



"""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   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			Optional[int]:






      '''simple docstring'''


      lowercase  :      Tuple					=   ElectraConfig.from_json_file(_UpperCAmelCase				)
      print(f'''Building PyTorch model from configuration: {config}'''				)

      if discriminator_or_generator == "discriminator":
            lowercase  :      Optional[int]					=   ElectraForPreTraining(_UpperCAmelCase				)
      elif discriminator_or_generator == "generator":
            lowercase  :      Optional[int]					=   ElectraForMaskedLM(_UpperCAmelCase				)
      else:
            raise ValueError('The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''				)

      # Load weights from tf checkpoint
      load_tf_weights_in_electra(
          _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      discriminator_or_generator=_UpperCAmelCase				)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
      torch.save(model.state_dict()	,      _UpperCAmelCase				)


if __name__ == "__main__":
     _UpperCamelCase:				Union[str, Any]    							=  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\'.'
         ),
     )
     _UpperCamelCase:				List[str]    							=  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
     )





"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:				int    							=  TypeVar('T', bound='TaskTemplate')


@dataclass(frozen=SCREAMING_SNAKE_CASE__     )
class   a__		(						abc.ABC     ):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42


   def        lowercase				(							self      :							T,     lowerCAmelCase      :							Features			)    ->       T:
         return copy.deepcopy(self			)


   @property
   def        lowercase				(							self      :							Union[str, Any]			)    ->       Features:
         return Features(**self.input_schema,     **self.label_schema			)


   @property
   @abc.abstractmethod
   def        lowercase				(							self      :							Tuple			)    ->       Dict[str, str]:
         raise NotImplementedError






   @classmethod
   def        lowercase				(							cls      :							Type[T],     lowerCAmelCase      :							dict			)    ->       T:
         lowercase  :      Union[str, Any]					=   {f.name for f in dataclasses.fields(cls			)}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}			)
