






import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__a      :       Optional[int]				  =   {
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Dict			,				__lowercase   :			List[str]			,				__lowercase   :			str=None    )						->      Optional[int]:





    """simple docstring"""

    __A         =	XLNetConfig.from_json_file(__lowercase    )

    __A         =	finetuning_task.lower() if finetuning_task is not None else """"""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(f"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"    )
        __A         =	finetuning_task
        __A         =	GLUE_TASKS_NUM_LABELS[finetuning_task]
        __A         =	XLNetForSequenceClassification(__lowercase    )
    elif "squad" in finetuning_task:
        __A         =	finetuning_task
        __A         =	XLNetForQuestionAnswering(__lowercase    )
    else:
        __A         =	XLNetLMHeadModel(__lowercase    )

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__lowercase			,				__lowercase			,				__lowercase    )

    # Save pytorch-model
    __A         =	os.path.join(__lowercase			,				__lowercase    )
    __A         =	os.path.join(__lowercase			,				__lowercase    )
    print(f"Save PyTorch model to {os.path.abspath(__lowercase    )}"    )
    torch.save(model.state_dict()			,				__lowercase    )
    print(f"Save configuration file to {os.path.abspath(__lowercase    )}"    )
    with open(__lowercase			,				"""w"""			,				encoding="""utf-8"""    ) as f:
        f.write(config.to_json_string()    )


if __name__ == "__main__":
  __a      :       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(
      "--xlnet_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained XLNet model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=True,
      help="Path to the folder to store the PyTorch model or dataset/vocab.",
  )
  parser.add_argument(
      "--finetuning_task",
      default=None,
      type=str,
      help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
  )
  __a      :       int				  =   parser.parse_args()
  print(args)

  convert_xlnet_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
  )












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




