


"""simple docstring"""







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


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    # 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    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[int]      =			"""graphormer"""
    UpperCAmelCase			:				Optional[int]      =			["""past_key_values"""]






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

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

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










"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase          = logging.get_logger(__name__)

__lowercase          = {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}






class 		_A    (    _a    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase			:				Any      =			"""squeezebert"""






    def __init__(   self							:       List[str]							,					__UpperCAmelCase							:       Tuple=30522							,					__UpperCAmelCase							:       Tuple=768							,					__UpperCAmelCase							:       int=12							,					__UpperCAmelCase							:       str=12							,					__UpperCAmelCase							:       List[Any]=3072							,					__UpperCAmelCase							:       Dict="gelu"							,					__UpperCAmelCase							:       List[Any]=0.1							,					__UpperCAmelCase							:       Optional[int]=0.1							,					__UpperCAmelCase							:       Optional[Any]=512							,					__UpperCAmelCase							:       int=2							,					__UpperCAmelCase							:       Dict=0.02							,					__UpperCAmelCase							:       Optional[int]=1e-12							,					__UpperCAmelCase							:       Optional[Any]=0							,					__UpperCAmelCase							:       Optional[Any]=768							,					__UpperCAmelCase							:       Tuple=4							,					__UpperCAmelCase							:       Dict=4							,					__UpperCAmelCase							:       str=4							,					__UpperCAmelCase							:       List[Any]=1							,					__UpperCAmelCase							:       Optional[int]=4							,					__UpperCAmelCase							:       Optional[int]=4							,					**__UpperCAmelCase							:       Tuple							,					):
      super().__init__(pad_token_id=__UpperCAmelCase							,					**__UpperCAmelCase)

      a		:						List[Any] 				=     vocab_size
      a		:						List[Any] 				=     hidden_size
      a		:						Dict 				=     num_hidden_layers
      a		:						int 				=     num_attention_heads
      a		:						Dict 				=     hidden_act
      a		:						Tuple 				=     intermediate_size
      a		:						int 				=     hidden_dropout_prob
      a		:						List[Any] 				=     attention_probs_dropout_prob
      a		:						Union[str, Any] 				=     max_position_embeddings
      a		:						Union[str, Any] 				=     type_vocab_size
      a		:						Optional[Any] 				=     initializer_range
      a		:						Any 				=     layer_norm_eps
      a		:						Any 				=     embedding_size
      a		:						Tuple 				=     q_groups
      a		:						List[str] 				=     k_groups
      a		:						str 				=     v_groups
      a		:						Optional[Any] 				=     post_attention_groups
      a		:						List[str] 				=     intermediate_groups
      a		:						Optional[int] 				=     output_groups






class 		_A    (    _a    ):







    """simple docstring"""




    @property
    def       __snake_case				(   self							:       Optional[Any]):
      if self.task == "multiple-choice":
        a		:						Optional[int] 				=     {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a		:						Tuple 				=     {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ])






