





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


A__:    Optional[int]  	=	re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
A__:    Optional[int]  	=	10
A__:    Optional[int]  	=	256



def 				lowerCAmelCase_ (							A_):
    if len(A_) < MIN_NUM_TOKENS:
        return None
    UpperCamelCase__:	Optional[Any]					=     MinHash(num_perm=A_)
    for token in set(A_):
        min_hash.update(token.encode())
    return min_hash



def 				lowerCAmelCase_ (							A_):
    return {t for t in NON_ALPHA.split(A_) if len(t.strip()) > 0}




class        _a    :

      """simple docstring"""


      def __init__(		self:			Tuple	,    *,
          __lowerCamelCase:			float = 0.85	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     duplication_jaccard_threshold
          UpperCamelCase__:	str					=     NUM_PERM
          UpperCamelCase__:	Any					=     MinHashLSH(threshold=self._duplication_jaccard_threshold	,    num_perm=self._num_perm					)

          UpperCamelCase__:	str					=     defaultdict(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			MinHash					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     self._index.query(__lowerCamelCase					)
          if code_key in self._index.keys:
              print(F"Duplicate key {code_key}"					)
              return

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






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.get_duplicate_clusters()
          with open(__lowerCamelCase	,    "w"					) as f:
              json.dump(__lowerCamelCase	,    __lowerCamelCase					)



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__				, UpperCamelCase__:	List[Any]					=     element
    UpperCamelCase__:	Tuple					=     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 				lowerCAmelCase_ (							A_):
    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash    ,ThreadedIterator(A_    ,max_queue_size=1_00_00)    ,chunksize=1_00    ,):
            if data is not None:
                yield data



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	List[str]					=     DuplicationIndex(duplication_jaccard_threshold=A_)

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A_))    ,max_queue_size=1_00)):
        di.add(A_    ,A_)

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



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Any					=     get_tokens(A_)
    UpperCamelCase__:	Dict					=     get_tokens(A_)
    return len(tokensa & tokensa) / len(tokensa | tokensa)


A__:    Any  	=	None



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     []
    for elementa in cluster:
        UpperCamelCase__:	Tuple					=     _shared_dataset[elementa["base_index"]]["content"]
        for elementa in extremes:
            UpperCamelCase__:	Tuple					=     _shared_dataset[elementa["base_index"]]["content"]
            if jaccard_similarity(A_    ,A_) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            UpperCamelCase__:	str					=     1
            extremes.append(A_)
    return extremes



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    global _shared_dataset
    UpperCamelCase__:	Optional[int]					=     dataset
    UpperCamelCase__:	Dict					=     []
    UpperCamelCase__:	List[Any]					=     partial(_find_cluster_extremes_shared    ,jaccard_threshold=A_)
    with mp.Pool() as pool:
        for extremes in tqdm(
            pool.imap_unordered(
                A_    ,A_    ,)    ,total=len(A_)    ,):
            extremes_list.append(A_)
    return extremes_list




def 				lowerCAmelCase_ (							A_    ,A_ = 0.85):
    UpperCamelCase__:	Tuple					=     make_duplicate_clusters(A_    ,A_)
    UpperCamelCase__:	Dict					=     {x["base_index"] for cluster in duplicate_clusters for x in cluster}
    UpperCamelCase__:	List[Any]					=     {}
    UpperCamelCase__:	Dict					=     find_extremes(A_    ,A_    ,A_)
    for extremes in extremes_clusters:
        for element in extremes:
            UpperCamelCase__:	Any					=     element
    UpperCamelCase__:	Union[str, Any]					=     duplicate_indices - set(extreme_dict.keys())
    UpperCamelCase__:	Union[str, Any]					=     dataset.filter(lambda A_    ,A_: idx not in remove_indices    ,with_indices=A_)

    # update duplicate_clusters
    for cluster in duplicate_clusters:
        for element in cluster:
            UpperCamelCase__:	List[str]					=     element["base_index"] in extreme_dict
            if element["is_extreme"]:
                UpperCamelCase__:	Optional[Any]					=     extreme_dict[element["base_index"]]["copies"]

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

    return ds_filter, duplicate_clusters








from __future__ import annotations

from collections import namedtuple



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	List[str]					=     namedtuple("result"    ,"name value")
    if (voltage, current, power).count(0) != 1:
        raise ValueError("Only one argument must be 0")
    elif power < 0:
        raise ValueError(
            "Power cannot be negative in any electrical/electronics system")
    elif voltage == 0:
        return result("voltage"    ,power / current)
    elif current == 0:
        return result("current"    ,power / voltage)
    elif power == 0:
        return result("power"    ,float(round(abs(voltage * current)    ,2)))
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
   import doctest

   doctest.testmod()

