



import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    Optional[int]				,   __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    List[Any]	)  ->						Optional[int]:
					# Initialise PyTorch model
					__lowerCAmelCase	   =FunnelConfig.from_json_file(__lowerCamelCase	)
					print(f"""Building PyTorch model from configuration: {config}"""	)
					__lowerCAmelCase	   =FunnelBaseModel(__lowerCamelCase	) if base_model else FunnelModel(__lowerCamelCase	)

					# Load weights from tf checkpoint
					load_tf_weights_in_funnel(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
					torch.save(model.state_dict()				,   __lowerCamelCase	)


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(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
				)
				lowercase_	       =			parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					if len(__lowerCamelCase	) <= 1:
										return lst

					__lowerCAmelCase	   =1

					while i < len(__lowerCamelCase	):
										if lst[i - 1] <= lst[i]:
															i += 1
										else:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =lst[i], lst[i - 1]
															i -= 1
															if i == 0:
																				__lowerCAmelCase	   =1

					return lst


if __name__ == "__main__":
				lowercase_	       =			input('''Enter numbers separated by a comma:\n''').strip()
				lowercase_	       =			[int(item) for item in user_input.split(''',''')]
				print(gnome_sort(unsorted))




