'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      list    )    ->    list:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    len(_UpperCAmelCase    )
       for _ in range(_UpperCAmelCase    ):
              for i in range(_ % 2			,	arr_size - 1			,	2    ):
                     if arr[i + 1] < arr[i]:
                            _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  list(range(10, 0, -1))
  print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')






import argparse

import torch

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


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				LxmertConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)
    __a						       =				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						:List[Any]      		=     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						:Optional[Any]      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





