

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


lowerCAmelCase__	   = {
    """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__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							str=None      )			->      str:




				'''simple docstring'''





				A__        =			XLNetConfig.from_json_file(snake_case_      )

				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(snake_case_      )
				elif "squad" in finetuning_task:
								A__        =			finetuning_task
								A__        =			XLNetForQuestionAnswering(snake_case_      )
				else:
								A__        =			XLNetLMHeadModel(snake_case_      )

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

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


if __name__ == "__main__":
	lowerCAmelCase__	   = 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""",
	)
	lowerCAmelCase__	   = 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
	)




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      *lowercase      ,      **lowercase       )  ->						None:
							'''simple docstring'''






							warnings.warn(
							    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use CLIPImageProcessor instead."      ,      lowercase      ,      )
							super().__init__(*lowercase      ,      **lowercase       )

