code: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowerCamelCase							:     List[str]								=			{
      'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
      'tokenization_realm': ['RealmTokenizer'],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[Any]								=			['RealmTokenizerFast']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Dict								=			[
       'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RealmEmbedder',
       'RealmForOpenQA',
       'RealmKnowledgeAugEncoder',
       'RealmPreTrainedModel',
       'RealmReader',
       'RealmScorer',
       'load_tf_weights_in_realm',
   ]
   lowerCamelCase							:     str								=			['RealmRetriever']
  
  
  if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_realm import RealmTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_realm import (
        REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RealmEmbedder,
        RealmForOpenQA,
        RealmKnowledgeAugEncoder,
        RealmPreTrainedModel,
        RealmReader,
        RealmScorer,
        load_tf_weights_in_realm,
    )
    from .retrieval_realm import RealmRetriever
  
  
  else:
   import sys
  
   lowerCamelCase							:     int								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
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
   )
  
  
  
  
  