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






def 	a_		(	__lowercase       :     List[str]			)			->  Optional[MinHash]:
			if len(__lowercase			) < MIN_NUM_TOKENS:
						return None
			_snake_case          =      MinHash(num_perm=__lowercase			)
			for token in set(__lowercase			):
						min_hash.update(token.encode()			)
			return min_hash






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




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   Optional[Any]      ,  *,
	    lowercase     :   float = 0.85      ,  ):


				'''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(lowercase     )



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   Tuple      ,  lowercase     :   MinHash     ):


				'''simple docstring'''


				_snake_case          =      self._index.query(lowercase     )
				if code_key in self._index.keys:
							print(f'''Duplicate key {code_key}'''     )
							return

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



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      []
				for base, duplicates in self._duplicate_clusters.items():
							_snake_case          =      [base] + list(lowercase     )
							# 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(lowercase     )
				return duplicate_clusters





	def 			A							(  self     :   Any      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


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






def 	a_		(	__lowercase       :     Any			)			->  List[Any]:
			_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_		(	__lowercase       :     Type[Dataset]			)			->  Optional[int]:
			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 	a_		(	__lowercase       :     Type[Dataset]	, __lowercase       :     float			)			->  str:
			_snake_case          =      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 	a_		(	__lowercase       :     str	, __lowercase       :     str			)			->  float:
			_snake_case          =      get_tokens(__lowercase			)
			_snake_case          =      get_tokens(__lowercase			)
			return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCamelCase	:    List[str]          =      None






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     Optional[int]			)			->  Union[str, 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(__lowercase	, __lowercase			) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									_snake_case          =      1
									extremes.append(__lowercase			)
			return extremes






def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Any	, __lowercase       :     Any			)			->  str:
			global _shared_dataset
			_snake_case          =      dataset
			_snake_case          =      []
			_snake_case          =      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 	a_		(	__lowercase       :     Type[Dataset]	, __lowercase       :     float = 0.8_5			)			->  Tuple[Type[Dataset], List[List[Dict]]]:
			_snake_case          =      make_duplicate_clusters(__lowercase	, __lowercase			)
			_snake_case          =      {x['base_index'] for cluster in duplicate_clusters for x in cluster}
			_snake_case          =      {}
			_snake_case          =      find_extremes(__lowercase	, __lowercase	, __lowercase			)
			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 __lowercase	, __lowercase			: idx not in remove_indices	, with_indices=__lowercase			)

			# 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(__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

import baseaa






def 	a_		(	__lowercase       :     str			)			->  bytes:
			return baseaa.aaaencode(string.encode('utf-8'			)			)






def 	a_		(	__lowercase       :     bytes			)			->  str:
			return baseaa.aaadecode(__lowercase			).decode('utf-8'			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()