'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Tuple  ={}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  """llama"""
    a__       :						Any          =  ["""past_key_values"""]



    def __init__(self    :    List[str]   ,			__a    :    List[str]=32000   ,			__a    :    Tuple=4096   ,			__a    :    List[Any]=11008   ,			__a    :    Dict=32   ,			__a    :    Tuple=32   ,			__a    :    Any=None   ,			__a    :    Any="silu"   ,			__a    :    List[Any]=2048   ,			__a    :    List[Any]=0.02   ,			__a    :    str=1E-6   ,			__a    :    Optional[Any]=True   ,			__a    :    Union[str, Any]=0   ,			__a    :    Any=1   ,			__a    :    Dict=2   ,			__a    :    Dict=1   ,			__a    :    str=False   ,			__a    :    str=None   ,			**__a    :    Optional[Any]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
              UpperCAmelCase_           =   num_attention_heads

         UpperCAmelCase_           =   num_key_value_heads
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   rms_norm_eps
         UpperCAmelCase_           =   pretraining_tp
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   rope_scaling
         self._rope_scaling_validation()

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






    def 					_lowercase						(self    :    List[str] ):
         if self.rope_scaling is None:
              return

         if not isinstance(self.rope_scaling   ,			__a ) 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}""" )
         UpperCAmelCase_           =   self.rope_scaling.get("type"   ,			__a )
         UpperCAmelCase_           =   self.rope_scaling.get("factor"   ,			__a )
         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(__a   ,			__a ) or rope_scaling_factor <= 1.0:
              raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )





'''simple docstring'''




from itertools import product




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      list[int]:
							lowercase_					:			List[Any]										=       sides_number
							lowercase_					:			Dict										=       max_face_number * dice_number
							lowercase_					:			List[str]										=       [0] * (max_total + 1)

							lowercase_					:			Union[str, Any]										=       1
							lowercase_					:			Dict										=       range(UpperCAmelCase__      ,      max_face_number + 1 )
							for dice_numbers in product(UpperCAmelCase__      ,      repeat=UpperCAmelCase__ ):
														lowercase_					:			Any										=       sum(UpperCAmelCase__ )
														totals_frequencies[total] += 1

							return totals_frequencies




def 							lowerCamelCase	(      )			->      float:
							lowercase_					:			Optional[Any]										=       total_frequency_distribution(
							    sides_number=4      ,      dice_number=9 )
							lowercase_					:			List[str]										=       total_frequency_distribution(
							    sides_number=6      ,      dice_number=6 )

							lowercase_					:			Union[str, Any]										=       0
							lowercase_					:			Tuple										=       9
							lowercase_					:			Optional[int]										=       4 * 9
							lowercase_					:			List[Any]										=       6
							for peter_total in range(UpperCAmelCase__      ,      max_peter_total + 1 ):
														peter_wins_count += peter_totals_frequencies[peter_total] * sum(
														    colin_totals_frequencies[min_colin_total:peter_total] )

							lowercase_					:			str										=       (4**9) * (6**6)
							lowercase_					:			List[Any]										=       peter_wins_count / total_games_number

							lowercase_					:			Dict										=       round(UpperCAmelCase__      ,      ndigits=7 )

							return rounded_peter_win_probability


if __name__ == "__main__":
						print(f"""{solution() = }""")





