





'''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
lowerCAmelCase      :  Optional[int]      							=       {
    '''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_						(		__lowerCamelCase      :      dict[str, list[str]]				):
   lowercase_    :Union[str, Any]					    =				{node: {node} for node in graph}

   lowercase_    :Optional[int]					    =				{node: graph[node][:] for node in graph}

   while len(__lowerCamelCase				) > 2:
      # Choose a random edge.
      lowercase_    :int					    =				random.choice(list(graph_copy.keys()				)				)
      lowercase_    :List[str]					    =				random.choice(graph_copy[u]				)

      # Contract edge (u, v) to new node uv
      lowercase_    :Optional[int]					    =				u + v
      lowercase_    :List[Any]					    =				list(set(graph_copy[u] + graph_copy[v]				)				)
      uv_neighbors.remove(__lowerCamelCase				)
      uv_neighbors.remove(__lowerCamelCase				)
      lowercase_    :Any					    =				uv_neighbors
      for neighbor in uv_neighbors:
         graph_copy[neighbor].append(__lowerCamelCase				)

      lowercase_    :Optional[int]					    =				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(__lowerCamelCase				)
         if v in graph_copy[neighbor]:
            graph_copy[neighbor].remove(__lowerCamelCase				)

    # Find cutset.
   lowercase_    :List[str]					    =				[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 multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      Dict			,   lowercase	:      NestedDataStructureLike[PathLike]			,   lowercase	:      Optional[NamedSplit] = None			,   lowercase	:      Optional[Features] = None			,   lowercase	:      str = None			,   lowercase	:      bool = False			,   lowercase	:      bool = False			,   lowercase	:      Optional[int] = None			,   **lowercase	:      Any			,   ):





        """simple docstring"""




        super().__init__(
            lowercase			,   split=lowercase			,   features=lowercase			,   cache_dir=lowercase			,   keep_in_memory=lowercase			,   streaming=lowercase			,   num_proc=lowercase			,   **lowercase			,   )
        lowercase_    :Any					    =				path_or_paths if isinstance(lowercase			,   lowercase		) else {self.split: path_or_paths}
        lowercase_    :Any					    =				Csv(
            cache_dir=lowercase			,   data_files=lowercase			,   features=lowercase			,   **lowercase			,   )







     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        if self.streaming:
           lowercase_    :Union[str, Any]					    =				self.builder.as_streaming_dataset(split=self.split		)
        # Build regular (map-style) dataset
        else:
           lowercase_    :Dict					    =				None
           lowercase_    :List[str]					    =				None
           lowercase_    :List[Any]					    =				None
           lowercase_    :Union[str, Any]					    =				None

           self.builder.download_and_prepare(
               download_config=lowercase			,   download_mode=lowercase			,   verification_mode=lowercase			,   base_path=lowercase			,   num_proc=self.num_proc			,   )
           lowercase_    :int					    =				self.builder.as_dataset(
               split=self.split			,   verification_mode=lowercase			,   in_memory=self.keep_in_memory		)
        return dataset







class    a_     :





     def __init__(				self	:      Dict			,   lowercase	:      Dataset			,   lowercase	:      Union[PathLike, BinaryIO]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[int] = None			,   **lowercase	:      Any			,   ):





        """simple docstring"""




        if num_proc is not None and num_proc <= 0:
           raise ValueError(F'num_proc {num_proc} must be an integer > 0.'		)

        lowercase_    :str					    =				dataset
        lowercase_    :List[Any]					    =				path_or_buf
        lowercase_    :Any					    =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        lowercase_    :int					    =				num_proc
        lowercase_    :Optional[Any]					    =				"utf-8"
        lowercase_    :Dict					    =				to_csv_kwargs





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :int					    =				self.to_csv_kwargs.pop("path_or_buf"			,   lowercase		)
        lowercase_    :List[str]					    =				self.to_csv_kwargs.pop("header"			,   lowercase		)
        lowercase_    :Optional[Any]					    =				self.to_csv_kwargs.pop("index"			,   lowercase		)

        if isinstance(self.path_or_buf			,   (str, bytes, os.PathLike)		):
           with open(self.path_or_buf			,   "wb+"		) as buffer:
              lowercase_    :List[Any]					    =				self._write(file_obj=lowercase			,   header=lowercase			,   index=lowercase			,   **self.to_csv_kwargs		)
        else:
           lowercase_    :Any					    =				self._write(file_obj=self.path_or_buf			,   header=lowercase			,   index=lowercase			,   **self.to_csv_kwargs		)
        return written





     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_    :Optional[Any]					    =				args

        lowercase_    :Dict					    =				query_table(
            table=self.dataset.data			,   key=slice(lowercase			,   offset + self.batch_size		)			,   indices=self.dataset._indices			,   )
        lowercase_    :List[str]					    =				batch.to_pandas().to_csv(
            path_or_buf=lowercase			,   header=header if (offset == 0) else False			,   index=lowercase			,   **lowercase		)
        return csv_str.encode(self.encoding		)







     def 							lowercase__   (				self	:      Dict			,   lowercase	:      BinaryIO			,   lowercase	:      Dict			,   lowercase	:      int			,   **lowercase	:      Any		):





        """simple docstring"""




        lowercase_    :Dict					    =				0

        if self.num_proc is None or self.num_proc == 1:
           for offset in logging.tqdm(
               range(0			,   len(self.dataset		)			,   self.batch_size		)			,   unit="ba"			,   disable=not logging.is_progress_bar_enabled()			,   desc="Creating CSV from Arrow format"			,   ):
              lowercase_    :Optional[Any]					    =				self._batch_csv((offset, header, index, to_csv_kwargs)		)
              written += file_obj.write(lowercase		)

        else:
           lowercase_    ,							lowercase_    :Optional[int]					    =				len(self.dataset		), self.batch_size
           with multiprocessing.Pool(self.num_proc		) as pool:
              for csv_str in logging.tqdm(
                  pool.imap(
                      self._batch_csv			,   [(offset, header, index, to_csv_kwargs) for offset in range(0			,   lowercase			,   lowercase		)]			,   )			,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,   unit="ba"			,   disable=not logging.is_progress_bar_enabled()			,   desc="Creating CSV from Arrow format"			,   ):
                 written += file_obj.write(lowercase		)

        return written


