






"""simple docstring"""
from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 				snake_case      (   A__						):
			return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code						)







class 			UpperCamelCase_       (UpperCamelCase__     ):





	@staticmethod
	def      _SCREAMING_SNAKE_CASE      (      lowerCAmelCase_					: List[str]						)						->			Tuple:
				UpperCAmelCase_     : List[str]			     =					parser.add_parser("download"						)
				download_parser.add_argument(
				    "--cache-dir"	,	type=lowerCAmelCase__	,	default=lowerCAmelCase__	,	help="Path to location to store the models"						)
				download_parser.add_argument(
				    "--force"	,	action="store_true"	,	help="Force the model to be download even if already in cache-dir"						)
				download_parser.add_argument(
				    "--trust-remote-code"	,	action="store_true"	,	help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"	,	)
				download_parser.add_argument("model"	,	type=lowerCAmelCase__	,	help="Name of the model to download"						)
				download_parser.set_defaults(func=lowerCAmelCase__						)





	def __init__(      self					: Optional[Any]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: List[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Tuple			     =					model
				UpperCAmelCase_     : Optional[int]			     =					cache
				UpperCAmelCase_     : Any			     =					force
				UpperCAmelCase_     : Dict			     =					trust_remote_code





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Union[str, Any]:
				from ..models.auto import AutoModel, AutoTokenizer

				AutoModel.from_pretrained(
				    self._model	,	cache_dir=self._cache	,	force_download=self._force	,	trust_remote_code=self._trust_remote_code						)
				AutoTokenizer.from_pretrained(
				    self._model	,	cache_dir=self._cache	,	force_download=self._force	,	trust_remote_code=self._trust_remote_code						)










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	:	int		     =       {
    """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       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  a__	:					Optional[int]										=XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE						)

  a__	:					Dict										=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__	:					List[str]										=finetuning_task
    a__	:					Tuple										=GLUE_TASKS_NUM_LABELS[finetuning_task]
    a__	:					List[Any]										=XLNetForSequenceClassification(SCREAMING_SNAKE_CASE						)
  elif "squad" in finetuning_task:
    a__	:					Optional[int]										=finetuning_task
    a__	:					Dict										=XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					List[Any]										=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
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  a__	:					Dict										=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	:	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""",
     )
     UpperCAmelCase	:	int		     =       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
     )






