






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


__UpperCAmelCase							:   Tuple							=  logging.get_logger(__name__)

__UpperCAmelCase							:   Optional[Any]							=  {}


class 					_snake_case						(			_A ):
	_A          =   'llama'
	_A          =   ['past_key_values']






	def __init__(		self					,UpperCamelCase=32_000					,UpperCamelCase=4_096					,UpperCamelCase=11_008					,UpperCamelCase=32					,UpperCamelCase=32					,UpperCamelCase=None					,UpperCamelCase="silu"					,UpperCamelCase=2_048					,UpperCamelCase=0.02					,UpperCamelCase=1E-6					,UpperCamelCase=True					,UpperCamelCase=0					,UpperCamelCase=1					,UpperCamelCase=2					,UpperCamelCase=1					,UpperCamelCase=False					,UpperCamelCase=None					,**UpperCamelCase					,)						->    Tuple:
					snake_case__      :int         =   vocab_size
					snake_case__      :Any         =   max_position_embeddings
					snake_case__      :int         =   hidden_size
					snake_case__      :List[Any]         =   intermediate_size
					snake_case__      :int         =   num_hidden_layers
					snake_case__      :Union[str, Any]         =   num_attention_heads

					# for backward compatibility
					if num_key_value_heads is None:
									snake_case__      :Dict         =   num_attention_heads

					snake_case__      :List[str]         =   num_key_value_heads
					snake_case__      :Optional[int]         =   hidden_act
					snake_case__      :Any         =   initializer_range
					snake_case__      :Dict         =   rms_norm_eps
					snake_case__      :List[str]         =   pretraining_tp
					snake_case__      :Any         =   use_cache
					snake_case__      :Optional[Any]         =   rope_scaling
					self._rope_scaling_validation()

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






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					if self.rope_scaling is None:
									return

					if not isinstance(self.rope_scaling					,UpperCamelCase       ) 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}'       )
					snake_case__      :Optional[int]         =   self.rope_scaling.get("type"					,UpperCamelCase       )
					snake_case__      :Optional[Any]         =   self.rope_scaling.get("factor"					,UpperCamelCase       )
					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(UpperCamelCase					,UpperCamelCase       ) or rope_scaling_factor <= 1.0:
									raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'       )







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]