


'''simple docstring'''


import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=100					,				SCREAMING_SNAKE_CASE=" "       ):




     '''simple docstring'''





     __A  :					List[Any]						     =       text.split(SCREAMING_SNAKE_CASE       )
     return [character.join(text[i : i + n]       ).strip() for i in range(0					,				len(SCREAMING_SNAKE_CASE       )					,				SCREAMING_SNAKE_CASE       )]


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A     ,__A  :					List[Any]						     =       [], []
     for title, text in zip(documents["title"]					,				documents["text"]       ):
          if text is not None:
               for passage in split_text(SCREAMING_SNAKE_CASE       ):
                    titles.append(title if title is not None else ""       )
                    texts.append(SCREAMING_SNAKE_CASE       )
     return {"title": titles, "text": texts}


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					str						     =       load_dataset(
         "csv"					,				data_files=[csv_path]					,				split="train"					,				delimiter="\t"					,				column_names=["title", "text"]       )
     __A  :					Optional[Any]						     =       kb_dataset.map(
         SCREAMING_SNAKE_CASE					,				batched=SCREAMING_SNAKE_CASE					,				num_proc=1       )  # if you want you can load already splitted csv.
     __A  :					int						     =       [kb_dataset.shard(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				contiguous=SCREAMING_SNAKE_CASE       ) for i in range(SCREAMING_SNAKE_CASE       )]
     __A  :					List[Any]						     =       kb_list[process_num]

     __A  :					str						     =       "data_" + str(SCREAMING_SNAKE_CASE       )
     __A  :					List[str]						     =       os.path.join(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )

     __A  :					Dict						     =       DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"       )
     __A  :					Any						     =       ctx_encoder.to(device=SCREAMING_SNAKE_CASE       )

     def embed(
         SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ) -> dict:
          __A  :					Dict						     =       ctx_tokenizer(
              documents["title"]					,				documents["text"]					,				truncation=SCREAMING_SNAKE_CASE					,				padding="longest"					,				return_tensors="pt"       )["input_ids"]
          __A  :					Any						     =       ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE       )					,				return_dict=SCREAMING_SNAKE_CASE       ).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     __A  :					str						     =       Features(
         {"text": Value("string"       ), "title": Value("string"       ), "embeddings": Sequence(Value("float32"       )       )}       )  # optional, save as float32 instead of float64 to save space

     __A  :					Tuple						     =       data_shrad.map(
         partial(SCREAMING_SNAKE_CASE					,				ctx_encoder=SCREAMING_SNAKE_CASE					,				ctx_tokenizer=SCREAMING_SNAKE_CASE					,				device=SCREAMING_SNAKE_CASE       )					,				batched=SCREAMING_SNAKE_CASE					,				batch_size=16					,				features=SCREAMING_SNAKE_CASE					,				)
     dataset.save_to_disk(SCREAMING_SNAKE_CASE       )






def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					str						     =       []

     for shard_address in glob(str(SCREAMING_SNAKE_CASE       ) + "/*/"       ):
          data_shard_list.append(load_from_disk(SCREAMING_SNAKE_CASE       )       )

     __A  :					int						     =       concatenate_datasets(SCREAMING_SNAKE_CASE       )
     faiss.omp_set_num_threads(96       )

     __A  :					List[str]						     =       faiss.IndexHNSWFlat(768					,				128					,				faiss.METRIC_INNER_PRODUCT       )
     concat.add_faiss_index("embeddings"					,				custom_index=SCREAMING_SNAKE_CASE       )
     concat.get_index("embeddings"       ).save(
         SCREAMING_SNAKE_CASE       )  # since we load the index in to memory,we can directly update the index in the disk





'''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))

