
'''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 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				int			,       _a  :				List[str]			,       _a  :				Optional[int]      ):
      # Initialise PyTorch model
      snake_case_    							=				ElectraConfig.from_json_file(SCREAMING_SNAKE_CASE__      )
      print(F'''Building PyTorch model from configuration: {config}'''      )

      if discriminator_or_generator == "discriminator":
            snake_case_    							=				ElectraForPreTraining(SCREAMING_SNAKE_CASE__      )
      elif discriminator_or_generator == "generator":
            snake_case_    							=				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__":
     A							     = 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'."
         ),
     )
     A							     = 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__)






