

import numpy



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		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.
      snake_case							:		Dict        =		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.
      snake_case							:		Union[str, Any]        =		numpy.random.rand(
          4   ,3    )

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

      # Real output values provided.
      snake_case							:		Any        =		output_array

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, 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.
      snake_case							:		int        =		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.
      snake_case							:		Dict        =		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    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		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    )   ,)
      snake_case							:		Union[str, 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    )   ,)
      snake_case							:		List[str]        =		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   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''





      for iteration in range(1   ,iterations + 1    ):
          snake_case							:		Dict        =		self.feedforward()
          self.back_propagation()
          if give_loss:
              snake_case							:		Tuple        =		numpy.mean(numpy.square(output - self.feedforward()    )    )
              print(F"""Iteration {iteration} Loss: {loss}"""    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		input_arr

      snake_case							:		Optional[Any]        =		sigmoid(
          numpy.dot(self.array   ,self.input_layer_and_first_hidden_layer_weights    )    )

      snake_case							:		str        =		sigmoid(
          numpy.dot(
              self.layer_between_input_and_first_hidden_layer   ,self.first_hidden_layer_and_second_hidden_layer_weights   ,)    )

      snake_case							:		str        =		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					(	__A :					Union[str, Any]		)				->			str:





    '''simple docstring'''




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







def       lowercase					(	__A :					List[str]		)				->			Any:





    '''simple docstring'''




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







def       lowercase					(	)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		str        =		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.
    snake_case							:		str        =		numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) ,    dtype=numpy.floataa		)

    # Calling neural network class.
    snake_case							:		Optional[Any]        =		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=10 ,    give_loss=__A		)

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


if __name__ == "__main__":
     example()










import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(lowerCamelCase )

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

