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


__lowerCamelCase    :				List[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						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: List[Any]=None			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   XLNetConfig.from_json_file(__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   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}"""			)
    SCREAMING_SNAKE_CASE__    							=   finetuning_task
    SCREAMING_SNAKE_CASE__    							=   GLUE_TASKS_NUM_LABELS[finetuning_task]
    SCREAMING_SNAKE_CASE__    							=   XLNetForSequenceClassification(__UpperCamelCase			)
  elif "squad" in finetuning_task:
    SCREAMING_SNAKE_CASE__    							=   finetuning_task
    SCREAMING_SNAKE_CASE__    							=   XLNetForQuestionAnswering(__UpperCamelCase			)
  else:
    SCREAMING_SNAKE_CASE__    							=   XLNetLMHeadModel(__UpperCamelCase			)

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

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


if __name__ == "__main__":
   __lowerCamelCase    :				Union[str, Any]							     =	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''',
   )
   __lowerCamelCase    :				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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


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

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
