






"""simple docstring"""






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


UpperCAmelCase_     :				Optional[int]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{
    """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__       (    __a				):






      '''simple docstring'''

      __UpperCamelCase							=						"gpt_neox"
      def __init__(		self  :						List[str]		,			lowercase_  :						Any=50432		,			lowercase_  :						Optional[int]=6144		,			lowercase_  :						Any=44		,			lowercase_  :						List[Any]=64		,			lowercase_  :						Union[str, Any]=24576		,			lowercase_  :						Optional[int]="gelu"		,			lowercase_  :						List[str]=0.25		,			lowercase_  :						Optional[Any]=10000		,			lowercase_  :						Tuple=0.0		,			lowercase_  :						int=0.0		,			lowercase_  :						Optional[int]=0.1		,			lowercase_  :						Dict=2048		,			lowercase_  :						Any=0.02		,			lowercase_  :						Union[str, Any]=1e-5		,			lowercase_  :						Optional[Any]=True		,			lowercase_  :						Optional[Any]=0		,			lowercase_  :						Dict=2		,			lowercase_  :						Union[str, Any]=False		,			lowercase_  :						Union[str, Any]=True		,			lowercase_  :						Optional[int]=None		,			**lowercase_  :						Optional[int]		,			):




          '''simple docstring'''


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




          '''simple docstring'''


          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}')
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.rope_scaling.get('''type'''		,			A__)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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 collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	9, 14  # noqa: F841
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    SCREAMING_SNAKE_CASE_   :						Dict							=	defaultdict(__a							)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]							)
        adjancency[nodea].append([nodea, cost]							)

    SCREAMING_SNAKE_CASE_   :						Any							=	mst(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        SCREAMING_SNAKE_CASE_   :						Dict							=	tuple(answer[:2]							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tuple(edge[::-1]							)
        assert edge in result or reverse in result


