
"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

a         =      TypeVar('''T''')





class     lowercase_	(					Generic[T]							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	T		):
       _A        =       data
       _A        =       None



 def __str__(			self	:	Optional[Any]		):
       return F'''{self.data}'''





class     lowercase_	(					Generic[T]							):



 '''simple docstring'''

 def __init__(			self	:	Optional[int]		):
       _A        =       None

 def __iter__(			self	:	Any		):
       _A        =       self.top
       while node:
             yield node.data
             _A        =       node.next

 def __str__(			self	:	Optional[int]		):
       return "->".join([str(_UpperCAmelCase		) for item in self]		)

 def __len__(			self	:	int		):
       return len(tuple(iter(self		)		)		)

 def       lowerCAmelCase_							(			self	:	Any		):
       return self.top is None

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	T		):
       _A        =       Node(_UpperCAmelCase		)
       if not self.is_empty():
             _A        =       self.top
       _A        =       node

 def       lowerCAmelCase_							(			self	:	List[str]		):
       if self.is_empty():
             raise IndexError('pop from empty stack'		)
       assert isinstance(self.top   ,							_UpperCAmelCase		)
       _A        =       self.top
       _A        =       self.top.next
       return pop_node.data

 def       lowerCAmelCase_							(			self	:	List[str]		):
       if self.is_empty():
             raise IndexError('peek from empty stack'		)

       assert self.top is not None
       return self.top.data



 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       None


if __name__ == "__main__":
       from doctest import testmod

       testmod()







"""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         =      {
    '''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 						_snake_case					( _snake_case							: str    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       XLNetConfig.from_json_file(_snake_case							)

      _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(_snake_case							)
      elif "squad" in finetuning_task:
            _A        =       finetuning_task
            _A        =       XLNetForQuestionAnswering(_snake_case							)
      else:
            _A        =       XLNetLMHeadModel(_snake_case							)

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

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


if __name__ == "__main__":
       a         =      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         =      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
       )

