




"""simple docstring"""




import numpy

class   UpperCAmelCase_    :
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       None:
					__lowercase			:  Any								=       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.
					__lowercase			:  str								=       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.
					__lowercase			:  Union[str, Any]								=       numpy.random.rand(
					    4				,						3       )

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

					# Real output values provided.
					__lowercase			:  List[Any]								=       output_array

					# Predicted output values by the neural network.
					# Predicted_output array initially consists of zeroes.
					__lowercase			:  int								=       numpy.zeros(output_array.shape       )
		def 	_lowerCamelCase							(						self       )						->       numpy.ndarray:
					__lowercase			:  Union[str, Any]								=       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.
					__lowercase			:  str								=       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.
					__lowercase			:  str								=       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 	_lowerCamelCase							(						self       )						->       None:
					__lowercase			:  List[Any]								=       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       )				,						)
					__lowercase			:  Union[str, Any]								=       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       )				,						)
					__lowercase			:  Union[str, Any]								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       None:
					for iteration in range(1				,						iterations + 1       ):
								__lowercase			:  Optional[int]								=       self.feedforward()
								self.back_propagation()
								if give_loss:
											__lowercase			:  Tuple								=       numpy.mean(numpy.square(output - self.feedforward()       )       )
											print(F"""Iteration {iteration} Loss: {loss}"""       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       int:
					__lowercase			:  List[Any]								=       input_arr

					__lowercase			:  List[Any]								=       sigmoid(
					    numpy.dot(self.array				,						self.input_layer_and_first_hidden_layer_weights       )       )

					__lowercase			:  Tuple								=       sigmoid(
					    numpy.dot(
					        self.layer_between_input_and_first_hidden_layer				,						self.first_hidden_layer_and_second_hidden_layer_weights				,						)       )

					__lowercase			:  Any								=       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     ( __UpperCamelCase	):
			return 1 / (1 + numpy.exp(-value	))







def      __UpperCAmelCase     ( __UpperCamelCase	):
			return (value) * (1 - (value))







def      __UpperCAmelCase     ( ):
			__lowercase			:  List[str]								=       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.
			__lowercase			:  Optional[Any]								=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	,				dtype=numpy.floataa	)

			# Calling neural network class.
			__lowercase			:  Optional[Any]								=       TwoHiddenLayerNeuralNetwork(
			    input_array=__UpperCamelCase	,				output_array=__UpperCamelCase	)

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

			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 PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				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			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([1.0, 1.0, 0.0, 1.0, 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      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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






