layer_weights
      )
      self.second_hidden_layer_and_output_layer_weights += (
          updated_second_hidden_layer_and_output_layer_weights
      )
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       numpy.ndarray					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       bool			)  -> None:


      """simple docstring"""




      for iteration in range(1					,							iterations + 1			):
           __SCREAMING_SNAKE_CASE     =      self.feedforward()
           self.back_propagation()
           if give_loss:
                __SCREAMING_SNAKE_CASE     =      numpy.mean(numpy.square(output - self.feedforward()			)			)
                print(f'Iteration {iteration} Loss: {loss}'			)


 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       numpy.ndarray			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      input_arr

      __SCREAMING_SNAKE_CASE     =      sigmoid(
          numpy.dot(self.array					,							self.input_layer_and_first_hidden_layer_weights			)			)

      __SCREAMING_SNAKE_CASE     =      sigmoid(
          numpy.dot(
              self.layer_between_input_and_first_hidden_layer					,							self.first_hidden_layer_and_second_hidden_layer_weights					,							)			)

      __SCREAMING_SNAKE_CASE     =      sigmoid(
          numpy.dot(
              self.layer_between_first_hidden_layer_and_second_hidden_layer					,							self.second_hidden_layer_and_output_layer_weights					,							)			)

      return int(self.layer_between_second_hidden_layer_and_output > 0.6			)







def    a__       (  a__						):




     """simple docstring"""


     return 1 / (1 + numpy.exp(-value						))







def    a__       (  a__						):




     """simple docstring"""


     return (value) * (1 - (value))







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      numpy.array(
         (
             [0, 0, 0],
             [0, 0, 1],
             [0, 1, 0],
             [0, 1, 1],
             [1, 0, 0],
             [1, 0, 1],
             [1, 1, 0],
             [1, 1, 1],
         )      ,    dtype=numpy.floataa      ,    )

     # True output values for the given input values.
     __SCREAMING_SNAKE_CASE     =      numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])      ,    dtype=numpy.floataa						)

     # Calling neural network class.
     __SCREAMING_SNAKE_CASE     =      TwoHiddenLayerNeuralNetwork(
         input_array=a__      ,    output_array=a__						)

     # Calling training function.
     # Set give_loss to True if you want to see loss in every iteration.
     neural_network.train(output=a__      ,    iterations=10      ,    give_loss=a__						)

     return neural_network.predict(numpy.array(([1, 1, 1])      ,    dtype=numpy.floataa						)						)


if __name__ == "__main__":
  example()









'''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


UpperCAmelCase :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


     return LambdaLR(a__      ,    lambda a__						: 1      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1.0      ,    a__						)						)
          return 1.0

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      step_rules.split(""","""						)
     for rule_str in rule_list[:-1]:
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      rule_str.split(""":"""						)
          __SCREAMING_SNAKE_CASE     =      int(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

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

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


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

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      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(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      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(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      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__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __SCREAMING_SNAKE_CASE     =      lr_init - lr_end
               __SCREAMING_SNAKE_CASE     =      num_training_steps - num_warmup_steps
               __SCREAMING_SNAKE_CASE     =      1 - (current_step - num_warmup_steps) / decay_steps
               __SCREAMING_SNAKE_CASE     =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    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    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(a__      ,    last_epoch=a__						)

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

     # 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(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

     # 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(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    num_cycles=a__      ,    last_epoch=a__      ,    )

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

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






