






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					:		Any	  =						re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__UpperCamelCase					:		Tuple	  =						10
__UpperCamelCase					:		Union[str, Any]	  =						256
def      a_ (      _A  )			->			Optional[MinHash]:







				"""simple docstring"""
				if len(_A  ) < MIN_NUM_TOKENS:
								return None
				snake_case__									=     MinHash(num_perm=_A  )
				for token in set(_A  ):
								min_hash.update(token.encode()  )
				return min_hash
def      a_ (      _A  )			->			Set[str]:







				"""simple docstring"""
				return {t for t in NON_ALPHA.split(_A  ) if len(t.strip()  ) > 0}


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      int       ,	*,
							    UpperCamelCase:      float = 0.85       ,	)		->		List[Any]:
											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:      List[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      MinHash						)		->		None:
											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:      List[Any]						)		->		List[List[Dict]]:
											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:      Optional[Any]       ,	UpperCamelCase:      int						)		->		None:
											snake_case__									=     self.get_duplicate_clusters()
											with open(UpperCamelCase       ,	'w'						) as f:
															json.dump(UpperCamelCase       ,	UpperCamelCase						)


def      a_ (      _A  )			->			Union[str, Any]:







				"""simple docstring"""
				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_ (      _A  )			->			Any:







				"""simple docstring"""
				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash      ,   ThreadedIterator(_A      ,   max_queue_size=10000  )      ,   chunksize=100      ,   ):
												if data is not None:
																yield data
def      a_ (      _A      ,   _A  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     DuplicationIndex(duplication_jaccard_threshold=_A  )

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

				# Returns a List[Cluster] where Cluster is List[str] with the filenames.
				return di.get_duplicate_clusters()
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     get_tokens(_A  )
				snake_case__									=     get_tokens(_A  )
				return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


__UpperCamelCase					:		Union[str, Any]	  =						None
def      a_ (      _A      ,   _A  )			->			Dict:







				"""simple docstring"""
				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(_A      ,   _A  ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												snake_case__									=     1
												extremes.append(_A  )
				return extremes
def      a_ (      _A      ,   _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				global _shared_dataset
				snake_case__									=     dataset
				snake_case__									=     []
				snake_case__									=     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      a_ (      _A      ,   _A = 0.85  )			->			Tuple[Type[Dataset], List[List[Dict]]]:







				"""simple docstring"""
				snake_case__									=     make_duplicate_clusters(_A      ,   _A  )
				snake_case__									=     {x['base_index'] for cluster in duplicate_clusters for x in cluster}
				snake_case__									=     {}
				snake_case__									=     find_extremes(_A      ,   _A      ,   _A  )
				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 _A      ,   _A  : idx not in remove_indices      ,   with_indices=_A  )

				# 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(_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












def      a_ (      _A = 4000000  )			->			int:







				"""simple docstring"""
				snake_case__									=     [0, 1]
				snake_case__									=     0
				while fib[i] <= n:
								fib.append(fib[i] + fib[i + 1]  )
								if fib[i + 2] > n:
												break
								i += 1
				snake_case__									=     0
				for j in range(len(_A  ) - 1  ):
								if fib[j] % 2 == 0:
												total += fib[j]

				return total


if __name__ == "__main__":
							print(f'''{solution() = }''')




