

'''simple docstring'''



import numpy







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Union[str, 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   :			Optional[int]     )    ->					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   :			str     )    ->					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   :			Tuple   ,				__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   :			Optional[int]   ,				__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 unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = UNetaDModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,				)
      return model


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2







@slow
@require_torch
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






