



import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


UpperCAmelCase_			      =  TypeVar('''T''', bound='''TaskTemplate''')





@dataclass(frozen=snake_case__      )
class 		__SCREAMING_SNAKE_CASE      (				abc.ABC      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  4_2
				SCREAMING_SNAKE_CASE_ 				=  4_2
				SCREAMING_SNAKE_CASE_ 				=  4_2







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					return copy.deepcopy(self      )







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return Features(**self.input_schema						,	**self.label_schema      )







				@property
				@abc.abstractmethod
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					raise NotImplementedError







				@classmethod
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   {f.name for f in dataclasses.fields(cls      )}
					return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )








"""simple docstring"""





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


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[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      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






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

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

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


