





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   __lowerCamelCase   (				__a  :Tuple       ,	__a  :int       ,	__a  :Optional[int]       ,	__a  :List[Any]=None       )						->							Union[str, Any]:






		"""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__":
					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       :   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   __lowerCamelCase   (				__a  :int = 4_0_0_0_0_0_0       )						->							int:






		"""simple docstring"""

		A__     =  []
		A__ ,     A__     =  0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(__a       )
				A__ ,     A__     =  b, a + b
		return sum(__a       )


if __name__ == "__main__":
					print(F'''{solution() = }''')



