






"""simple docstring"""



import numpy





class 					_UpperCAmelCase       :




					def __init__(				self     :						List[Any]    ,						A     :						numpy.ndarray    ,						A     :						numpy.ndarray   )  ->  None:
							lowercase_ : Union[str, 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_ : Optional[int]							       =   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_ : Optional[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_ : Optional[Any]							       =   numpy.random.rand(3    ,						1   )

							# Real output values provided.
							lowercase_ : str							       =   output_array

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




					def   A  (				self     :						Union[str, Any]   )  ->  numpy.ndarray:
							lowercase_ : List[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_ : Dict							       =   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   A  (				self     :						Optional[int]   )  ->  None:
							lowercase_ : 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_ : 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   )    ,						)
							lowercase_ : Dict							       =   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   A  (				self     :						str    ,						A     :						numpy.ndarray    ,						A     :						int    ,						A     :						bool   )  ->  None:
							for iteration in range(1    ,						iterations + 1   ):
									lowercase_ : int							       =   self.feedforward()
									self.back_propagation()
									if give_loss:
											lowercase_ : Optional[Any]							       =   numpy.mean(numpy.square(output - self.feedforward()   )   )
											print(F'''Iteration {iteration} Loss: {loss}'''   )






					def   A  (				self     :						Optional[Any]    ,						A     :						numpy.ndarray   )  ->  int:
							lowercase_ : Optional[int]							       =   input_arr

							lowercase_ : int							       =   sigmoid(
							    numpy.dot(self.array    ,						self.input_layer_and_first_hidden_layer_weights   )   )

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

							lowercase_ : int							       =   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 						lowercase    (				__snake_case				: numpy.ndarray	):
		return 1 / (1 + numpy.exp(-value	))



def 						lowercase    (				__snake_case				: numpy.ndarray	):
		return (value) * (1 - (value))



def 						lowercase    (				):
		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_ : int							       =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,					dtype=numpy.floataa	)

		# Calling neural network class.
		lowercase_ : str							       =   TwoHiddenLayerNeuralNetwork(
		    input_array=__snake_case		,					output_array=__snake_case	)

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

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


if __name__ == "__main__":
						example()




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  str						     =	{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "xglm"
					snake_case__				    = ["past_key_values"]

					snake_case__				    = {
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(				self   :	Any    ,lowerCamelCase__   :	Any=256_008    ,lowerCamelCase__   :	Optional[Any]=2_048    ,lowerCamelCase__   :	List[str]=1_024    ,lowerCamelCase__   :	List[str]=4_096    ,lowerCamelCase__   :	Tuple=24    ,lowerCamelCase__   :	Optional[int]=16    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	str=2    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[int]=0    ,lowerCamelCase__   :	Tuple=2    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							ffn_dim
									UpperCAmelCase__             =							num_layers
									UpperCAmelCase__             =							attention_heads
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							layerdrop
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True
									UpperCAmelCase__             =							use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,decoder_start_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)



