"""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


_a      :		str           =   {
    '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     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       str=None	)       ->      List[str]:
  # Initialise PyTorch model
  _lowerCAmelCase						:       str  			=     XLNetConfig.from_json_file(_lowerCamelCase	)

  _lowerCAmelCase						:       int  			=     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}"	)
    _lowerCAmelCase						:       str  			=     finetuning_task
    _lowerCAmelCase						:       Optional[int]  			=     GLUE_TASKS_NUM_LABELS[finetuning_task]
    _lowerCAmelCase						:       Any  			=     XLNetForSequenceClassification(_lowerCamelCase	)
  elif "squad" in finetuning_task:
    _lowerCAmelCase						:       Optional[int]  			=     finetuning_task
    _lowerCAmelCase						:       List[str]  			=     XLNetForQuestionAnswering(_lowerCamelCase	)
  else:
    _lowerCAmelCase						:       List[str]  			=     XLNetLMHeadModel(_lowerCamelCase	)

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

  # Save pytorch-model
  _lowerCAmelCase						:       str  			=     os.path.join(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       Any  			=     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__":
     _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      :		List[Any]           =   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__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> str:
								__a										=    []
								__a										=    min(len(_stra  )						,    len(_stra  )  ) // 2
								for i, l in enumerate(_stra  ):
												__a										=    int(max(0						,    i - limit  )  )
												__a										=    int(min(i + limit + 1						,    len(_stra  )  )  )
												if l in _stra[left:right]:
																matched.append(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

				# transposition
				__a										=    (
				    len([(ca, ca) for ca, ca in zip(a__						,    a__  ) if ca != ca]  ) // 2
				)

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				__a										=    0
				for ca, ca in zip(stra[:4]						,    stra[:4]  ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(jaro_winkler('hello', 'world'))