






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 collections.abc import Sequence



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Sequence[float]			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    return sum(c * (x**i) for i, c in enumerate(__lowercase    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Sequence[float]			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    __A         =	0.0
    for coeff in reversed(__lowercase    ):
        __A         =	result * x + coeff
    return result


if __name__ == "__main__":
  __a      :       Dict				  =   (0.0, 0.0, 5.0, 9.3, 7.0)
  __a      :       Optional[Any]				  =   1_0.0
  print(evaluate_poly(poly, x))
  print(horner(poly, x))




