






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	  =						{
    "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      a_ (      _A      ,   _A      ,   _A      ,   _A=None  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     XLNetConfig.from_json_file(_SCREAMING_SNAKE_CASE  )

				snake_case__									=     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}'''  )
								snake_case__									=     finetuning_task
								snake_case__									=     GLUE_TASKS_NUM_LABELS[finetuning_task]
								snake_case__									=     XLNetForSequenceClassification(_SCREAMING_SNAKE_CASE  )
				elif "squad" in finetuning_task:
								snake_case__									=     finetuning_task
								snake_case__									=     XLNetForQuestionAnswering(_SCREAMING_SNAKE_CASE  )
				else:
								snake_case__									=     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
				snake_case__									=     os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE  )
				snake_case__									=     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	  =						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	  =						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
							)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase					:		str	  =						{"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""LayoutXLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
							from .processing_layoutxlm import LayoutXLMProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutxlm import LayoutXLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




