






'''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


_UpperCamelCase						:						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 __UpperCAmelCase   (					A					:			int			,       A					:			Optional[Any]			,       A					:			Optional[Any]			,       A					:			List[Any]=None		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[Any]          =       XLNetConfig.from_json_file(_SCREAMING_SNAKE_CASE		)

			UpperCAmelCase_      :       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}"		)
						UpperCAmelCase_      :       int          =       finetuning_task
						UpperCAmelCase_      :       Union[str, Any]          =       GLUE_TASKS_NUM_LABELS[finetuning_task]
						UpperCAmelCase_      :       List[str]          =       XLNetForSequenceClassification(_SCREAMING_SNAKE_CASE		)
			elif "squad" in finetuning_task:
						UpperCAmelCase_      :       Union[str, Any]          =       finetuning_task
						UpperCAmelCase_      :       List[str]          =       XLNetForQuestionAnswering(_SCREAMING_SNAKE_CASE		)
			else:
						UpperCAmelCase_      :       Optional[int]          =       XLNetLMHeadModel(_SCREAMING_SNAKE_CASE		)

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

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


if __name__ == "__main__":
						_UpperCamelCase						:						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',
						)
						_UpperCamelCase						:						Union[str, 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase__  			=				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



