





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


_UpperCamelCase       :				str           =			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 		_lowerCAmelCase(		__A):

  """simple docstring"""







  lowerCamelCase__     	=       """graphormer"""
  lowerCamelCase__     	=       ["""past_key_values"""]

  def __init__(   self    ,							UpperCAmelCase = 1    ,							UpperCAmelCase = 5_12 * 9    ,							UpperCAmelCase = 5_12 * 3    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 1_28    ,							UpperCAmelCase = 5    ,							UpperCAmelCase = 10_24    ,							UpperCAmelCase = "multi_hop"    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = False    ,							UpperCAmelCase = 12    ,							UpperCAmelCase = 7_68    ,							UpperCAmelCase = 7_68    ,							UpperCAmelCase = 32    ,							UpperCAmelCase = 0.1    ,							UpperCAmelCase = 0.1    ,							UpperCAmelCase = 0.0    ,							UpperCAmelCase = False    ,							UpperCAmelCase = False    ,							UpperCAmelCase = False    ,							UpperCAmelCase = "gelu"    ,							UpperCAmelCase = None    ,							UpperCAmelCase = False    ,							UpperCAmelCase = 0    ,							UpperCAmelCase = False    ,							UpperCAmelCase = 0.0    ,							UpperCAmelCase = 8    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = True    ,							UpperCAmelCase = True    ,							UpperCAmelCase=0    ,							UpperCAmelCase=1    ,							UpperCAmelCase=2    ,							**UpperCAmelCase    ,							)->   Optional[int]:
     __A    =  num_classes
     __A    =  num_atoms
     __A    =  num_in_degree
     __A    =  num_out_degree
     __A    =  num_edges
     __A    =  num_spatial
     __A    =  num_edge_dis
     __A    =  edge_type
     __A    =  multi_hop_max_dist
     __A    =  spatial_pos_max
     __A    =  max_nodes
     __A    =  num_hidden_layers
     __A    =  embedding_dim
     __A    =  embedding_dim
     __A    =  ffn_embedding_dim
     __A    =  num_attention_heads
     __A    =  dropout
     __A    =  attention_dropout
     __A    =  layerdrop
     __A    =  encoder_normalize_before
     __A    =  pre_layernorm
     __A    =  apply_graphormer_init
     __A    =  activation_fn
     __A    =  embed_scale
     __A    =  freeze_embeddings
     __A    =  num_trans_layers_to_freeze
     __A    =  share_input_output_embed
     __A    =  traceable
     __A    =  q_noise
     __A    =  qn_block_size

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

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









from typing import TYPE_CHECKING

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


A										=      {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      ['DetrFeatureExtractor']
				A										=      ['DetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetrForObjectDetection',
				    'DetrForSegmentation',
				    'DetrModel',
				    'DetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				A										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)