





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # Initialise PyTorch model
   a__						:    List[Any]   		=	BertConfig.from_json_file(lowerCamelCase )
   print(F"""Building PyTorch model from configuration: {config}""" )
   a__						:    int   		=	BertForPreTraining(lowerCamelCase )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert(lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      int         =    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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      Tuple         =    [
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

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

