

from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase   :      List[Any]     =     3_00  # TEMPERATURE (unit = K)

def       A_     (							a    ,      a    ,      a    ,      ):

      """simple docstring"""



      if donor_conc <= 0:
            raise ValueError('Donor concentration should be positive'     )
      elif acceptor_conc <= 0:
            raise ValueError('Acceptor concentration should be positive'     )
      elif intrinsic_conc <= 0:
            raise ValueError('Intrinsic concentration should be positive'     )
      elif donor_conc <= intrinsic_conc:
            raise ValueError(
                'Donor concentration should be greater than intrinsic concentration'     )
      elif acceptor_conc <= intrinsic_conc:
            raise ValueError(
                'Acceptor concentration should be greater than intrinsic concentration'     )
      else:
            return (
                Boltzmann
                * T
                * log((donor_conc * acceptor_conc) / intrinsic_conc**2     )
                / physical_constants["electron volt"][0]
            )


if __name__ == "__main__":
    import doctest

    doctest.testmod()













"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

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

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

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

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