
"""simple docstring"""



import numpy





class 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					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__	:      Optional[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.
          UpperCAmelCase__	:      List[str]     							=					numpy.random.rand(3     ,	1      )

          # Real output values provided.
          UpperCAmelCase__	:      List[str]     							=					output_array

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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[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__	:      Dict     							=					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__	:      Union[str, Any]     							=					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      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					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__	:      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      )     ,	)
          UpperCAmelCase__	:      Optional[Any]     							=					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     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          for iteration in range(1     ,	iterations + 1      ):
              UpperCAmelCase__	:      Optional[Any]     							=					self.feedforward()
              self.back_propagation()
              if give_loss:
                  UpperCAmelCase__	:      Union[str, Any]     							=					numpy.mean(numpy.square(output - self.feedforward()      )      )
                  print(F'''Iteration {iteration} Loss: {loss}'''      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					input_arr

          UpperCAmelCase__	:      Optional[int]     							=					sigmoid(
              numpy.dot(self.array     ,	self.input_layer_and_first_hidden_layer_weights      )      )

          UpperCAmelCase__	:      List[str]     							=					sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer     ,	self.first_hidden_layer_and_second_hidden_layer_weights     ,	)      )

          UpperCAmelCase__	:      List[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 					lowerCamelCase   (     _snake_case ):
    return 1 / (1 + numpy.exp(-value ))
def 					lowerCamelCase   (     _snake_case ):
    return (value) * (1 - (value))
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      Tuple     							=					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__	:      List[str]     							=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,dtype=numpy.floataa )

    # Calling neural network class.
    UpperCAmelCase__	:      Optional[Any]     							=					TwoHiddenLayerNeuralNetwork(
        input_array=_snake_case    ,output_array=_snake_case )

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

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


if __name__ == "__main__":
    example()



"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      int     							=					[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,max_position_embeddings=1024    ,encoder_layers=args.encoder_layers    ,decoder_layers=args.decoder_layers    ,encoder_attention_heads=args.encoder_attention_heads    ,decoder_attention_heads=args.decoder_attention_heads    ,encoder_ffn_dim=args.encoder_ffn_embed_dim    ,decoder_ffn_dim=args.decoder_ffn_embed_dim    ,d_model=args.encoder_embed_dim    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

    UpperCAmelCase__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

