






"""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        A	(							__snake_case:   Any   ,    __snake_case:   int   ,    __snake_case:   Dict   ,    __snake_case:   Dict     )						->		Optional[Any]:

       """simple docstring"""





       __magic_name__										=						ElectraConfig.from_json_file(__snake_case     )
       print(F"""Building PyTorch model from configuration: {config}"""     )

       if discriminator_or_generator == "discriminator":
              __magic_name__										=						ElectraForPreTraining(__snake_case     )
       elif discriminator_or_generator == "generator":
              __magic_name__										=						ElectraForMaskedLM(__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(
           __snake_case   ,    __snake_case   ,    __snake_case   ,    discriminator_or_generator=__snake_case     )

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


if __name__ == "__main__":
 snake_case   :   List[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'."""
     ),
 )
 snake_case   :   Union[str, Any]    		=		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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case   :   Optional[int]    		=		{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		[
     """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SplinterForQuestionAnswering""",
     """SplinterForPreTraining""",
     """SplinterLayer""",
     """SplinterModel""",
     """SplinterPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
 from .tokenization_splinter import SplinterTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_splinter_fast import SplinterTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_splinter import (
      SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SplinterForPreTraining,
      SplinterForQuestionAnswering,
      SplinterLayer,
      SplinterModel,
      SplinterPreTrainedModel,
  )


else:
 import sys

 snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)