






"""simple docstring"""







import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_UpperCamelCase					      =  re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_UpperCamelCase					      =  10
_UpperCamelCase					      =  256

def _a			(		_snake_case							):







      """simple docstring"""






      if len(_snake_case							) < MIN_NUM_TOKENS:
            return None
      UpperCAmelCase          =	MinHash(num_perm=_snake_case							)
      for token in set(_snake_case							):
            min_hash.update(token.encode()							)
      return min_hash

def _a			(		_snake_case							):







      """simple docstring"""






      return {t for t in NON_ALPHA.split(_snake_case							) if len(t.strip()							) > 0}







class  lowerCamelCase__ :






    def __init__(			self     ,*,
        A = 0.85     ,):
          UpperCAmelCase          =	duplication_jaccard_threshold
          UpperCAmelCase          =	NUM_PERM
          UpperCAmelCase          =	MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm )

          UpperCAmelCase          =	defaultdict(A )






    def     _UpperCamelCase							(			self     ,A     ,A ):
          UpperCAmelCase          =	self._index.query(A )
          if code_key in self._index.keys:
                print(F'''Duplicate key {code_key}''' )
                return

          self._index.insert(A     ,A )
          if len(A ) > 0:
                for base_duplicate in close_duplicates:
                      if base_duplicate in self._duplicate_clusters:
                            self._duplicate_clusters[base_duplicate].add(A )
                            break
                else:
                      self._duplicate_clusters[close_duplicates[0]].add(A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	[]
          for base, duplicates in self._duplicate_clusters.items():
                UpperCAmelCase          =	[base] + list(A )
                # reformat the cluster to be a list of dict
                UpperCAmelCase          =	[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
                duplicate_clusters.append(A )
          return duplicate_clusters






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.get_duplicate_clusters()
          with open(A     ,"""w""" ) as f:
                json.dump(A     ,A )







def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	element
      UpperCAmelCase          =	get_min_hash([t for t in NON_ALPHA.split(data["""content"""]							) if len(t.strip()							) > 0]							)
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash

def _a			(		_snake_case							):







      """simple docstring"""






      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash				,	ThreadedIterator(_snake_case				,	max_queue_size=1_0000							)				,	chunksize=100				,	):
                  if data is not None:
                        yield data

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	DuplicationIndex(duplication_jaccard_threshold=_snake_case							)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_snake_case							)							)				,	max_queue_size=100							)							):
            di.add(_snake_case				,	_snake_case							)

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	get_tokens(_snake_case							)
      UpperCAmelCase          =	get_tokens(_snake_case							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_UpperCamelCase					      =  None

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      for elementa in cluster:
            UpperCAmelCase          =	_shared_dataset[elementa["""base_index"""]]["""content"""]
            for elementa in extremes:
                  UpperCAmelCase          =	_shared_dataset[elementa["""base_index"""]]["""content"""]
                  if jaccard_similarity(_snake_case				,	_snake_case							) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  UpperCAmelCase          =	1
                  extremes.append(_snake_case							)
      return extremes

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      global _shared_dataset
      UpperCAmelCase          =	dataset
      UpperCAmelCase          =	[]
      UpperCAmelCase          =	partial(_find_cluster_extremes_shared				,	jaccard_threshold=_snake_case							)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _snake_case				,	_snake_case				,	)				,	total=len(_snake_case							)				,	):
                  extremes_list.append(_snake_case							)
      return extremes_list






def _a			(		_snake_case				,	_snake_case = 0.85							):







      """simple docstring"""






      UpperCAmelCase          =	make_duplicate_clusters(_snake_case				,	_snake_case							)
      UpperCAmelCase          =	{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
      UpperCAmelCase          =	{}
      UpperCAmelCase          =	find_extremes(_snake_case				,	_snake_case				,	_snake_case							)
      for extremes in extremes_clusters:
            for element in extremes:
                  UpperCAmelCase          =	element
      UpperCAmelCase          =	duplicate_indices - set(extreme_dict.keys()							)
      UpperCAmelCase          =	dataset.filter(lambda _snake_case				,	_snake_case							: idx not in remove_indices				,	with_indices=_snake_case							)

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  UpperCAmelCase          =	element["""base_index"""] in extreme_dict
                  if element["is_extreme"]:
                        UpperCAmelCase          =	extreme_dict[element["""base_index"""]]["""copies"""]

      print(F'''Original dataset size: {len(_snake_case							)}'''							)
      print(F'''Number of duplicate clusters: {len(_snake_case							)}'''							)
      print(F'''Files in duplicate cluster: {len(_snake_case							)}'''							)
      print(F'''Unique files in duplicate cluster: {len(_snake_case							)}'''							)
      print(F'''Filtered dataset size: {len(_snake_case							)}'''							)

      return ds_filter, duplicate_clusters











"""simple docstring"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if (voltage, current, resistance).count(0							) != 1:
            raise ValueError("""One and only one argument must be 0"""							)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""							)
      if voltage == 0:
            return {"voltage": float(current * resistance							)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



