





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


__UpperCamelCase			:		Union[str, Any]		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Optional[int]		    =   {
    # 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__   ):
 snake_case__    	=    '''graphormer'''
 snake_case__    	=    ['''past_key_values''']



 def __init__(			self							,    _snake_case = 1							,    _snake_case = 5_12 * 9							,    _snake_case = 5_12 * 3							,    _snake_case = 5_12							,    _snake_case = 5_12							,    _snake_case = 5_12							,    _snake_case = 1_28							,    _snake_case = 5							,    _snake_case = 10_24							,    _snake_case = "multi_hop"							,    _snake_case = 5_12							,    _snake_case = False							,    _snake_case = 12							,    _snake_case = 7_68							,    _snake_case = 7_68							,    _snake_case = 32							,    _snake_case = 0.1							,    _snake_case = 0.1							,    _snake_case = 0.0							,    _snake_case = False							,    _snake_case = False							,    _snake_case = False							,    _snake_case = "gelu"							,    _snake_case = None							,    _snake_case = False							,    _snake_case = 0							,    _snake_case = False							,    _snake_case = 0.0							,    _snake_case = 8							,    _snake_case = None							,    _snake_case = None							,    _snake_case = True							,    _snake_case = True							,    _snake_case=0							,    _snake_case=1							,    _snake_case=2							,    **_snake_case							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      num_classes
  lowerCAmelCase  							=      num_atoms
  lowerCAmelCase  							=      num_in_degree
  lowerCAmelCase  							=      num_out_degree
  lowerCAmelCase  							=      num_edges
  lowerCAmelCase  							=      num_spatial
  lowerCAmelCase  							=      num_edge_dis
  lowerCAmelCase  							=      edge_type
  lowerCAmelCase  							=      multi_hop_max_dist
  lowerCAmelCase  							=      spatial_pos_max
  lowerCAmelCase  							=      max_nodes
  lowerCAmelCase  							=      num_hidden_layers
  lowerCAmelCase  							=      embedding_dim
  lowerCAmelCase  							=      embedding_dim
  lowerCAmelCase  							=      ffn_embedding_dim
  lowerCAmelCase  							=      num_attention_heads
  lowerCAmelCase  							=      dropout
  lowerCAmelCase  							=      attention_dropout
  lowerCAmelCase  							=      layerdrop
  lowerCAmelCase  							=      encoder_normalize_before
  lowerCAmelCase  							=      pre_layernorm
  lowerCAmelCase  							=      apply_graphormer_init
  lowerCAmelCase  							=      activation_fn
  lowerCAmelCase  							=      embed_scale
  lowerCAmelCase  							=      freeze_embeddings
  lowerCAmelCase  							=      num_trans_layers_to_freeze
  lowerCAmelCase  							=      share_input_output_embed
  lowerCAmelCase  							=      traceable
  lowerCAmelCase  							=      q_noise
  lowerCAmelCase  							=      qn_block_size

  # These parameters are here for future extensions
  # atm, the model only supports self attention
  lowerCAmelCase  							=      kdim
  lowerCAmelCase  							=      vdim
  lowerCAmelCase  							=      self_attention
  lowerCAmelCase  							=      bias

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













"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

