





"""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		:					Union[str, 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		:       int       ,			_lowercase		:       List[str]       ,			_lowercase		:       Tuple       ,			_lowercase		:       Any=None   )					->Optional[int]:






	'''simple docstring'''






	a :						str      =							XLNetConfig.from_json_file(_lowercase   )

	a :						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 :						Union[str, Any]      =							finetuning_task
		a :						Any      =							GLUE_TASKS_NUM_LABELS[finetuning_task]
		a :						str      =							XLNetForSequenceClassification(_lowercase   )
	elif "squad" in finetuning_task:
		a :						str      =							finetuning_task
		a :						Optional[Any]      =							XLNetForQuestionAnswering(_lowercase   )
	else:
		a :						Tuple      =							XLNetLMHeadModel(_lowercase   )

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

	# Save pytorch-model
	a :						Union[str, Any]      =							os.path.join(_lowercase       ,			_lowercase   )
	a :						List[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		:					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''',
		)
		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"""




from numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       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()


