from __future__ import annotations






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

       _A        =  [element for element in array[1:] if element >= pivot]
       _A        =  [pivot, *longest_subsequence(snake_case__)]
       if len(snake_case__) > len(snake_case__):
              return temp_array
       else:
              return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(snake_case__   ,	snake_case__   ,	snake_case__)

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


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						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(
        '--albert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained ALBERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)




