





def       UpperCAmelCase							(     _lowerCamelCase      :  list[int]			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					int(_lowerCamelCase     )

						# Initialize Result
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[]

						# Traverse through all denomination
						for denomination in reversed(_lowerCamelCase     ):
												# Find denominations
												while int(_lowerCamelCase     ) >= int(_lowerCamelCase     ):
																		total_value -= int(_lowerCamelCase     )
																		answer.append(_lowerCamelCase     )  # Append the "answers" array

						return answer


# Driver Code
if __name__ == "__main__":
				__lowercase       :Dict       =  []
				__lowercase       :Tuple       =  "0"

				if (
				    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
				    == "y"
				):
								__lowercase       :str       =  int(input("Enter the number of denominations you want to add: ").strip())

								for i in range(0, n):
												denominations.append(int(input(f"Denomination {i}: ").strip()))
								__lowercase       :str       =  input("Enter the change you want to make in Indian Currency: ").strip()
				else:
								# All denominations of Indian Currency if user does not enter
								__lowercase       :Dict       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
								__lowercase       :Tuple       =  input("Enter the change you want to make: ").strip()

				if int(value) == 0 or int(value) < 0:
								print("The total value cannot be zero or negative.")

				else:
								print(f"Following is minimal change for {value}: ")
								__lowercase       :Dict       =  find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=" ")






import numpy



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     numpy.ndarray							,	a				:     numpy.ndarray    )	->None:
													SCREAMING_SNAKE_CASE__     :				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.
													SCREAMING_SNAKE_CASE__     :				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.
													SCREAMING_SNAKE_CASE__     :				Dict   =					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__     :				List[Any]   =					numpy.random.rand(3							,	1    )

													# Real output values provided.
													SCREAMING_SNAKE_CASE__     :				str   =					output_array

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

							def      A_  (      self				:     Union[str, Any]    )	->numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :				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.
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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__     :				int   =					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				:     int    )	->None:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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__     :				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    )							,	)
													SCREAMING_SNAKE_CASE__     :				int   =					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				:     int							,	a				:     numpy.ndarray							,	a				:     int							,	a				:     bool    )	->None:
													for iteration in range(1							,	iterations + 1    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.feedforward()
																			self.back_propagation()
																			if give_loss:
																									SCREAMING_SNAKE_CASE__     :				int   =					numpy.mean(numpy.square(output - self.feedforward()    )    )
																									print(f"""Iteration {iteration} Loss: {loss}"""    )






							def      A_  (      self				:     Tuple							,	a				:     numpy.ndarray    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_arr

													SCREAMING_SNAKE_CASE__     :				Dict   =					sigmoid(
													    numpy.dot(self.array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													SCREAMING_SNAKE_CASE__     :				Any   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													SCREAMING_SNAKE_CASE__     :				Optional[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       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return 1 / (1 + numpy.exp(-value     ))







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return (value) * (1 - (value))







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				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.
						SCREAMING_SNAKE_CASE__     :				Any   =					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,   dtype=numpy.floataa     )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE__     :				List[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()