



"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__		:    Any				   =     HfArgumentParser(InitializationArguments)
lowercase__		:    Dict				   =     parser.parse_args()

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

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__		:    Optional[int]				   =     {
    """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)
lowercase__		:    List[str]				   =     AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__		:    Dict				   =     AutoModelForCausalLM.from_config(config)

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










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    list[int]   ,       lowerCAmelCase__				:    list[int]						)						->       None:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			len(lowerCAmelCase__						)
  print('The following activities are selected:'						)

  # The first activity is always selected
  lowerCAmelCase_       :    str								=			0
  print(lowerCAmelCase__   ,       end=','						)

  # Consider rest of the activities
  for j in range(lowerCAmelCase__						):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(lowerCAmelCase__   ,       end=','						)
      lowerCAmelCase_       :    Tuple								=			j


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowercase__		:    List[str]				   =     [1, 3, 0, 5, 8, 5]
     lowercase__		:    Dict				   =     [2, 4, 6, 7, 9, 9]
     print_max_activities(start, finish)





