





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE_						       =	HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE_						       =	parser.parse_args()

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

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE_						       =	{
    """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)
SCREAMING_SNAKE_CASE_						       =	AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE_						       =	AutoModelForCausalLM.from_config(config)

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









from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     ) - 1

      while i < j:
            if nums[i] + nums[j] == target:
                  return [i, j]
            elif nums[i] + nums[j] < target:
                  SCREAMING_SNAKE_CASE		=						i + 1
            else:
                  SCREAMING_SNAKE_CASE		=						j - 1

      return []


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(F'''{two_pointer([2, 7, 1_1, 1_5], 9) = }''')


