

'''simple docstring'''


import pytest

import datasets


# Import fixture modules as plugins
lowerCAmelCase__										= ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
	for item in items:
		if any(marker in item.keywords for marker in ['integration', 'unit']  ):
			continue
		item.add_marker(pytest.mark.unit  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Union[str, Any]:
	config.addinivalue_line('markers'   ,'torchaudio_latest: mark test to run with torchaudio>=0.12'  )







@pytest.fixture(autouse=UpperCamelCase  )
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
	UpperCAmelCase_		:      Any       	=					tmp_path_factory.getbasetemp() / 'cache'
	UpperCAmelCase_		:      Optional[Any]       	=					test_hf_cache_home / 'datasets'
	UpperCAmelCase_		:      Any       	=					test_hf_cache_home / 'metrics'
	UpperCAmelCase_		:      Optional[int]       	=					test_hf_cache_home / 'modules'
	monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'   ,str(UpperCamelCase  )  )
	monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'   ,str(UpperCamelCase  )  )
	monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'   ,str(UpperCamelCase  )  )
	UpperCAmelCase_		:      Any       	=					test_hf_datasets_cache / 'downloads'
	monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'   ,str(UpperCamelCase  )  )
	UpperCAmelCase_		:      int       	=					test_hf_datasets_cache / 'downloads' / 'extracted'
	monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,str(UpperCamelCase  )  )







@pytest.fixture(autouse=UpperCamelCase   ,scope='session'  )
def 						SCREAMING_SNAKE_CASE(  )    ->    Optional[Any]:
	datasets.disable_progress_bar()







@pytest.fixture(autouse=UpperCamelCase  )
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	# don't take tests into account when counting downloads
	monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'   ,UpperCamelCase  )







@pytest.fixture
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
	# To be removed once SQLAlchemy 2.0 supported
	monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'   ,UpperCamelCase  )









'''simple docstring'''


import numpy



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Optional[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.
								UpperCAmelCase_		:      Tuple       	=					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_		:      List[str]       	=					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_		:      Dict       	=					numpy.random.rand(3    ,			1)

								# Real output values provided.
								UpperCAmelCase_		:      str       	=					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)							->				numpy.ndarray:
								UpperCAmelCase_		:      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_		:      Tuple       	=					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_		:      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 						_snake_case							(			self)							->				None:
								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_		:      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)    ,			)
								UpperCAmelCase_		:      Tuple       	=					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    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				None:
								for iteration in range(1    ,			iterations + 1):
									UpperCAmelCase_		:      int       	=					self.feedforward()
									self.back_propagation()
									if give_loss:
										UpperCAmelCase_		:      List[Any]       	=					numpy.mean(numpy.square(output - self.feedforward()))
										print(F"""Iteration {iteration} Loss: {loss}""")





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					input_arr

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

								UpperCAmelCase_		:      Optional[int]       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								UpperCAmelCase_		:      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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return 1 / (1 + numpy.exp(-value  ))







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return (value) * (1 - (value))







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					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_		:      Dict       	=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,dtype=numpy.floataa  )

	# Calling neural network class.
	UpperCAmelCase_		:      List[str]       	=					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()






