


'''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
a__    :   List[Any]								=			{
    '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 _UpperCamelCase			(	__A       )				->						set[tuple[str, str]]:


      '''simple docstring'''



      UpperCamelCase__      =							{node: {node} for node in graph}

      UpperCamelCase__      =							{node: graph[node][:] for node in graph}

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

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

            UpperCamelCase__      =							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(__A       )
                  if v in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(__A       )

    # Find cutset.
      UpperCamelCase__      =							[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))









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





