
import numpy

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       str						,	snake_case_ :       numpy.ndarray						,	snake_case_ :       numpy.ndarray      ):
							UpperCamelCase_:							str	=						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_:							List[Any]	=						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_:							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.
							UpperCamelCase_:							List[Any]	=						numpy.random.rand(3						,	1      )

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

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

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Optional[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.
							UpperCamelCase_:							Union[str, Any]	=						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_:							List[Any]	=						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 :       Optional[Any]      ):
							UpperCamelCase_:							Tuple	=						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_:							Optional[int]	=						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_:							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 :       int						,	snake_case_ :       numpy.ndarray						,	snake_case_ :       int						,	snake_case_ :       bool      ):
							for iteration in range(1						,	iterations + 1      ):
											UpperCamelCase_:							Tuple	=						self.feedforward()
											self.back_propagation()
											if give_loss:
															UpperCamelCase_:							int	=						numpy.mean(numpy.square(output - self.feedforward()      )      )
															print(f'''Iteration {iteration} Loss: {loss}'''      )




			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       numpy.ndarray      ):
							UpperCamelCase_:							Optional[Any]	=						input_arr

							UpperCamelCase_:							Tuple	=						sigmoid(
							    numpy.dot(self.array						,	self.input_layer_and_first_hidden_layer_weights      )      )

							UpperCamelCase_:							Dict	=						sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer						,	self.first_hidden_layer_and_second_hidden_layer_weights						,	)      )

							UpperCamelCase_:							List[str]	=						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__					(						lowerCamelCase						)  ->	numpy.ndarray:
				return 1 / (1 + numpy.exp(-value						))







def        A__					(						lowerCamelCase						)  ->	numpy.ndarray:
				return (value) * (1 - (value))







def        A__					(						)  ->	int:
				UpperCamelCase_:							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.
				UpperCamelCase_:							Union[str, Any]	=						numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,    dtype=numpy.floataa						)

				# Calling neural network class.
				UpperCamelCase_:							Optional[Any]	=						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=10     ,    give_loss=lowerCamelCase						)

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


if __name__ == "__main__":
							example()








import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowerCamelCase_						: Optional[int]         =  HUGGINGFACE_HUB_CACHE


lowerCamelCase_						: List[str]         =  """config.json"""
lowerCamelCase_						: Any         =  """diffusion_pytorch_model.bin"""
lowerCamelCase_						: Union[str, Any]         =  """diffusion_flax_model.msgpack"""
lowerCamelCase_						: Dict         =  """model.onnx"""
lowerCamelCase_						: List[Any]         =  """diffusion_pytorch_model.safetensors"""
lowerCamelCase_						: Optional[Any]         =  """weights.pb"""
lowerCamelCase_						: Optional[Any]         =  """https://huggingface.co"""
lowerCamelCase_						: Union[str, Any]         =  default_cache_path
lowerCamelCase_						: Tuple         =  """diffusers_modules"""
lowerCamelCase_						: Optional[Any]         =  os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowerCamelCase_						: str         =  ["""fp16""", """non-ema"""]
lowerCamelCase_						: List[Any]         =  """.self_attn"""






