


'''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


__A		={
    '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 (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None   ):
			# Initialise PyTorch model
			UpperCAmelCase__					:   List[str]       				=       XLNetConfig.from_json_file(UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       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__					:   Optional[Any]       				=       finetuning_task
						UpperCAmelCase__					:   Any       				=       GLUE_TASKS_NUM_LABELS[finetuning_task]
						UpperCAmelCase__					:   Optional[int]       				=       XLNetForSequenceClassification(UpperCamelCase__   )
			elif "squad" in finetuning_task:
						UpperCAmelCase__					:   List[Any]       				=       finetuning_task
						UpperCAmelCase__					:   Tuple       				=       XLNetForQuestionAnswering(UpperCamelCase__   )
			else:
						UpperCAmelCase__					:   Any       				=       XLNetLMHeadModel(UpperCamelCase__   )

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

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


if __name__ == "__main__":
		__A		=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',
		)
		__A		=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'''



def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       len(UpperCamelCase__   )
			UpperCAmelCase__					:   List[Any]       				=       sum(UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       [[False for x in range(s + 1   )] for y in range(n + 1   )]

			for i in range(1	, n + 1   ):
						UpperCAmelCase__					:   int       				=       True

			for i in range(1	, s + 1   ):
						UpperCAmelCase__					:   Dict       				=       False

			for i in range(1	, n + 1   ):
						for j in range(1	, s + 1   ):
									UpperCAmelCase__					:   int       				=       dp[i][j - 1]

									if arr[i - 1] <= j:
												UpperCAmelCase__					:   str       				=       dp[i][j] or dp[i - 1][j - arr[i - 1]]

			for j in range(int(s / 2   )	, -1	, -1   ):
						if dp[n][j] is True:
									UpperCAmelCase__					:   int       				=       s - 2 * j
									break

			return diff