from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase    :				Tuple							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Optional[int]							     =	{
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"open-llama"




   def __init__(					self    :       Dict ,			_lowercase    :       Tuple=10_00_00 ,			_lowercase    :       Dict=40_96 ,			_lowercase    :       Tuple=1_10_08 ,			_lowercase    :       str=32 ,			_lowercase    :       int=32 ,			_lowercase    :       List[str]="silu" ,			_lowercase    :       Optional[Any]=20_48 ,			_lowercase    :       List[Any]=0.02 ,			_lowercase    :       Tuple=1E-6 ,			_lowercase    :       str=True ,			_lowercase    :       int=0 ,			_lowercase    :       Dict=1 ,			_lowercase    :       Union[str, Any]=2 ,			_lowercase    :       Union[str, Any]=False ,			_lowercase    :       str=True ,			_lowercase    :       Optional[int]=0.1 ,			_lowercase    :       Tuple=0.1 ,			_lowercase    :       List[str]=True ,			_lowercase    :       Dict=True ,			_lowercase    :       Optional[Any]=None ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   rms_norm_eps
     SCREAMING_SNAKE_CASE__    							=   use_cache
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop(
         """use_memorry_efficient_attention""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   use_stable_embedding
     SCREAMING_SNAKE_CASE__    							=   shared_input_output_embedding
     SCREAMING_SNAKE_CASE__    							=   rope_scaling
     self._rope_scaling_validation()

     super().__init__(
         pad_token_id=_lowercase ,			bos_token_id=_lowercase ,			eos_token_id=_lowercase ,			tie_word_embeddings=_lowercase ,			**_lowercase ,			)




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     if self.rope_scaling is None:
       return

     if not isinstance(self.rope_scaling ,			_lowercase   ) or len(self.rope_scaling   ) != 2:
       raise ValueError(
           """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
           f"""got {self.rope_scaling}"""   )
     SCREAMING_SNAKE_CASE__    							=   self.rope_scaling.get("""type""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.rope_scaling.get("""factor""" ,			_lowercase   )
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
       raise ValueError(
           f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
     if rope_scaling_factor is None or not isinstance(_lowercase ,			_lowercase   ) or rope_scaling_factor <= 1.0:
       raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )

from math import log

from scipy.constants import Boltzmann, physical_constants

__lowerCamelCase    :				int							     =	300  # TEMPERATURE (unit = K)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  )							->      float:







  """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()
