"""simple docstring"""






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


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

lowerCAmelCase__    :    Dict			=      {
    # 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 							snake_case			(					A_							):




					"""simple docstring"""

					snake_case__							       =					"graphormer"
					snake_case__							       =					["past_key_values"]







					def __init__(							self  :      List[Any]       ,lowerCamelCase__  :      int = 1       ,lowerCamelCase__  :      int = 512 * 9       ,lowerCamelCase__  :      int = 512 * 3       ,lowerCamelCase__  :      int = 512       ,lowerCamelCase__  :      int = 512       ,lowerCamelCase__  :      int = 512       ,lowerCamelCase__  :      int = 128       ,lowerCamelCase__  :      int = 5       ,lowerCamelCase__  :      int = 1_024       ,lowerCamelCase__  :      str = "multi_hop"       ,lowerCamelCase__  :      int = 512       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      int = 12       ,lowerCamelCase__  :      int = 768       ,lowerCamelCase__  :      int = 768       ,lowerCamelCase__  :      int = 32       ,lowerCamelCase__  :      float = 0.1       ,lowerCamelCase__  :      float = 0.1       ,lowerCamelCase__  :      float = 0.0       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      str = "gelu"       ,lowerCamelCase__  :      float = None       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      int = 0       ,lowerCamelCase__  :      bool = False       ,lowerCamelCase__  :      float = 0.0       ,lowerCamelCase__  :      int = 8       ,lowerCamelCase__  :      int = None       ,lowerCamelCase__  :      int = None       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Tuple=0       ,lowerCamelCase__  :      Optional[Any]=1       ,lowerCamelCase__  :      Optional[Any]=2       ,**lowerCamelCase__  :      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


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

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

