





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      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Union[str, Any]:
  # Initialise PyTorch model
  __snake_case:		Optional[Any]					    =		ElectraConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  print(F'''Building PyTorch model from configuration: {config}''')

  if discriminator_or_generator == "discriminator":
    __snake_case:		int					    =		ElectraForPreTraining(SCREAMING_SNAKE_CASE__)
  elif discriminator_or_generator == "generator":
    __snake_case:		int					    =		ElectraForMaskedLM(SCREAMING_SNAKE_CASE__)
  else:
    raise ValueError("""The discriminator_or_generator argument should be either 'discriminator' or 'generator'""")

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

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


if __name__ == "__main__":
      __UpperCAmelCase			:  List[str]										=				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			:  Optional[Any]										=				parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
      )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase			:  Optional[Any]										=				{"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  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

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






