



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__SCREAMING_SNAKE_CASE :      Union[str, Any]             ={
    # 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   SCREAMING_SNAKE_CASE__				(   _a					):
 """simple docstring"""


 A__    :    int						=				'''graphormer'''
 A__    :    int						=				['''past_key_values''']


 def __init__(		self					,   A = 1					,   A = 5_12 * 9					,   A = 5_12 * 3					,   A = 5_12					,   A = 5_12					,   A = 5_12					,   A = 1_28					,   A = 5					,   A = 10_24					,   A = "multi_hop"					,   A = 5_12					,   A = False					,   A = 12					,   A = 7_68					,   A = 7_68					,   A = 32					,   A = 0.1					,   A = 0.1					,   A = 0.0					,   A = False					,   A = False					,   A = False					,   A = "gelu"					,   A = None					,   A = False					,   A = 0					,   A = False					,   A = 0.0					,   A = 8					,   A = None					,   A = None					,   A = True					,   A = True					,   A=0					,   A=1					,   A=2					,   **A					,   ) ->							List[str]:
    A:      List[str]														=   num_classes
    A:      Optional[int]														=   num_atoms
    A:      List[Any]														=   num_in_degree
    A:      int														=   num_out_degree
    A:      str														=   num_edges
    A:      Dict														=   num_spatial
    A:      List[Any]														=   num_edge_dis
    A:      List[str]														=   edge_type
    A:      Dict														=   multi_hop_max_dist
    A:      str														=   spatial_pos_max
    A:      Any														=   max_nodes
    A:      List[Any]														=   num_hidden_layers
    A:      List[str]														=   embedding_dim
    A:      Union[str, Any]														=   embedding_dim
    A:      Optional[int]														=   ffn_embedding_dim
    A:      List[Any]														=   num_attention_heads
    A:      Union[str, Any]														=   dropout
    A:      int														=   attention_dropout
    A:      Tuple														=   layerdrop
    A:      int														=   encoder_normalize_before
    A:      Any														=   pre_layernorm
    A:      List[str]														=   apply_graphormer_init
    A:      List[Any]														=   activation_fn
    A:      Any														=   embed_scale
    A:      str														=   freeze_embeddings
    A:      List[Any]														=   num_trans_layers_to_freeze
    A:      List[Any]														=   share_input_output_embed
    A:      Any														=   traceable
    A:      List[str]														=   q_noise
    A:      List[Any]														=   qn_block_size

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

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










'''simple docstring'''
from ..utils import DummyObject, requires_backends


class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							Tuple:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Optional[int]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							List[Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)







class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							List[str]:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





