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


__lowerCAmelCase    :  Optional[Any]										= logging.get_logger(__name__)

__lowerCAmelCase    :  Tuple										= {
    # 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					):
      __lowerCamelCase							=					'graphormer'
      __lowerCamelCase							=					['past_key_values']






      def __init__(      self  ,		__lowerCamelCase = 1  ,		__lowerCamelCase = 5_12 * 9  ,		__lowerCamelCase = 5_12 * 3  ,		__lowerCamelCase = 5_12  ,		__lowerCamelCase = 5_12  ,		__lowerCamelCase = 5_12  ,		__lowerCamelCase = 1_28  ,		__lowerCamelCase = 5  ,		__lowerCamelCase = 10_24  ,		__lowerCamelCase = "multi_hop"  ,		__lowerCamelCase = 5_12  ,		__lowerCamelCase = False  ,		__lowerCamelCase = 12  ,		__lowerCamelCase = 7_68  ,		__lowerCamelCase = 7_68  ,		__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  ,		)     ->				Tuple:


          '''simple docstring'''







          snake_case:						List[str]         =   num_classes
          snake_case:						Optional[Any]         =   num_atoms
          snake_case:						Tuple         =   num_in_degree
          snake_case:						int         =   num_out_degree
          snake_case:						Tuple         =   num_edges
          snake_case:						Any         =   num_spatial
          snake_case:						int         =   num_edge_dis
          snake_case:						List[str]         =   edge_type
          snake_case:						List[str]         =   multi_hop_max_dist
          snake_case:						Dict         =   spatial_pos_max
          snake_case:						str         =   max_nodes
          snake_case:						List[str]         =   num_hidden_layers
          snake_case:						Dict         =   embedding_dim
          snake_case:						Tuple         =   embedding_dim
          snake_case:						Optional[Any]         =   ffn_embedding_dim
          snake_case:						Tuple         =   num_attention_heads
          snake_case:						Tuple         =   dropout
          snake_case:						Union[str, Any]         =   attention_dropout
          snake_case:						List[Any]         =   layerdrop
          snake_case:						int         =   encoder_normalize_before
          snake_case:						Union[str, Any]         =   pre_layernorm
          snake_case:						Union[str, Any]         =   apply_graphormer_init
          snake_case:						List[Any]         =   activation_fn
          snake_case:						Any         =   embed_scale
          snake_case:						Dict         =   freeze_embeddings
          snake_case:						Tuple         =   num_trans_layers_to_freeze
          snake_case:						Tuple         =   share_input_output_embed
          snake_case:						List[str]         =   traceable
          snake_case:						Dict         =   q_noise
          snake_case:						Union[str, Any]         =   qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          snake_case:						str         =   kdim
          snake_case:						List[str]         =   vdim
          snake_case:						str         =   self_attention
          snake_case:						Any         =   bias

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__        =				logging.get_logger(__name__)

UpperCAmelCase__        =				{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     '''rembert'''





     def __init__(self						,	UpperCAmelCase=2_5_0_3_0_0						,	UpperCAmelCase=1_1_5_2						,	UpperCAmelCase=3_2						,	UpperCAmelCase=1_8						,	UpperCAmelCase=2_5_6						,	UpperCAmelCase=1_6_6_4						,	UpperCAmelCase=4_6_0_8						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=1e-12						,	UpperCAmelCase=True						,	UpperCAmelCase=0						,	UpperCAmelCase=3_1_2						,	UpperCAmelCase=3_1_3						,	**UpperCAmelCase						,	)	->  Tuple:
      super().__init__(pad_token_id=UpperCAmelCase						,	bos_token_id=UpperCAmelCase						,	eos_token_id=UpperCAmelCase						,	**UpperCAmelCase			)

      _lowercase				 =vocab_size
      _lowercase				 =input_embedding_size
      _lowercase				 =output_embedding_size
      _lowercase				 =max_position_embeddings
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =intermediate_size
      _lowercase				 =hidden_act
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =classifier_dropout_prob
      _lowercase				 =initializer_range
      _lowercase				 =type_vocab_size
      _lowercase				 =layer_norm_eps
      _lowercase				 =use_cache
      _lowercase				 =False







class  lowerCamelCase__  (			lowerCAmelCase):





     @property
     def 	__A   (self			)	->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase				 ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase				 ={0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]			)





     @property
     def 	__A   (self			)	->  float:
      return 1e-4

