





from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase							   =      TypeVar("""T""")






class 							_SCREAMING_SNAKE_CASE(   Generic[T]	):
   def __init__(    self							,     lowerCAmelCase_    )	->int:
        a  :    str				=						data  # Assign data
        a  :    Node[T] | None				=						None  # Initialize next as null
        a  :    Node[T] | None				=						None  # Initialize prev as null






class 							_SCREAMING_SNAKE_CASE(   Generic[T]	):
   def __init__(    self    )	->None:
        a  :    Node[T] | None				=						None
   def _lowercase(    self							,     lowerCAmelCase_    )	->None:
        if self.head is None:
             a  :    str				=						Node(lowerCAmelCase_    )
        else:
             a  :    str				=						Node(lowerCAmelCase_    )
             a  :    Optional[Any]				=						new_node
             a  :    Optional[Any]				=						self.head
             a  :    List[Any]				=						None
             a  :    int				=						new_node
   def _lowercase(    self    )	->T | None:
        if self.head is None:
             return None
        else:
             assert self.head is not None
             a  :    Union[str, Any]				=						self.head.data
             a  :    Optional[Any]				=						self.head.next
             if self.head is not None:
                  a  :    Union[str, Any]				=						None
             return temp
   def _lowercase(    self    )	->T | None:
        return self.head.data if self.head is not None else None
   def __len__(    self    )	->int:
        a  :    int				=						self.head
        a  :    str				=						0
        while temp is not None:
             count += 1
             a  :    List[Any]				=						temp.next
        return count
   def _lowercase(    self    )	->bool:
        return self.head is None


   def _lowercase(    self    )	->None:
        print('''stack elements are:'''    )
        a  :    str				=						self.head
        while temp is not None:
             print(temp.data							,     end='''->'''    )
             a  :    List[Any]				=						temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  UpperCAmelCase							   =      Stack()

  # Insert 4 at the beginning. So stack becomes 4->None
  print("""Stack operations using Doubly LinkedList""")
  stack.push(4)

  # Insert 5 at the beginning. So stack becomes 4->5->None
  stack.push(5)

  # Insert 6 at the beginning. So stack becomes 4->5->6->None
  stack.push(6)

  # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
  stack.push(7)

  # Print the stack
  stack.print_stack()

  # Print the top element
  print("""\nTop element is """, stack.top())

  # Print the stack size
  print("""Size of the stack is """, len(stack))

  # pop the top element
  stack.pop()

  # pop the top element
  stack.pop()

  # two elements have now been popped off
  stack.print_stack()

  # Print True if the stack is empty else False
  print("""\nstack is empty:""", stack.is_empty())
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # 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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['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_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

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

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