


from __future__ import annotations


def        _UpperCamelCase							(      lowercase__      ):  # This function is recursive
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		len(lowercase__      )
						# If the array contains only one element, we return it (it's the stop condition of
						# recursion)
						if array_length <= 1:
												return array
												# Else
						__SCREAMING_SNAKE_CASE						:     Dict					=		array[0]
						__SCREAMING_SNAKE_CASE						:     Tuple					=		False
						__SCREAMING_SNAKE_CASE						:     List[str]					=		1
						__SCREAMING_SNAKE_CASE						:     list[int]					=		[]
						while not is_found and i < array_length:
												if array[i] < pivot:
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		True
																		__SCREAMING_SNAKE_CASE						:     Dict					=		[element for element in array[i:] if element >= array[i]]
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		longest_subsequence(lowercase__      )
																		if len(lowercase__      ) > len(lowercase__      ):
																								__SCREAMING_SNAKE_CASE						:     Optional[int]					=		temp_array
												else:
																		i += 1

						__SCREAMING_SNAKE_CASE						:     Dict					=		[element for element in array[1:] if element >= pivot]
						__SCREAMING_SNAKE_CASE						:     Tuple					=		[pivot, *longest_subsequence(lowercase__      )]
						if len(lowercase__      ) > len(lowercase__      ):
												return temp_array
						else:
												return longest_subseq


if __name__ == "__main__":
						import doctest

						doctest.testmod()





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)





