




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


lowerCamelCase    :		Tuple			   =       re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowerCamelCase    :		Any			   =       10
lowerCamelCase    :		Union[str, Any]			   =       256

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



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







class   A:



 '''simple docstring'''


 def __init__(    self :  Optional[int]						,							*,
     A_ :  float = 0.85						,							)       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   duplication_jaccard_threshold
       lowerCamelCase_							=   NUM_PERM
       lowerCamelCase_							=   MinHashLSH(threshold=self._duplication_jaccard_threshold						,							num_perm=self._num_perm  )

       lowerCamelCase_							=   defaultdict(A_  )

 def     a__			(    self :  Optional[int]						,							A_ :  Tuple						,							A_ :  MinHash  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   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     a__			(    self :  Union[str, Any]  )       ->     List[List[Dict]]:

       """simple docstring"""






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





 def     a__			(    self :  Optional[Any]						,							A_ :  Optional[Any]  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   self.get_duplicate_clusters()
       with open(A_						,							'w'  ) as f:
             json.dump(A_						,							A_  )

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   element
      lowerCamelCase_							=   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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Type[Dataset]					):


      '''simple docstring'''



      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash     ,							ThreadedIterator(lowercase     ,							max_queue_size=1_00_00					)     ,							chunksize=1_00     ,							):
                  if data is not None:
                        yield data

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Type[Dataset]     ,							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   DuplicationIndex(duplication_jaccard_threshold=lowercase					)

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   get_tokens(lowercase					)
      lowerCamelCase_							=   get_tokens(lowercase					)
      return len(tokensa & tokensa					) / len(tokensa | tokensa					)


lowerCamelCase    :		Optional[int]			   =       None

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	int     ,							lowercase     :	List[str]					):


      '''simple docstring'''



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






def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Type[Dataset]     ,							lowercase     :	float = 0.85					):


      '''simple docstring'''



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

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

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

      return ds_filter, duplicate_clusters









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



