
def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     int(lowerCamelCase__       )
				if n_element < 1:
								lowerCamelCase_         =     ValueError("a should be a positive number"       )
								raise my_error

				lowerCamelCase_         =     [1]
				lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_         =     (0, 0, 0)
				lowerCamelCase_         =     1
				while index < n_element:
								while hamming_list[i] * 2 <= hamming_list[-1]:
												i += 1
								while hamming_list[j] * 3 <= hamming_list[-1]:
												j += 1
								while hamming_list[k] * 5 <= hamming_list[-1]:
												k += 1
								hamming_list.append(
								    min(hamming_list[i] * 2      ,					hamming_list[j] * 3      ,					hamming_list[k] * 5       )       )
								index += 1
				return hamming_list


if __name__ == "__main__":
			__A							=input('''Enter the last number (nth term) of the Hamming Number Series: ''')
			print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
			__A							=hamming(int(n))
			print('''-----------------------------------------------------''')
			print(F"""The list with nth numbers is: {hamming_numbers}""")
			print('''-----------------------------------------------------''')




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      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None       ):
				# Initialise PyTorch model
				lowerCamelCase_         =     XLNetConfig.from_json_file(lowerCamelCase__       )

				lowerCamelCase_         =     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_         =     finetuning_task
								lowerCamelCase_         =     GLUE_TASKS_NUM_LABELS[finetuning_task]
								lowerCamelCase_         =     XLNetForSequenceClassification(lowerCamelCase__       )
				elif "squad" in finetuning_task:
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     XLNetForQuestionAnswering(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     XLNetLMHeadModel(lowerCamelCase__       )

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

				# Save pytorch-model
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				print(F'Save PyTorch model to {os.path.abspath(lowerCamelCase__       )}'       )
				torch.save(model.state_dict()      ,					lowerCamelCase__       )
				print(F'Save configuration file to {os.path.abspath(lowerCamelCase__       )}'       )
				with open(lowerCamelCase__      ,					"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
			)


