

from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
a_     :    int     		=   HfArgumentParser(InitializationArguments)
a_     :    Tuple     		=   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
a_     :    Any     		=   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
a_     :    Tuple     		=   {
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
a_     :    List[Any]     		=   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
a_     :    List[str]     		=   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)




from collections.abc import Sequence


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Sequence[int] | None = None						)		->				int:
 '''simple docstring'''


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

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

 return ans


if __name__ == "__main__":
      import doctest

      doctest.testmod()

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

