code: import argparse
  
  import torch
  
  from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Any:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     RoFormerConfig.from_json_file(lowerCamelCase__					)
    print(f"Building PyTorch model from configuration: {config}"					)
    a__     		=     RoFormerForMaskedLM(lowerCamelCase__					)
  
    # Load weights from tf checkpoint
    load_tf_weights_in_roformer(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)
  
    # Save pytorch-model
    print(f"Save PyTorch model to {pytorch_dump_path}"					)
    torch.save(model.state_dict() ,				lowerCamelCase__ ,				_use_new_zipfile_serialization=lowerCamelCase__					)
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--bert_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       _SCREAMING_SNAKE_CASE   						=						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  