
"""simple docstring"""

from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Tuple				  =     ''''''
 UpperCAmelCase   :					List[str]				  =     '''hf-legacy'''  # "hf://"" is reserved for hffs

 def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[DatasetInfo] = None   ,							_UpperCAmelCase	:	Optional[str] = None   ,							**_UpperCAmelCase	:	List[str]   ,							):
       super().__init__(self   ,							**_UpperCAmelCase		)
       _A        =       repo_info
       _A        =       token
       _A        =       None

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       if self.dir_cache is None:
             _A        =       {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   _A        =       {
                       'name': hf_file.rfilename,
                       'size': None,
                       'type': 'file',
                   }
                   self.dir_cache.update(
                       {
                           str(_UpperCAmelCase		): {'name': str(_UpperCAmelCase		), 'size': None, 'type': 'directory'}
                           for d in list(PurePosixPath(hf_file.rfilename		).parents		)[:-1]
                       }		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str = "rb"   ,							**_UpperCAmelCase	:	int   ,							):
       if not isinstance(self.repo_info   ,							_UpperCAmelCase		):
             raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''		)
       _A        =       hf_hub_url(self.repo_info.id   ,							_UpperCAmelCase   ,							revision=self.repo_info.sha		)
       return fsspec.open(
           _UpperCAmelCase   ,							mode=_UpperCAmelCase   ,							headers=get_authentication_headers_for_url(_UpperCAmelCase   ,							use_auth_token=self.token		)   ,							client_kwargs={'trust_env': True}   ,							).open()

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Union[str, Any]   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       self._get_dirs()
       _A        =       self._strip_protocol(_UpperCAmelCase		)
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Union[str, Any]=False   ,							**_UpperCAmelCase	:	str		):
       self._get_dirs()
       _A        =       PurePosixPath(path.strip('/'		)		)
       _A        =       {}
       for p, f in self.dir_cache.items():
             _A        =       PurePosixPath(p.strip('/'		)		)
             _A        =       p.parent
             if root == path:
                   _A        =       f
       _A        =       list(paths.values()		)
       if detail:
             return out
       else:
             return sorted(f['name'] for f in out		)







"""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
       )

