



'''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


lowercase_		       =  {
    "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  ,	__A  ,	__A  ,	__A=None):



     """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__":
  lowercase_		       =  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",
  )
  lowercase_		       =  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
  )








'''simple docstring'''





from __future__ import annotations

from typing import TypedDict






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str
 __lowerCamelCase    :       int





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter s type must be str.''')

     return [s[i:] + s[:i] for i in range(len(__A))]




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter s type must be str.''')
     if not s:
          raise ValueError('''The parameter s must not be empty.''')

     _a      =  all_rotations(__A)
     rotations.sort()  # sort the list of rotations in alphabetically order
     # make a string composed of the last char of each rotation
     _a      =  {
         "bwt_string": "".join([word[-1] for word in rotations]),
         "idx_original_string": rotations.index(__A),
     }
     return response




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter bwt_string type must be str.''')
     if not bwt_string:
          raise ValueError('''The parameter bwt_string must not be empty.''')
     try:
          _a      =  int(__A)
     except ValueError:
          raise TypeError(
              '''The parameter idx_original_string type must be int or passive'''
              ''' of cast to int.''')
     if idx_original_string < 0:
          raise ValueError('''The parameter idx_original_string must not be lower than 0.''')
     if idx_original_string >= len(__A):
          raise ValueError(
              '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).''')

     _a      =  [''''''] * len(__A)
     for _ in range(len(__A)):
          for i in range(len(__A)):
               _a      =  bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
  lowercase_		       =  "Provide a string that I will generate its BWT transform: "
  lowercase_		       =  input(entry_msg).strip()
  lowercase_		       =  bwt_transform(s)
  print(
      F"""Burrows Wheeler transform for string '{s}' results """
      F"""in '{result['bwt_string']}'"""
  )
  lowercase_		       =  reverse_bwt(result["bwt_string"], result["idx_original_string"])
  print(
      F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
      F"""we get original string '{original_string}'"""
  )



