





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


snake_case_							=			{
    '''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_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=None		)   ->     Optional[Any]:
		lowerCamelCase   : str								=XLNetConfig.from_json_file(a__		)

		lowerCamelCase   : Any								=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   : List[str]								=finetuning_task
				lowerCamelCase   : int								=GLUE_TASKS_NUM_LABELS[finetuning_task]
				lowerCamelCase   : List[str]								=XLNetForSequenceClassification(a__		)
		elif "squad" in finetuning_task:
				lowerCamelCase   : int								=finetuning_task
				lowerCamelCase   : Optional[Any]								=XLNetForQuestionAnswering(a__		)
		else:
				lowerCamelCase   : Optional[int]								=XLNetLMHeadModel(a__		)

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

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


if __name__ == "__main__":
				snake_case_							=			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''',
				)
				snake_case_							=			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 string


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Optional[Any]								=''''''
		for i in sequence:
				lowerCamelCase   : int								=ord(SCREAMING_SNAKE_CASE_		)
				if 6_5 <= extract <= 9_0:
						output += chr(1_5_5 - extract		)
				elif 9_7 <= extract <= 1_2_2:
						output += chr(2_1_9 - extract		)
				else:
						output += i
		return output


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Tuple								=string.ascii_letters
		lowerCamelCase   : int								=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(SCREAMING_SNAKE_CASE_		)] if c in letters else c for c in sequence		)


def 					A__	(       )   ->     None:
		from timeit import timeit

		print('''Running performance benchmarks...'''		)
		lowerCamelCase   : Tuple								='''from string import printable ; from __main__ import atbash, atbash_slow'''
		print(F"> atbash_slow(): {timeit('atbash_slow(printable)'							,       setup=SCREAMING_SNAKE_CASE_		)} seconds"		)
		print(F">      atbash(): {timeit('atbash(printable)'							,       setup=SCREAMING_SNAKE_CASE_		)} seconds"		)


if __name__ == "__main__":
				for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
								print(F"""{example} encrypted in atbash: {atbash(example)}""")
				benchmark()

