




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


a_ 		=  logging.get_logger(__name__)

a_ 		=  {
    # 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 			_lowerCamelCase    (      a_ ):

      """simple docstring"""

      lowerCAmelCase__      :			int     =       '''graphormer'''
      lowerCAmelCase__      :			str     =       ['''past_key_values''']







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

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

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



