





'''simple docstring'''







import numpy
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Any					,				__a     :					numpy.ndarray					,				__a     :					numpy.ndarray					):
								_a								= 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.
								_a								= 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.
								_a								= numpy.random.rand(
								    4					,				3					)

								# Random initial values for the second hidden layer.
								# Second hidden layer has 3 nodes.
								# Output layer has 1 node.
								_a								= numpy.random.rand(3					,				1					)

								# Real output values provided.
								_a								= output_array

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




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= 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.
								_a								= 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.
								_a								= 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   UpperCamelCase__   (  self     :					Dict					):
								_a								= 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					)					,				)
								_a								= 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					)					,				)
								_a								= 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   UpperCamelCase__   (  self     :					Any					,				__a     :					numpy.ndarray					,				__a     :					int					,				__a     :					bool					):
								for iteration in range(1					,				iterations + 1					):
												_a								= self.feedforward()
												self.back_propagation()
												if give_loss:
																_a								= numpy.mean(numpy.square(output - self.feedforward()					)					)
																print(f'Iteration {iteration} Loss: {loss}'					)




				def   UpperCamelCase__   (  self     :					Tuple					,				__a     :					numpy.ndarray					):
								_a								= input_arr

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

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

								_a								= 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  _lowerCamelCase				( lowercase						:      numpy.ndarray						)	->	numpy.ndarray:
				return 1 / (1 + numpy.exp(-value						))






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






def  _lowerCamelCase				( )	->	int:
				_a								= 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.
				_a								= numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,							dtype=numpy.floataa						)

				# Calling neural network class.
				_a								= TwoHiddenLayerNeuralNetwork(
				    input_array=lowercase    ,							output_array=lowercase						)

				# Calling training function.
				# Set give_loss to True if you want to see loss in every iteration.
				neural_network.train(output=lowercase    ,							iterations=10    ,							give_loss=lowercase						)

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


if __name__ == "__main__":
					example()







'''simple docstring'''







import argparse
import os
import re


lowerCAmelCase_					:    Any      	=			'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_					:    List[str]      	=			re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
lowerCAmelCase_					:    Tuple      	=			re.compile(R'\s*\(\s*"(\S[^"]+)"')






def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      bool = False						)	->	Optional[Any]:
				with open(lowercase    ,							"r"    ,							encoding="utf-8"						) as f:
								_a								= f.read()

				_a								= content.split("\n"						)
				_a								= []
				_a								= 0
				while line_idx < len(lowercase						):
								if _re_intro_mapping.search(lines[line_idx]						) is not None:
												_a								= len(re.search(r"^(\s*)\S"    ,							lines[line_idx]						).groups()[0]						) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(" " * indent + "("						):
																new_lines.append(lines[line_idx]						)
																line_idx += 1

												_a								= []
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				_a								= line_idx
																				while not lines[line_idx].startswith(" " * indent + ")"						):
																								line_idx += 1
																				blocks.append("\n".join(lines[start_idx : line_idx + 1]						)						)
																else:
																				blocks.append(lines[line_idx]						)
																line_idx += 1

												# Sort blocks by their identifiers
												_a								= sorted(lowercase    ,							key=lambda lowercase						: _re_identifier.search(lowercase						).groups()[0]						)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]						)
												line_idx += 1

				if overwrite:
								with open(lowercase    ,							"w"    ,							encoding="utf-8"						) as f:
												f.write("\n".join(lowercase						)						)
				elif "\n".join(lowercase						) != content:
								return True






def  _lowerCamelCase				( lowercase						:      bool = False						)	->	List[str]:
				_a								= [os.path.join(lowercase    ,							lowercase						) for f in os.listdir(lowercase						) if f.endswith(".py"						)]
				_a								= [sort_auto_mapping(lowercase    ,							overwrite=lowercase						) for fname in fnames]

				if not overwrite and any(lowercase						):
								_a								= [f for f, d in zip(lowercase    ,							lowercase						) if d]
								raise ValueError(
								    F'The following files have auto mappings that need sorting: {", ".join(lowercase						)}. Run `make style` to fix'
								    " this."						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			argparse.ArgumentParser()
					parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
					lowerCAmelCase_					:    Optional[int]      	=			parser.parse_args()

					sort_all_auto_mappings(not args.check_only)
