


'''simple docstring'''



from typing import List

import numpy as np







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       {key: len(UpperCamelCase__   ) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__	, UpperCamelCase__   )}
			if len(set(lists_lengths.values()   )   ) > 1:
						raise RuntimeError(
						    (
						        """Sharding is ambiguous for this dataset: """
						        + """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
						        + """\n""".join(f'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()   )
						        + """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
						        + """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
						    )   )
			UpperCAmelCase__					:   Any       				=       max(lists_lengths.values()	, default=0   )
			return max(1	, UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       []
			for group_idx in range(UpperCamelCase__   ):
						UpperCAmelCase__					:   str       				=       num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
						if num_shards_to_add == 0:
									break
						UpperCAmelCase__					:   List[Any]       				=       shards_indices_per_group[-1].stop if shards_indices_per_group else 0
						UpperCAmelCase__					:   Optional[int]       				=       range(UpperCamelCase__	, start + num_shards_to_add   )
						shards_indices_per_group.append(UpperCamelCase__   )
			return shards_indices_per_group







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       _number_of_shards_in_gen_kwargs(UpperCamelCase__   )
			if num_shards == 1:
						return [dict(UpperCamelCase__   )]
			else:
						UpperCAmelCase__					:   Union[str, Any]       				=       _distribute_shards(num_shards=UpperCamelCase__	, max_num_jobs=UpperCamelCase__   )
						return [
						    {
						        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
						        if isinstance(UpperCamelCase__	, UpperCamelCase__   )
						        else value
						        for key, value in gen_kwargs.items()
						    }
						    for group_idx in range(len(UpperCamelCase__   )   )
						]







def       _UpperCamelCase (						UpperCamelCase__   ):
			return {
			    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
			    if isinstance(gen_kwargs_list[0][key]	, UpperCamelCase__   )
			    else gen_kwargs_list[0][key]
			    for key in gen_kwargs_list[0]
			}







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       {len(UpperCamelCase__   ) for value in gen_kwargs.values() if isinstance(UpperCamelCase__	, UpperCamelCase__   )}
			UpperCAmelCase__					:   str       				=       {}
			for size in list_sizes:
						UpperCAmelCase__					:   Tuple       				=       list(range(UpperCamelCase__   )   )
						rng.shuffle(indices_per_size[size]   )
			# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
			UpperCAmelCase__					:   Optional[int]       				=       dict(UpperCamelCase__   )
			for key, value in shuffled_kwargs.items():
						if isinstance(UpperCamelCase__	, UpperCamelCase__   ):
									UpperCAmelCase__					:   Tuple       				=       [value[i] for i in indices_per_size[len(UpperCamelCase__   )]]
			return shuffled_kwargs



'''simple docstring'''



import numpy


class 						_snake_case   :




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       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__					:   Optional[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__					:   Optional[int]       				=       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__					:   Any       				=       numpy.random.rand(3							,							1)

								# Real output values provided.
								UpperCAmelCase__					:   Tuple       				=       output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase__					:   Union[str, Any]       				=       numpy.zeros(output_array.shape)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       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__					:   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__					:   Tuple       				=       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 			snake_case__  ( self):
								UpperCAmelCase__					:   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)							,							)
								UpperCAmelCase__					:   str       				=       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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								for iteration in range(1							,							iterations + 1):
											UpperCAmelCase__					:   Optional[Any]       				=       self.feedforward()
											self.back_propagation()
											if give_loss:
														UpperCAmelCase__					:   str       				=       numpy.mean(numpy.square(output - self.feedforward()))
														print(f'''Iteration {iteration} Loss: {loss}''')




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       input_arr

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

								UpperCAmelCase__					:   List[Any]       				=       sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer							,							self.first_hidden_layer_and_second_hidden_layer_weights							,							))

								UpperCAmelCase__					:   Optional[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 (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       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__					:   str       				=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	, dtype=numpy.floataa   )

			# Calling neural network class.
			UpperCAmelCase__					:   List[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=1_0	, give_loss=UpperCamelCase__   )

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


if __name__ == "__main__":
		example()