
import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE							:Union[str, Any]										=    [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]


def 						UpperCAmelCase      (     a_						)		->     Dict:







				"""simple docstring"""



				for file in FILES_TO_FIND:
								if not (transformers_path / file).exists():
												return False
				return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int										=    argparse.ArgumentParser()
					parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
					SCREAMING_SNAKE_CASE							:str										=    parser.parse_args()
					if args.check_lib:
										SCREAMING_SNAKE_CASE							:Tuple										=    importlib.import_module('transformers')
										SCREAMING_SNAKE_CASE							:Union[str, Any]										=    Path(transformers_module.__file__).parent
					else:
										SCREAMING_SNAKE_CASE							:List[str]										=    Path.cwd() / 'build/lib/transformers'
					if not test_custom_files_are_present(transformers_path):
										raise ValueError('The built release does not contain the custom files. Fix this before going further!')


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,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 :			str			):
					__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 :			Tuple							,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 :			List[Any]							,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 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return 1 / (1 + numpy.exp(-value						))


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return (value) * (1 - (value))


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__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=a_		,       output_array=a_						)

				# Calling training function.
				# Set give_loss to True if you want to see loss in every iteration.
				neural_network.train(output=a_		,       iterations=1_0		,       give_loss=a_						)

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


if __name__ == "__main__":
					example()
