





import argparse

import torch

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


logging.set_verbosity_info()



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    # Initialise PyTorch model
    UpperCamelCase__:	Optional[Any]					=     LxmertConfig.from_json_file(A_)
    print(F"Building PyTorch model from configuration: {config}")
    UpperCamelCase__:	Union[str, Any]					=     LxmertForPreTraining(A_)

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

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


if __name__ == "__main__":
   A__:    Tuple  	=	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.'''
   )
   A__:    List[Any]  	=	parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








from __future__ import annotations

A__:    List[Any]  	=	10



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Tuple					=     1
    UpperCamelCase__:	str					=     max(A_)
    while placement <= max_digit:
        # declare and initialize empty buckets
        UpperCamelCase__:	list[list]					=     [[] for _ in range(A_)]
        # split list_of_ints between the buckets
        for i in list_of_ints:
            UpperCamelCase__:	Union[str, Any]					=     int((i / placement) % RADIX)
            buckets[tmp].append(A_)
        # put each buckets' contents into list_of_ints
        UpperCamelCase__:	Union[str, Any]					=     0
        for b in range(A_):
            for i in buckets[b]:
                UpperCamelCase__:	Any					=     i
                a += 1
        # move to next
        placement *= RADIX
    return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()

