




"""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  :			Dict        =       {
    '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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=None	):




	"""simple docstring"""






	A_  :				str	   =		XLNetConfig.from_json_file(_UpperCAmelCase	)

	A_  :				Optional[int]	   =		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_  :				Dict	   =		finetuning_task
		A_  :				int	   =		GLUE_TASKS_NUM_LABELS[finetuning_task]
		A_  :				Tuple	   =		XLNetForSequenceClassification(_UpperCAmelCase	)
	elif "squad" in finetuning_task:
		A_  :				str	   =		finetuning_task
		A_  :				Dict	   =		XLNetForQuestionAnswering(_UpperCAmelCase	)
	else:
		A_  :				List[str]	   =		XLNetLMHeadModel(_UpperCAmelCase	)

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

	# Save pytorch-model
	A_  :				Union[str, Any]	   =		os.path.join(_UpperCAmelCase  ,      _UpperCAmelCase	)
	A_  :				Optional[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  :			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  :			str        =       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 collections import namedtuple

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		namedtuple('''result'''  ,      '''name value'''	)
	if (voltage, current, power).count(0	) != 1:
		raise ValueError('''Only one argument must be 0'''	)
	elif power < 0:
		raise ValueError(
		    '''Power cannot be negative in any electrical/electronics system'''	)
	elif voltage == 0:
		return result('''voltage'''  ,      power / current	)
	elif current == 0:
		return result('''current'''  ,      power / voltage	)
	elif power == 0:
		return result('''power'''  ,      float(round(abs(voltage * current	)  ,      2	)	)	)
	else:
		raise ValueError('''Exactly one argument must be 0'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





