






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


lowercase  							= logging.get_logger(__name__)

lowercase  							= {
    # 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 			UpperCamelCase_				(      snake_case_    ):



	'''simple docstring'''




	lowerCAmelCase													=			'''graphormer'''
	lowerCAmelCase													=			['''past_key_values''']




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

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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

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





