


"""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


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







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[MinHash]:
							if len(_SCREAMING_SNAKE_CASE			) < MIN_NUM_TOKENS:
														return None
							snake_case_          =						MinHash(num_perm=_SCREAMING_SNAKE_CASE			)
							for token in set(_SCREAMING_SNAKE_CASE			):
														min_hash.update(token.encode()			)
							return min_hash







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Set[str]:
							return {t for t in NON_ALPHA.split(_SCREAMING_SNAKE_CASE			) if len(t.strip()			) > 0}





class        __A       :





							'''simple docstring'''

							def __init__(			self							: Tuple						,    *,
							    UpperCAmelCase_							: float = 0.85						,    )   ->Any:



														"""simple docstring"""



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

														snake_case_          =						defaultdict(UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Any						,    UpperCAmelCase_							: Tuple						,    UpperCAmelCase_							: MinHash    )   ->None:



														"""simple docstring"""



														snake_case_          =						self._index.query(UpperCAmelCase_    )
														if code_key in self._index.keys:
																					print(F"""Duplicate key {code_key}"""    )
																					return

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



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[List[Dict]]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: Optional[Any]    )   ->None:



														"""simple docstring"""



														snake_case_          =						self.get_duplicate_clusters()
														with open(UpperCAmelCase_						,    """w"""    ) as f:
																					json.dump(UpperCAmelCase_						,    UpperCAmelCase_    )







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							snake_case_    ,  snake_case_          =						element
							snake_case_          =						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			( _SCREAMING_SNAKE_CASE			)      ->			Dict:
							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash			,	ThreadedIterator(_SCREAMING_SNAKE_CASE			,	max_queue_size=10_000			)			,	chunksize=100			,	):
																					if data is not None:
																												yield data







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						DuplicationIndex(duplication_jaccard_threshold=_SCREAMING_SNAKE_CASE			)

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

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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			float:
							snake_case_          =						get_tokens(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						get_tokens(_SCREAMING_SNAKE_CASE			)
							return len(tokensa & tokensa			) / len(tokensa | tokensa			)


__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      None







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							snake_case_          =						[]
							for elementa in cluster:
														snake_case_          =						_shared_dataset[elementa["""base_index"""]]["""content"""]
														for elementa in extremes:
																					snake_case_          =						_shared_dataset[elementa["""base_index"""]]["""content"""]
																					if jaccard_similarity(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					snake_case_          =						1
																					extremes.append(_SCREAMING_SNAKE_CASE			)
							return extremes







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Dict:
							global _shared_dataset
							snake_case_          =						dataset
							snake_case_          =						[]
							snake_case_          =						partial(_find_cluster_extremes_shared			,	jaccard_threshold=_SCREAMING_SNAKE_CASE			)
							with mp.Pool() as pool:
														for extremes in tqdm(
														    pool.imap_unordered(
														        _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	)			,	total=len(_SCREAMING_SNAKE_CASE			)			,	):
																					extremes_list.append(_SCREAMING_SNAKE_CASE			)
							return extremes_list







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0.85			)      ->			Tuple[Type[Dataset], List[List[Dict]]]:
							snake_case_          =						make_duplicate_clusters(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							snake_case_          =						{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
							snake_case_          =						{}
							snake_case_          =						find_extremes(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							for extremes in extremes_clusters:
														for element in extremes:
																					snake_case_          =						element
							snake_case_          =						duplicate_indices - set(extreme_dict.keys()			)
							snake_case_          =						dataset.filter(lambda _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			: idx not in remove_indices			,	with_indices=_SCREAMING_SNAKE_CASE			)

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

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

							return ds_filter, duplicate_clusters







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list:

							if len(_SCREAMING_SNAKE_CASE			) <= 1:
														return [tuple(_SCREAMING_SNAKE_CASE			)]

							snake_case_          =						[]

							def generate(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														if k == 1:
																					res.append(tuple(arr[:]			)			)
																					return

														generate(k - 1			,	_SCREAMING_SNAKE_CASE			)

														for i in range(k - 1			):
																					if k % 2 == 0:  # k is even
																												snake_case_    ,  snake_case_          =						arr[k - 1], arr[i]
																					else:  # k is odd
																												snake_case_    ,  snake_case_          =						arr[k - 1], arr[0]
																					generate(k - 1			,	_SCREAMING_SNAKE_CASE			)

							generate(len(_SCREAMING_SNAKE_CASE			)			,	_SCREAMING_SNAKE_CASE			)
							return res


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      List[Any]						=      input('Enter numbers separated by a comma:\n').strip()
				__SCREAMING_SNAKE_CASE							:      str						=      [int(item) for item in user_input.split(',')]
				print(heaps(arr))



