






"""simple docstring"""


from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase    :   Optional[int]   			=						HfArgumentParser(InitializationArguments)
_UpperCamelCase    :   Dict   			=						parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase    :   Optional[Any]   			=						AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase    :   Any   			=						{
    '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)
_UpperCamelCase    :   Union[str, Any]   			=						AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase    :   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"""


_UpperCamelCase    :   List[Any]   			=						8.31_44_62  # Unit - J mol-1 K-1




def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:float				):







  '''simple docstring'''

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError('Invalid inputs. Enter positive value.'				)
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume




def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:float				):







  '''simple docstring'''

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError('Invalid inputs. Enter positive value.'				)
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
   from doctest import testmod

   testmod()




