code: 
  
  
  '''simple docstring'''
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if not inplace:
                lowercase__       =				ma + ma
         else:
                ma += ma
  
         return ma
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				-1 * len(A )
         lowercase__       =				list(range(len(tensor.shape[:zero_index] ) ) )
         return tensor.permute(first_inds + [zero_index + i for i in inds] )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return t.reshape(t.shape[:-no_dims] + (-1,) )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A = 1E-4 ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				mask.expand(*value.shape )
         return torch.sum(mask * value						,				dim=A ) / (eps + torch.sum(A						,				dim=A ))
  
  def _SCREAMING_SNAKE_CASE   (A						,				A = 2.3_125						,				A = 21.6_875						,				A = 64 ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				torch.linspace(A						,				A						,				no_bins - 1						,				device=pts.device )
         lowercase__       =				torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2						,				dim=-1 ) )
         return torch.bucketize(A						,				A )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				dicts[0]
         lowercase__       =				{}
         for k, v in first.items():
                lowercase__       =				[d[k] for d in dicts]
                if isinstance(A						,				A ):
                       lowercase__       =				dict_multimap(A						,				A )
                else:
                       lowercase__       =				fn(A )
  
         return new_dict
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				v_bins.view(((1,) * len(x.shape )) + (len(A ),) )
         lowercase__       =				x[..., None] - reshaped_bins
         lowercase__       =				torch.argmin(torch.abs(A )						,				dim=-1 )
         return nn.functional.one_hot(A						,				num_classes=len(A ) ).float()
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A = 0						,				A = 0 ) -> torch.Tensor:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				[]
         for i, s in enumerate(data.shape[:no_batch_dims] ):
                lowercase__       =				torch.arange(A )
                lowercase__       =				r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
                ranges.append(A )
  
         lowercase__       =				[slice(A ) for _ in range(len(data.shape ) - no_batch_dims )]
         lowercase__       =				inds
         ranges.extend(A )
         # Matt note: Editing this to get around the behaviour of using a list as an array index changing
         # in recent Numpy versions
         return data[tuple(A )]
  
  
  lowerCamelCase							:     Dict								=			TypeVar('T')
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Dict[Any, Union[dict, list, tuple, Any]]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				{}
         for k, v in dic.items():
                if isinstance(A						,				A ):
                       lowercase__       =				dict_map(A						,				A						,				A )
                else:
                       lowercase__       =				tree_map(A						,				A						,				A )
  
         return new_dict
  
  @overload
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Any:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         ...
  
  @overload
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         ...
  
  @overload
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> list:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         ...
  
  @overload
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         ...
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if isinstance(A						,				A ):
                return dict_map(A						,				A						,				A )
         elif isinstance(A						,				A ):
                return [tree_map(A						,				A						,				A ) for x in tree]
         elif isinstance(A						,				A ):
                return tuple(tree_map(A						,				A						,				A ) for x in tree )
         elif isinstance(A						,				A ):
                return fn(A )
         else:
                print(type(A ) )
                raise ValueError('''Not supported''' )
  
  lowerCamelCase							:     Any								=			partial(tree_map, leaf_type=torch.Tensor)
  
  
  
  
  
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
   )
  
  
  
  
  