





'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			int      )					->					str:





     if not isinstance(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE      ):
          raise ValueError('iterations must be defined as integers'      )
     if not isinstance(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE      ) or not number >= 1:
          raise ValueError(
              'starting number must be\n                         and integer and be more than 0'      )
     if not iterations >= 1:
          raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'      )

     __A						:			Union[str, Any]								=	""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(_SCREAMING_SNAKE_CASE      )

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
  import doctest

  doctest.testmod()






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

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

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

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

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