

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Any:





    '''simple docstring'''






    A__   				=       MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
    print(f'Building PyTorch model from configuration: {config}'				)
    A__   				=       MobileBertForPreTraining(SCREAMING_SNAKE_CASE__				)
    # Load weights from tf checkpoint
    A__   				=       load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    torch.save(model.state_dict()   ,      SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
     lowercase_			=  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(
         "--mobilebert_config_file",
         default=None,
         type=str,
         required=True,
         help=(
             "The config json file corresponding to the pre-trained MobileBERT 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."
     )
     lowercase_			=  parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




from collections.abc import Sequence
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Sequence[int] | None = None				)							->				int:





    '''simple docstring'''






    if nums is None or not nums:
        raise ValueError('Input sequence should not be empty'				)

    A__   				=       nums[0]
    for i in range(1   ,      len(SCREAMING_SNAKE_CASE__				)				):
        A__   				=       nums[i]
        A__   				=       max(SCREAMING_SNAKE_CASE__   ,      ans + num   ,      SCREAMING_SNAKE_CASE__				)

    return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     lowercase_			=  int(input("Enter number of elements : ").strip())
     lowercase_			=  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
     print(max_subsequence_sum(array))

