






"""simple docstring"""
def 				snake_case      (   A__						):
			return credit_card_number.startswith(("34", "35", "37", "4", "5", "6")						)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Tuple			     =					credit_card_number
			UpperCAmelCase_     : Union[str, Any]			     =					0
			UpperCAmelCase_     : List[str]			     =					len(A__						) - 2
			for i in range(A__	,-1	,-2						):
						#  double the value of every second digit
						UpperCAmelCase_     : List[Any]			     =					int(cc_number[i]						)
						digit *= 2
						# If doubling of a number results in a two digit number
						# i.e greater than 9(e.g., 6 × 2 = 12),
						# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
						# to get a single digit number.
						if digit > 9:
									digit %= 10
									digit += 1
						UpperCAmelCase_     : List[str]			     =					cc_number[:i] + str(A__						) + cc_number[i + 1 :]
						total += digit

			# Sum up the remaining digits
			for i in range(len(A__						) - 1	,-1	,-2						):
						total += int(cc_number[i]						)

			return total % 10 == 0

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[Any]			     =					F"""{credit_card_number} is an invalid credit card number because"""
			if not credit_card_number.isdigit():
						print(F"""{error_message} it has nonnumerical characters."""						)
						return False

			if not 13 <= len(A__						) <= 16:
						print(F"""{error_message} of its length."""						)
						return False

			if not validate_initial_digits(A__						):
						print(F"""{error_message} of its first two digits."""						)
						return False

			if not luhn_validation(A__						):
						print(F"""{error_message} it fails the Luhn check."""						)
						return False

			print(F"""{credit_card_number} is a valid credit card number."""						)
			return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			validate_credit_card_number('''4111111111111111''')
			validate_credit_card_number('''32323''')










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
     )






