code: 
  
  """simple docstring"""
  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  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     'graphormer'
    SCREAMING_SNAKE_CASE							=     ['past_key_values']
  
  
  
    def __init__(self       ,					_lowerCamelCase = 1       ,					_lowerCamelCase = 512 * 9       ,					_lowerCamelCase = 512 * 3       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 128       ,					_lowerCamelCase = 5       ,					_lowerCamelCase = 1024       ,					_lowerCamelCase = "multi_hop"       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = False       ,					_lowerCamelCase = 12       ,					_lowerCamelCase = 768       ,					_lowerCamelCase = 768       ,					_lowerCamelCase = 32       ,					_lowerCamelCase = 0.1       ,					_lowerCamelCase = 0.1       ,					_lowerCamelCase = 0.0       ,					_lowerCamelCase = False       ,					_lowerCamelCase = False       ,					_lowerCamelCase = False       ,					_lowerCamelCase = "gelu"       ,					_lowerCamelCase = None       ,					_lowerCamelCase = False       ,					_lowerCamelCase = 0       ,					_lowerCamelCase = False       ,					_lowerCamelCase = 0.0       ,					_lowerCamelCase = 8       ,					_lowerCamelCase = None       ,					_lowerCamelCase = None       ,					_lowerCamelCase = True       ,					_lowerCamelCase = True       ,					_lowerCamelCase=0       ,					_lowerCamelCase=1       ,					_lowerCamelCase=2       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						num_classes
          UpperCAmelCase__ :       Union[str, Any]						=						num_atoms
          UpperCAmelCase__ :       Tuple						=						num_in_degree
          UpperCAmelCase__ :       List[Any]						=						num_out_degree
          UpperCAmelCase__ :       Any						=						num_edges
          UpperCAmelCase__ :       List[Any]						=						num_spatial
          UpperCAmelCase__ :       str						=						num_edge_dis
          UpperCAmelCase__ :       Any						=						edge_type
          UpperCAmelCase__ :       int						=						multi_hop_max_dist
          UpperCAmelCase__ :       Union[str, Any]						=						spatial_pos_max
          UpperCAmelCase__ :       Dict						=						max_nodes
          UpperCAmelCase__ :       Optional[int]						=						num_hidden_layers
          UpperCAmelCase__ :       Tuple						=						embedding_dim
          UpperCAmelCase__ :       int						=						embedding_dim
          UpperCAmelCase__ :       Optional[int]						=						ffn_embedding_dim
          UpperCAmelCase__ :       List[str]						=						num_attention_heads
          UpperCAmelCase__ :       Union[str, Any]						=						dropout
          UpperCAmelCase__ :       str						=						attention_dropout
          UpperCAmelCase__ :       str						=						layerdrop
          UpperCAmelCase__ :       Tuple						=						encoder_normalize_before
          UpperCAmelCase__ :       Optional[int]						=						pre_layernorm
          UpperCAmelCase__ :       Dict						=						apply_graphormer_init
          UpperCAmelCase__ :       Optional[int]						=						activation_fn
          UpperCAmelCase__ :       str						=						embed_scale
          UpperCAmelCase__ :       List[Any]						=						freeze_embeddings
          UpperCAmelCase__ :       List[str]						=						num_trans_layers_to_freeze
          UpperCAmelCase__ :       List[Any]						=						share_input_output_embed
          UpperCAmelCase__ :       Optional[Any]						=						traceable
          UpperCAmelCase__ :       Dict						=						q_noise
          UpperCAmelCase__ :       str						=						qn_block_size
  
          # These parameters are here for future extensions
          # atm, the model only supports self attention
          UpperCAmelCase__ :       Any						=						kdim
          UpperCAmelCase__ :       int						=						vdim
          UpperCAmelCase__ :       Tuple						=						self_attention
          UpperCAmelCase__ :       int						=						bias
  
          super().__init__(
              pad_token_id=_lowerCamelCase       ,					bos_token_id=_lowerCamelCase       ,					eos_token_id=_lowerCamelCase       ,					**_lowerCamelCase       ,					)
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  