


"""simple docstring"""






import numpy


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				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__     							=				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__     							=				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__     							=				numpy.random.rand(3       ,		1     )

								# Real output values provided.
								SCREAMING_SNAKE_CASE__     							=				output_array

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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				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__     							=				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__     							=				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     ):
								SCREAMING_SNAKE_CASE__     							=				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__     							=				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__     							=				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__     ):
								for iteration in range(1       ,		iterations + 1     ):
												SCREAMING_SNAKE_CASE__     							=				self.feedforward()
												self.back_propagation()
												if give_loss:
																SCREAMING_SNAKE_CASE__     							=				numpy.mean(numpy.square(output - self.feedforward()     )     )
																print(f'''Iteration {iteration} Loss: {loss}'''     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				input_arr

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

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

								SCREAMING_SNAKE_CASE__     							=				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     (      lowerCamelCase_  :      numpy.ndarray  ):
				return 1 / (1 + numpy.exp(-value  ))


def __lowercase     (      lowerCamelCase_  :      numpy.ndarray  ):
				return (value) * (1 - (value))


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				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__     							=				numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,					dtype=numpy.floataa  )

				# Calling neural network class.
				SCREAMING_SNAKE_CASE__     							=				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()




"""simple docstring"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  500000

_lowerCamelCase     ,     _lowerCamelCase     =  os.path.split(__file__)
_lowerCamelCase     =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.map(**lowerCamelCase_  )


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.filter(**lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE__     							=				datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
								SCREAMING_SNAKE_CASE__     							=				generate_example_dataset(
								    os.path.join(lowerCamelCase_						,					"dataset.arrow"  )						,					lowerCamelCase_						,					num_examples=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"						,					use_fast=lowerCamelCase_  )

								def tokenize(lowerCamelCase_  :      Union[str, Any]  ):
												return tokenizer(examples["text"]  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="numpy"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="pandas"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="torch"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="tensorflow"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				filter(lowerCamelCase_  )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_						,					"wb"  ) as f:
								f.write(json.dumps(lowerCamelCase_  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_map_filter()
