


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class   __lowercase				(  __snake_case ,					__snake_case  ):
      UpperCamelCase								=       1





      @register_to_config
      def __init__(		self						:					Optional[Any]					,       __lowerCamelCase						:					int = 1_0_0_0					,       __lowerCamelCase						:					Optional[Union[np.ndarray, List[float]]] = None       )    ->   Union[str, Any]:






         """simple docstring"""







         self.set_timesteps(__lowerCamelCase       )

         # standard deviation of the initial noise distribution
         UpperCAmelCase							=     1.0

         # For now we only support F-PNDM, i.e. the runge-kutta method
         # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
         # mainly at formula (9), (12), (13) and the Algorithm 2.
         UpperCAmelCase							=     4

         # running values
         UpperCAmelCase							=     []





      def        _lowercase				(		self						:					int					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Union[str, torch.device] = None       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     num_inference_steps
         UpperCAmelCase							=     torch.linspace(1					,       0					,       num_inference_steps + 1       )[:-1]
         UpperCAmelCase							=     torch.cat([steps, torch.tensor([0.0]       )]       )

         if self.config.trained_betas is not None:
            UpperCAmelCase							=     torch.tensor(self.config.trained_betas					,       dtype=torch.floataa       )
         else:
            UpperCAmelCase							=     torch.sin(steps * math.pi / 2       ) ** 2

         UpperCAmelCase							=     (1.0 - self.betas**2) ** 0.5

         UpperCAmelCase							=     (torch.atana(self.betas					,       self.alphas       ) / math.pi * 2)[:-1]
         UpperCAmelCase							=     timesteps.to(__lowerCamelCase       )

         UpperCAmelCase							=     []





      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					torch.FloatTensor					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					torch.FloatTensor					,       __lowerCamelCase						:					bool = True					,       )    ->   Union[SchedulerOutput, Tuple]:






         """simple docstring"""







         if self.num_inference_steps is None:
            raise ValueError(
                """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

         UpperCAmelCase							=     (self.timesteps == timestep).nonzero().item()
         UpperCAmelCase							=     timestep_index + 1

         UpperCAmelCase							=     sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
         self.ets.append(__lowerCamelCase       )

         if len(self.ets       ) == 1:
            UpperCAmelCase							=     self.ets[-1]
         elif len(self.ets       ) == 2:
            UpperCAmelCase							=     (3 * self.ets[-1] - self.ets[-2]) / 2
         elif len(self.ets       ) == 3:
            UpperCAmelCase							=     (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
         else:
            UpperCAmelCase							=     (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

         UpperCAmelCase							=     self._get_prev_sample(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=__lowerCamelCase       )





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					torch.FloatTensor					,       *__lowerCamelCase						:					Union[str, Any]					,       **__lowerCamelCase						:					str       )    ->   torch.FloatTensor:






         """simple docstring"""







         return sample





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Optional[Any]					,       __lowerCamelCase						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.alphas[timestep_index]
         UpperCAmelCase							=     self.betas[timestep_index]

         UpperCAmelCase							=     self.alphas[prev_timestep_index]
         UpperCAmelCase							=     self.betas[prev_timestep_index]

         UpperCAmelCase							=     (sample - sigma * ets) / max(__lowerCamelCase					,       1e-8       )
         UpperCAmelCase							=     next_alpha * pred + ets * next_sigma

         return prev_sample





      def __len__(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







         return self.config.num_train_timesteps







import numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     numpy.random.rand(
             4					,       3       )

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

         # Real output values provided.
         UpperCAmelCase							=     output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         UpperCAmelCase							=     numpy.zeros(output_array.shape       )





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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.
         UpperCAmelCase							=     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        _lowercase				(		self						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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       )					,       )
         UpperCAmelCase							=     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        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







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





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

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

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

         UpperCAmelCase							=     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   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   UpperCAmelCase							=     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.
   UpperCAmelCase							=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,						dtype=numpy.floataa   )

   # Calling neural network class.
   UpperCAmelCase							=     TwoHiddenLayerNeuralNetwork(
       input_array=lowerCAmelCase_  ,						output_array=lowerCAmelCase_   )

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=lowerCAmelCase_  ,						iterations=1_0  ,						give_loss=lowerCAmelCase_   )

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


if __name__ == "__main__":
 example()



