


'''simple docstring'''


import torch

from transformers import AutoTokenizer







class        __magic_name__  (					A__   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       AutoTokenizer.from_pretrained(_lowerCAmelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if isinstance(_lowerCAmelCase ,    _lowerCAmelCase		) and all(isinstance(_x ,    _lowerCAmelCase		) for _x in x		):
               __A  :					Optional[int]						     =       None
               for l in x:
                    __A  :					List[str]						     =       self.tokenizer(
                        _lowerCAmelCase ,    padding="max_length" ,    max_length=384 ,    truncation=_lowerCAmelCase ,    return_tensors="pt" ,    )
                    __A  :					Optional[int]						     =       torch.tensor([len(_lowerCAmelCase		)]		)
                    if d is not None:
                         for k in d.keys():
                              __A  :					List[Any]						     =       torch.cat((d[k], t[k]) ,    0		)
                    else:
                         __A  :					Union[str, Any]						     =       t

               __A  :					Dict						     =       torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"		)		)
               __A  :					Tuple						     =       torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"		)		)

          elif isinstance(_lowerCAmelCase ,    _lowerCAmelCase		) and all(isinstance(_x ,    _lowerCAmelCase		) for _x in x		):
               __A  :					str						     =       self.tokenizer(
                   _lowerCAmelCase ,    padding="max_length" ,    max_length=384 ,    truncation=_lowerCAmelCase ,    return_tensors="pt" ,    )

          else:
               raise Exception(
                   "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
                   " strings` for supports are supported."		)

          return d


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=0.70		):


          '''simple docstring'''





          __A  :					str						     =       []
          for idx in range(len(W_query["input_ids"]		)		):
               __A  :					int						     =       range(p_start.shape[1]		)

               __A  :					Optional[Any]						     =       []
               for start_id in start_indexes:
                    for end_id in end_indexes:
                         if start_id < end_id:
                              output.append(
                                  (
                                      start_id,
                                      end_id,
                                      p_start[idx][start_id].item(),
                                      p_end[idx][end_id].item(),
                                  )		)

               output.sort(key=lambda lowerCamelCase		: (tup[2] * tup[3]) ,    reverse=_lowerCAmelCase		)
               __A  :					str						     =       []
               for k in range(len(_lowerCAmelCase		)		):
                    if output[k][2] * output[k][3] >= thresh:
                         __A     ,__A  :					List[Any]						     =       output[k][0], output[k][1]
                         __A  :					Optional[Any]						     =       self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]		)
                         temp.append((decoded, output[k][2] * output[k][3])		)

               final_outputs.append(_lowerCAmelCase		)

          return final_outputs






'''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))

