


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


_A		:					Any          =							logging.get_logger(__name__)

_A		:					Tuple          =							{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}



class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Any       =						"graphormer"
     UpperCAmelCase__			:	Optional[Any]       =						["past_key_values"]





     def __init__(     self       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					int = 1     ,						SCREAMING_SNAKE_CASE_       :					int = 512 * 9     ,						SCREAMING_SNAKE_CASE_       :					int = 512 * 3     ,						SCREAMING_SNAKE_CASE_       :					int = 512     ,						SCREAMING_SNAKE_CASE_       :					int = 512     ,						SCREAMING_SNAKE_CASE_       :					int = 512     ,						SCREAMING_SNAKE_CASE_       :					int = 128     ,						SCREAMING_SNAKE_CASE_       :					int = 5     ,						SCREAMING_SNAKE_CASE_       :					int = 1024     ,						SCREAMING_SNAKE_CASE_       :					str = "multi_hop"     ,						SCREAMING_SNAKE_CASE_       :					int = 512     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					int = 12     ,						SCREAMING_SNAKE_CASE_       :					int = 768     ,						SCREAMING_SNAKE_CASE_       :					int = 768     ,						SCREAMING_SNAKE_CASE_       :					int = 32     ,						SCREAMING_SNAKE_CASE_       :					float = 0.1     ,						SCREAMING_SNAKE_CASE_       :					float = 0.1     ,						SCREAMING_SNAKE_CASE_       :					float = 0.0     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					str = "gelu"     ,						SCREAMING_SNAKE_CASE_       :					float = None     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					int = 0     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					float = 0.0     ,						SCREAMING_SNAKE_CASE_       :					int = 8     ,						SCREAMING_SNAKE_CASE_       :					int = None     ,						SCREAMING_SNAKE_CASE_       :					int = None     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=0     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]=1     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=2     ,						**SCREAMING_SNAKE_CASE_       :					Optional[Any]     ,						):
        __lowerCamelCase:					Optional[int]	 =						num_classes
        __lowerCamelCase:					List[Any]	 =						num_atoms
        __lowerCamelCase:					Optional[int]	 =						num_in_degree
        __lowerCamelCase:					Tuple	 =						num_out_degree
        __lowerCamelCase:					List[Any]	 =						num_edges
        __lowerCamelCase:					Union[str, Any]	 =						num_spatial
        __lowerCamelCase:					Any	 =						num_edge_dis
        __lowerCamelCase:					List[str]	 =						edge_type
        __lowerCamelCase:					List[str]	 =						multi_hop_max_dist
        __lowerCamelCase:					Optional[Any]	 =						spatial_pos_max
        __lowerCamelCase:					int	 =						max_nodes
        __lowerCamelCase:					int	 =						num_hidden_layers
        __lowerCamelCase:					Dict	 =						embedding_dim
        __lowerCamelCase:					Optional[int]	 =						embedding_dim
        __lowerCamelCase:					int	 =						ffn_embedding_dim
        __lowerCamelCase:					Union[str, Any]	 =						num_attention_heads
        __lowerCamelCase:					int	 =						dropout
        __lowerCamelCase:					Tuple	 =						attention_dropout
        __lowerCamelCase:					Optional[Any]	 =						layerdrop
        __lowerCamelCase:					Optional[Any]	 =						encoder_normalize_before
        __lowerCamelCase:					Optional[int]	 =						pre_layernorm
        __lowerCamelCase:					Any	 =						apply_graphormer_init
        __lowerCamelCase:					int	 =						activation_fn
        __lowerCamelCase:					Optional[int]	 =						embed_scale
        __lowerCamelCase:					Optional[int]	 =						freeze_embeddings
        __lowerCamelCase:					str	 =						num_trans_layers_to_freeze
        __lowerCamelCase:					str	 =						share_input_output_embed
        __lowerCamelCase:					Tuple	 =						traceable
        __lowerCamelCase:					int	 =						q_noise
        __lowerCamelCase:					Union[str, Any]	 =						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        __lowerCamelCase:					Union[str, Any]	 =						kdim
        __lowerCamelCase:					List[str]	 =						vdim
        __lowerCamelCase:					Union[str, Any]	 =						self_attention
        __lowerCamelCase:					int	 =						bias

        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_     ,						bos_token_id=SCREAMING_SNAKE_CASE_     ,						eos_token_id=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_     ,						)






import sys



def      __lowerCAmelCase    (				snake_case						:     int    ,   snake_case						:     list[int]			)							->				int:
   if not numbers:
      return 0
   if target == 0 and target in numbers:
      return 0
   if not isinstance(snake_case    ,   (list, tuple)			) or not all(
       isinstance(snake_case    ,   snake_case			) for number in numbers			):
      raise ValueError("""numbers must be an iterable of integers"""			)

   __lowerCamelCase:					Union[str, Any]	 =						0
   __lowerCamelCase:					str	 =						sys.maxsize

   while right < len(snake_case			):
      curr_sum += numbers[right]
      while curr_sum >= target and left <= right:
         __lowerCamelCase:					str	 =						min(snake_case    ,   right - left + 1			)
         curr_sum -= numbers[left]
         left += 1
      right += 1

   return 0 if min_len == sys.maxsize else min_len


