code: 
  
  
  '''simple docstring'''
  
  import numpy as np
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Any:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				abs(predict - actual )
         lowercase__       =				difference.mean()
  
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				predict - actual
         lowercase__       =				np.square(A )
  
         lowercase__       =				square_diff.mean()
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				predict - actual
         lowercase__       =				np.square(A )
         lowercase__       =				square_diff.mean()
         lowercase__       =				np.sqrt(A )
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				np.log(predict + 1 )
         lowercase__       =				np.log(actual + 1 )
  
         lowercase__       =				log_predict - log_actual
         lowercase__       =				np.square(A )
         lowercase__       =				square_diff.mean()
  
         lowercase__       =				np.sqrt(A )
  
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				np.array(A )
         lowercase__       =				np.array(A )
  
         lowercase__       =				predict - actual
         lowercase__       =				np.sum(A ) / len(A )
         lowercase__       =				np.sum(A ) / len(A )
         # print(numerator, denumerator)
         lowercase__       =				float(A ) / denumerator * 100
  
         return score
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return np.mean(np.array(A ) == np.array(A ) )
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A ) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				ElectraConfig.from_json_file(A )
         print(f"Building PyTorch model from configuration: {config}" )
  
         if discriminator_or_generator == "discriminator":
                lowercase__       =				ElectraForPreTraining(A )
         elif discriminator_or_generator == "generator":
                lowercase__       =				ElectraForMaskedLM(A )
         else:
                raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )
  
         # Load weights from tf checkpoint
         load_tf_weights_in_electra(
             A						,				A						,				A						,				discriminator_or_generator=A )
  
         # Save pytorch-model
         print(f"Save PyTorch model to {pytorch_dump_path}" )
         torch.save(model.state_dict()						,				A )
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     Tuple								=			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(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--discriminator_or_generator',
       default=None,
       type=str,
       required=True,
       help=(
           'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
           '\'generator\'.'
       ),
   )
   lowerCamelCase							:     Optional[int]								=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
   )
  
  
  
  
  