




"""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


lowercase__     						=						{
    """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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__=None		):
				# Initialise PyTorch model
				_lowerCamelCase     :			Dict    =    XLNetConfig.from_json_file(lowercase__		)

				_lowerCamelCase     :			int    =    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}'''		)
								_lowerCamelCase     :			int    =    finetuning_task
								_lowerCamelCase     :			str    =    GLUE_TASKS_NUM_LABELS[finetuning_task]
								_lowerCamelCase     :			Dict    =    XLNetForSequenceClassification(lowercase__		)
				elif "squad" in finetuning_task:
								_lowerCamelCase     :			Any    =    finetuning_task
								_lowerCamelCase     :			Dict    =    XLNetForQuestionAnswering(lowercase__		)
				else:
								_lowerCamelCase     :			Tuple    =    XLNetLMHeadModel(lowercase__		)

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

				# Save pytorch-model
				_lowerCamelCase     :			Tuple    =    os.path.join(lowercase__							,    lowercase__		)
				_lowerCamelCase     :			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__":
			lowercase__     						=						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""",
			)
			lowercase__     						=						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 pathlib import Path

import fire






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			dest_dir.mkdir(exist_ok=__lowerCAmelCase				)
			for path in src_dir.iterdir():
						lowerCAmelCase_						=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
						lowerCAmelCase_						=	dest_dir.joinpath(path.name				)
						print(__lowerCAmelCase				)
						dest_path.open("w"				).write("\n".join(__lowerCAmelCase				)				)


if __name__ == "__main__":
			fire.Fire(minify)



