





"""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		:				List[Any]  			=    {
    '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	(			_lowercase		:					Union[str, Any]					, _lowercase		:					Any					, _lowercase		:					str					, _lowercase		:					List[Any]=None      )				->Union[str, Any]:






 '''simple docstring'''






 a   :	Optional[int]			=       XLNetConfig.from_json_file(_lowercase      )

 a   :	List[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}"""      )
  a   :	int			=       finetuning_task
  a   :	Dict			=       GLUE_TASKS_NUM_LABELS[finetuning_task]
  a   :	Union[str, Any]			=       XLNetForSequenceClassification(_lowercase      )
 elif "squad" in finetuning_task:
  a   :	int			=       finetuning_task
  a   :	Union[str, Any]			=       XLNetForQuestionAnswering(_lowercase      )
 else:
  a   :	Dict			=       XLNetLMHeadModel(_lowercase      )

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

 # Save pytorch-model
 a   :	str			=       os.path.join(_lowercase					, _lowercase      )
 a   :	Union[str, Any]			=       os.path.join(_lowercase					, _lowercase      )
 print(F"""Save PyTorch model to {os.path.abspath(_lowercase      )}"""      )
 torch.save(model.state_dict()					, _lowercase      )
 print(F"""Save configuration file to {os.path.abspath(_lowercase      )}"""      )
 with open(_lowercase					, "w"					, encoding="utf-8"      ) as f:
  f.write(config.to_json_string()      )


if __name__ == "__main__":
  a		:				List[str]  			=    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		:				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
  )









"""simple docstring"""




a		:				str  			=    8.314_4598






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float      )				->float:






	'''simple docstring'''






	if temperature < 0:
		raise Exception("Temperature cannot be less than 0 K"      )
	if molar_mass <= 0:
		raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"      )
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		a		:				Any  			=    300
		a		:				Dict  			=    28
		a		:				Dict  			=    rms_speed_of_molecule(temperature, molar_mass)
		print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


