



'''simple docstring'''
from __future__ import annotations






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Checks if the entire collection has been sorted
						if len(UpperCAmelCase_							) <= 1 or n <= 1:
												return

						insert_next(UpperCAmelCase_  ,  n - 1							)
						rec_insertion_sort(UpperCAmelCase_  ,  n - 1							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Checks order between adjacent elements
						if index >= len(UpperCAmelCase_							) or collection[index - 1] <= collection[index]:
												return

						# Swaps adjacent elements since they are not in ascending order
						_UpperCamelCase    ,      _UpperCamelCase :		str			    =       (
						    collection[index],
						    collection[index - 1],
						)

						insert_next(UpperCAmelCase_  ,  index + 1							)


if __name__ == "__main__":
					snake_case_			:     Dict  	=  input('Enter integers separated by spaces: ')
					snake_case_			:     list[int]  	=  [int(num) for num in numbers.split()]
					rec_insertion_sort(number_list, len(number_list))
					print(number_list)







'''simple docstring'''
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Initialise PyTorch model
						_UpperCamelCase :		Any			    =       LxmertConfig.from_json_file(UpperCAmelCase_							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase :		int			    =       LxmertForPreTraining(UpperCAmelCase_							)

						# Load weights from tf checkpoint
						load_tf_weights_in_lxmert(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

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


if __name__ == "__main__":
					snake_case_			:     int  	=  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.'
					)
					snake_case_			:     List[str]  	=  parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


