


'''simple docstring'''


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        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'graphormer'
     lowerCamelCase__						=							['past_key_values']


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


          '''simple docstring'''





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

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

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





'''simple docstring'''


from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase          =     {
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       {node: {node} for node in graph}

     __A  :					List[str]						     =       {node: graph[node][:] for node in graph}

     while len(SCREAMING_SNAKE_CASE       ) > 2:
          # Choose a random edge.
          __A  :					Tuple						     =       random.choice(list(graph_copy.keys()       )       )
          __A  :					Any						     =       random.choice(graph_copy[u]       )

          # Contract edge (u, v) to new node uv
          __A  :					str						     =       u + v
          __A  :					Any						     =       list(set(graph_copy[u] + graph_copy[v]       )       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          __A  :					Any						     =       uv_neighbors
          for neighbor in uv_neighbors:
               graph_copy[neighbor].append(SCREAMING_SNAKE_CASE       )

          __A  :					str						     =       set(contracted_nodes[u].union(contracted_nodes[v]       )       )

          # Remove nodes u and v.
          del graph_copy[u]
          del graph_copy[v]
          for neighbor in uv_neighbors:
               if u in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )
               if v in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )

    # Find cutset.
     __A  :					int						     =       [contracted_nodes[node] for node in graph_copy]
     return {
         (node, neighbor)
         for node in groups[0]
         for neighbor in graph[node]
         if neighbor in groups[1]
     }


if __name__ == "__main__":
      print(partition_graph(TEST_GRAPH))

