

'''simple docstring'''


import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCAmelCase__										= {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	if got_ver is None or want_ver is None:
		raise ValueError(
		    f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
		    f""" reinstalling {pkg}."""  )
	if not ops[op](version.parse(UpperCamelCase  )   ,version.parse(UpperCamelCase  )  ):
		raise ImportError(
		    f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = None  )    ->    None:
	UpperCAmelCase_		:      str       	=					f"""\n{hint}""" if hint is not None else ''

	# non-versioned check
	if re.match(r'^[\w_\-\d]+$'   ,UpperCamelCase  ):
		UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					requirement, None, None
	else:
		UpperCAmelCase_		:      Any       	=					re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'   ,UpperCamelCase  )
		if not match:
			raise ValueError(
			    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
			    f""" got {requirement}"""  )
		UpperCAmelCase_	,    UpperCAmelCase_		:      List[str]       	=					match[0]
		UpperCAmelCase_		:      Tuple       	=					want_full.split(','  )  # there could be multiple requirements
		UpperCAmelCase_		:      Union[str, Any]       	=					{}
		for w in want_range:
			UpperCAmelCase_		:      Union[str, Any]       	=					re.findall(r'^([\s!=<>]{1,2})(.+)'   ,UpperCamelCase  )
			if not match:
				raise ValueError(
				    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
				    f""" but got {requirement}"""  )
			UpperCAmelCase_	,    UpperCAmelCase_		:      List[str]       	=					match[0]
			UpperCAmelCase_		:      Optional[int]       	=					want_ver
			if op not in ops:
				raise ValueError(f"""{requirement}: need one of {list(ops.keys()  )}, but got {op}"""  )

    # special case
	if pkg == "python":
		UpperCAmelCase_		:      Optional[Any]       	=					'.'.join([str(UpperCamelCase  ) for x in sys.version_info[:3]]  )
		for op, want_ver in wanted.items():
			_compare_versions(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
		return

	# check if any version is installed
	try:
		UpperCAmelCase_		:      List[str]       	=					importlib.metadata.version(UpperCamelCase  )
	except importlib.metadata.PackageNotFoundError:
		raise importlib.metadata.PackageNotFoundError(
		    f"""The '{requirement}' distribution was not found and is required by this application. {hint}"""  )

	# check that the right version is installed if version number or a range was provided
	if want_ver is not None:
		for op, want_ver in wanted.items():
			_compare_versions(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	UpperCAmelCase_		:      List[str]       	=					'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
	return require_version(UpperCamelCase   ,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()






