


"""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


lowerCAmelCase__      				=   {
    '''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 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    Any=None				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							XLNetConfig.from_json_file(__UpperCamelCase				)

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

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

  # Save pytorch-model
  lowerCAmelCase     :     List[str]								=							os.path.join(__UpperCamelCase				,      __UpperCamelCase				)
  lowerCAmelCase     :     Any								=							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__      				=   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__      				=   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
     )






from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
