"""simple docstring"""






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


__lowerCAmelCase    :       Dict					     ={
    """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 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :List[str]					,		lowerCAmelCase__     :str=None		) ->    Any:
      '''simple docstring'''


      lowercase														=	XLNetConfig.from_json_file(lowerCAmelCase__		)

      lowercase														=	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}'		)
            lowercase														=	finetuning_task
            lowercase														=	GLUE_TASKS_NUM_LABELS[finetuning_task]
            lowercase														=	XLNetForSequenceClassification(lowerCAmelCase__		)
      elif "squad" in finetuning_task:
            lowercase														=	finetuning_task
            lowercase														=	XLNetForQuestionAnswering(lowerCAmelCase__		)
      else:
            lowercase														=	XLNetLMHeadModel(lowerCAmelCase__		)

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

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


if __name__ == "__main__":
       __lowerCAmelCase    :       Union[str, 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""",
       )
       __lowerCAmelCase    :       List[str]					     =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
       )




"""simple docstring"""






from __future__ import annotations


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    int:
      '''simple docstring'''


      if not nums:
            return 0
      lowercase														=	nums[0]
      lowercase														=	0
      for num in nums[1:]:
            lowercase							,							lowercase														=	(
                max_excluding + num,
                max(lowerCAmelCase__					,		lowerCAmelCase__		),
            )
      return max(lowerCAmelCase__					,		lowerCAmelCase__		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



