
"""simple docstring"""



import random


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   a[left_index]
    _UpperCAmelCase          =   left_index + 1
    for j in range(left_index + 1					,      _A      ):
        if a[j] < pivot:
            _UpperCAmelCase    ,_UpperCAmelCase          =   a[i], a[j]
            i += 1
    _UpperCAmelCase    ,_UpperCAmelCase          =   a[i - 1], a[left_index]
    return i - 1


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    if left < right:
        _UpperCAmelCase          =   random.randint(_A					,      right - 1      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        _UpperCAmelCase          =   partition(_A					,      _A					,      _A      )
        quick_sort_random(
            _A					,      _A					,      _A      )  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            _A					,      pivot_index + 1					,      _A      )  # recursive quicksort to the right of the pivot point


def  _UpperCamelCase	(						)    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   input("""Enter numbers separated by a comma:\n"""      ).strip()
    _UpperCAmelCase          =   [int(_A      ) for item in user_input.split(""","""      )]

    quick_sort_random(_A					,      0					,      len(_A      )      )

    print(_A      )


if __name__ == "__main__":
 main()

"""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  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   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					:	Dict							=		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					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)