





import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   # Initialise PyTorch model
   a__	:						Union[str, Any]      		=  LxmertConfig.from_json_file(lowerCamelCase   )
   print(F"""Building PyTorch model from configuration: {config}"""   )
   a__	:						Optional[int]      		=  LxmertForPreTraining(lowerCamelCase   )

   # Load weights from tf checkpoint
   load_tf_weights_in_lxmert(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__			      =						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."""
  )
  SCREAMING_SNAKE_CASE__			      =						parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

