





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    :					List[Any] =      {
    '''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    __lowerCamelCase					(							__a   :Union[str, Any]    ,     __a   :Tuple    ,     __a   :str    ,     __a   :Any=None    )		->				Dict:






		"""simple docstring"""

		A__        =   XLNetConfig.from_json_file(__a    )

		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(__a    )
		elif "squad" in finetuning_task:
				A__        =   finetuning_task
				A__        =   XLNetForQuestionAnswering(__a    )
		else:
				A__        =   XLNetLMHeadModel(__a    )

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

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


if __name__ == "__main__":
					A    :					Optional[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(
					    '''--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    :					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
					)










def    __lowerCamelCase					(							__a   :str    )		->				list:






		"""simple docstring"""

		A__        =   [0] * len(__a    )

		for i in range(1    ,     len(__a    )    ):
				# use last results for better performance - dynamic programming
				A__        =   prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						A__        =   prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				A__        =   j

		return prefix_result







def    __lowerCamelCase					(							__a   :str    )		->				int:






		"""simple docstring"""

		return max(prefix_function(__a    )    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



