


from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
snake_case				:    str       				=     HfArgumentParser(InitializationArguments)
snake_case				:    Tuple       				=     parser.parse_args()

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

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

# Initialize new model with config
snake_case				:    Any       				=     AutoModelForCausalLM.from_config(config)

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











"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase__       (snake_case__							:					list[float]    ):
  """simple docstring"""

  _snake_case							:		int								=  0.00
  _snake_case							:		int								=  0
  for resistor in resistors:
    if resistor <= 0:
      _snake_case							:		Dict								=  F"Resistor at index {index} has a negative or zero value!"
      raise ValueError(snake_case__    )
    first_sum += 1 / float(snake_case__    )
    index += 1
  return 1 / first_sum


def 					UpperCAmelCase__       (snake_case__							:					list[float]    ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  0.00
  _snake_case							:		Any								=  0
  for resistor in resistors:
    sum_r += resistor
    if resistor < 0:
      _snake_case							:		Any								=  F"Resistor at index {index} has a negative value!"
      raise ValueError(snake_case__    )
    index += 1
  return sum_r


if __name__ == "__main__":
     import doctest

     doctest.testmod()


