"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE									=						{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}



class  _lowerCamelCase			(__lowerCamelCase     ):
							_snake_case     =  "gpt_neox"







							def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       Union[str, Any]=5_0_4_3_2			,					lowerCamelCase_    :       Optional[Any]=6_1_4_4			,					lowerCamelCase_    :       int=4_4			,					lowerCamelCase_    :       List[Any]=6_4			,					lowerCamelCase_    :       str=2_4_5_7_6			,					lowerCamelCase_    :       Any="gelu"			,					lowerCamelCase_    :       Tuple=0.25			,					lowerCamelCase_    :       List[Any]=1_0_0_0_0			,					lowerCamelCase_    :       Tuple=0.0			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       str=0.1			,					lowerCamelCase_    :       List[str]=2_0_4_8			,					lowerCamelCase_    :       List[str]=0.02			,					lowerCamelCase_    :       Tuple=1E-5			,					lowerCamelCase_    :       Optional[int]=True			,					lowerCamelCase_    :       Tuple=0			,					lowerCamelCase_    :       Optional[Any]=2			,					lowerCamelCase_    :       Optional[int]=False			,					lowerCamelCase_    :       str=True			,					lowerCamelCase_    :       Dict=None			,					**lowerCamelCase_    :       Tuple			,					):

								"""simple docstring"""






								super().__init__(bos_token_id=lowerCamelCase_			,					eos_token_id=lowerCamelCase_			,					**lowerCamelCase_						)
								_lowercase :    Optional[Any]							=							vocab_size
								_lowercase :    Union[str, Any]							=							max_position_embeddings
								_lowercase :    Optional[int]							=							hidden_size
								_lowercase :    Optional[Any]							=							num_hidden_layers
								_lowercase :    List[str]							=							num_attention_heads
								_lowercase :    int							=							intermediate_size
								_lowercase :    int							=							hidden_act
								_lowercase :    List[str]							=							rotary_pct
								_lowercase :    str							=							rotary_emb_base
								_lowercase :    Dict							=							attention_dropout
								_lowercase :    Any							=							hidden_dropout
								_lowercase :    List[Any]							=							classifier_dropout
								_lowercase :    Any							=							initializer_range
								_lowercase :    Any							=							layer_norm_eps
								_lowercase :    Optional[int]							=							use_cache
								_lowercase :    Union[str, Any]							=							tie_word_embeddings
								_lowercase :    int							=							use_parallel_residual
								_lowercase :    Any							=							rope_scaling
								self._rope_scaling_validation()

								if self.hidden_size % self.num_attention_heads != 0:
									raise ValueError(
									    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'						)







							def 							__UpperCAmelCase					( self    :       List[Any]						):

								"""simple docstring"""






								if self.rope_scaling is None:
									return

								if not isinstance(self.rope_scaling			,					lowerCamelCase_						) 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}'''						)
								_lowercase :    int							=							self.rope_scaling.get('type'			,					lowerCamelCase_						)
								_lowercase :    int							=							self.rope_scaling.get('factor'			,					lowerCamelCase_						)
								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(lowerCamelCase_			,					lowerCamelCase_						) 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"""







import functools


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) or not all(isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) for day in days			):
		raise ValueError('The parameter days should be a list of integers'			)

	if len(__UpperCAmelCase			) != 3 or not all(isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) for cost in costs			):
		raise ValueError('The parameter costs should be a list of three integers'			)

	if len(__UpperCAmelCase			) == 0:
		return 0

	if min(__UpperCAmelCase			) <= 0:
		raise ValueError('All days elements should be greater than 0'			)

	if max(__UpperCAmelCase			) >= 366:
		raise ValueError('All days elements should be less than 366'			)

	_lowercase :    List[Any]							=							set(__UpperCAmelCase			)

	@functools.cache
	def dynamic_programming(__UpperCAmelCase			) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1			)

		return min(
		    costs[0] + dynamic_programming(index + 1			)     ,costs[1] + dynamic_programming(index + 7			)     ,costs[2] + dynamic_programming(index + 30			)     ,)

	return dynamic_programming(1			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



