




"""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


a  =				re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
a  =				1_0
a  =				2_5_6





def 				lowercase       (snake_case__ :			List[str]			)  ->	Optional[MinHash]:


				'''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 				lowercase       (snake_case__ :			str			)  ->	Set[str]:


				'''simple docstring'''






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






class    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      Any    ,    *,
						    lowerCAmelCase					:      Union[str, Any] = 0.85    ,    ):
										lowerCAmelCase						=					duplication_jaccard_threshold
										lowerCAmelCase						=					NUM_PERM
										lowerCAmelCase						=					MinHashLSH(threshold=self._duplication_jaccard_threshold    ,    num_perm=self._num_perm       )

										lowerCAmelCase						=					defaultdict(lowerCAmelCase__       )


						def 	__lowercase			(				self					:      Dict    ,    lowerCAmelCase					:      Optional[Any]    ,    lowerCAmelCase					:      Tuple       ):
										lowerCAmelCase						=					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 	__lowercase			(				self					:      Optional[int]       ):
										lowerCAmelCase						=					[]
										for base, duplicates in self._duplicate_clusters.items():
														lowerCAmelCase						=					[base] + list(lowerCAmelCase__       )
														# 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(lowerCAmelCase__       )
										return duplicate_clusters




						def 	__lowercase			(				self					:      List[str]    ,    lowerCAmelCase					:      Tuple       ):
										lowerCAmelCase						=					self.get_duplicate_clusters()
										with open(lowerCAmelCase__    ,    """w"""       ) as f:
														json.dump(lowerCAmelCase__    ,    lowerCAmelCase__       )





def 				lowercase       (snake_case__ :			Optional[int]			)  ->	str:


				'''simple docstring'''






				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 				lowercase       (snake_case__ :			Type[Dataset]			)  ->	Union[str, Any]:


				'''simple docstring'''






				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash   ,							ThreadedIterator(_lowercase   ,							max_queue_size=10_000			)   ,							chunksize=100   ,							):
												if data is not None:
																yield data





def 				lowercase       (snake_case__ :			Type[Dataset]   ,							snake_case__ :			float			)  ->	Optional[int]:


				'''simple docstring'''






				lowerCAmelCase						=					DuplicationIndex(duplication_jaccard_threshold=_lowercase			)

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

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





def 				lowercase       (snake_case__ :			str   ,							snake_case__ :			str			)  ->	float:


				'''simple docstring'''






				lowerCAmelCase						=					get_tokens(_lowercase			)
				lowerCAmelCase						=					get_tokens(_lowercase			)
				return len(tokensa & tokensa			) / len(tokensa | tokensa			)


a  =				None





def 				lowercase       (snake_case__ :			Union[str, Any]   ,							snake_case__ :			List[str]			)  ->	List[Any]:


				'''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 				lowercase       (snake_case__ :			Optional[Any]   ,							snake_case__ :			str   ,							snake_case__ :			Dict			)  ->	int:


				'''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 				lowercase       (snake_case__ :			Type[Dataset]   ,							snake_case__ :			float = 0.85			)  ->	Tuple[Type[Dataset], List[List[Dict]]]:


				'''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 snake_case__   ,							snake_case__			: 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












"""simple docstring"""




class 			__UpperCamelCase							:  # Public class to implement a graph






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								a :						int      =							row
								a :						Tuple      =							col
								a :						Optional[int]      =							graph






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					bool:
								return (
								    0 <= i < self.ROW
								    and 0 <= j < self.COL
								    and not visited[i][j]
								    and self.graph[i][j]
								)






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								# Checking all 8 elements surrounding nth element
								a :						Optional[Any]      =							[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
								a :						str      =							[-1, 0, 1, -1, 1, -1, 0, 1]
								a :						str      =							True  # Make those cells visited
								for k in range(8 ):
									if self.is_safe(i + row_nbr[k] ,   j + col_nbr[k] ,   lowerCAmelCase__ ):
										self.diffs(i + row_nbr[k] ,   j + col_nbr[k] ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					int:  # And finally, count all islands.
								a :						Union[str, Any]      =							[[False for j in range(self.COL )] for i in range(self.ROW )]
								a :						Dict      =							0
								for i in range(self.ROW ):
									for j in range(self.COL ):
										if visited[i][j] is False and self.graph[i][j] == 1:
											self.diffs(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )
											count += 1
								return count


