


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:     List[Any]								=			{"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase							:     List[str]								=			[
           "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SEWDForCTC",
           "SEWDForSequenceClassification",
           "SEWDModel",
           "SEWDPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase							:     Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''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
 )




