






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				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   "graphormer"
	UpperCamelCase__			=   ["past_key_values"]

	def __init__(							self ,					UpperCAmelCase = 1 ,					UpperCAmelCase = 512 * 9 ,					UpperCAmelCase = 512 * 3 ,					UpperCAmelCase = 512 ,					UpperCAmelCase = 512 ,					UpperCAmelCase = 512 ,					UpperCAmelCase = 128 ,					UpperCAmelCase = 5 ,					UpperCAmelCase = 1024 ,					UpperCAmelCase = "multi_hop" ,					UpperCAmelCase = 512 ,					UpperCAmelCase = False ,					UpperCAmelCase = 12 ,					UpperCAmelCase = 768 ,					UpperCAmelCase = 768 ,					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 ,					):
							"""simple docstring"""







							_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=UpperCAmelCase ,					bos_token_id=UpperCAmelCase ,					eos_token_id=UpperCAmelCase ,					**UpperCAmelCase ,					)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

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