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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return {t for t in NON_ALPHA.split(lowerCamelCase__    ) if len(t.strip()    ) > 0}




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  *,
	    lowercase = 0.8_5						,  )				->       int:
					lowerCamelCase_						=			duplication_jaccard_threshold
					lowerCamelCase_						=			NUM_PERM
					lowerCamelCase_						=			MinHashLSH(threshold=self._duplication_jaccard_threshold						,  num_perm=self._num_perm )

					lowerCamelCase_						=			defaultdict(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       None:
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       List[List[Dict]]:
					lowerCamelCase_						=			[]
					for base, duplicates in self._duplicate_clusters.items():
									lowerCamelCase_						=			[base] + list(lowercase )
									# 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(lowercase )
					return duplicate_clusters



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.get_duplicate_clusters()
					with open(lowercase						,  "w" ) as f:
									json.dump(lowercase						,  lowercase )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				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 			lowerCamelCase_ (				lowerCamelCase__    ):
				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash	, ThreadedIterator(lowerCamelCase__	, max_queue_size=1_0_0_0_0    )	, chunksize=1_0_0	, ):
												if data is not None:
																yield data





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase__    )

				for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCamelCase__    )    )	, max_queue_size=1_0_0    )    ):
								di.add(lowerCamelCase__	, lowerCamelCase__    )

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			get_tokens(lowerCamelCase__    )
				lowerCamelCase_						=			get_tokens(lowerCamelCase__    )
				return len(tokensa & tokensa    ) / len(tokensa | tokensa    )


__A				=None





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				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(lowerCamelCase__	, lowerCamelCase__    ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												lowerCamelCase_						=			1
												extremes.append(lowerCamelCase__    )
				return extremes





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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 0.85    ):
				lowerCamelCase_						=			make_duplicate_clusters(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_						=			{x["base_index"] for cluster in duplicate_clusters for x in cluster}
				lowerCamelCase_						=			{}
				lowerCamelCase_						=			find_extremes(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
				for extremes in extremes_clusters:
								for element in extremes:
												lowerCamelCase_						=			element
				lowerCamelCase_						=			duplicate_indices - set(extreme_dict.keys()    )
				lowerCamelCase_						=			dataset.filter(lambda lowerCamelCase__	, lowerCamelCase__    : idx not in remove_indices	, with_indices=lowerCamelCase__    )

				# 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(lowerCamelCase__    )}'    )
				print(F'Number of duplicate clusters: {len(lowerCamelCase__    )}'    )
				print(F'Files in duplicate cluster: {len(lowerCamelCase__    )}'    )
				print(F'Unique files in duplicate cluster: {len(lowerCamelCase__    )}'    )
				print(F'Filtered dataset size: {len(lowerCamelCase__    )}'    )

				return ds_filter, duplicate_clusters









a__:      Dict									=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

a__:      str									=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[]

       for neighbour in graph[vert]:
              if not visited[neighbour]:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       order.append(UpperCamelCase__  )

       return order


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       list[bool]  )->list[int]:
       A__											=						True
       A__											=						[vert]

       for neighbour in reversed_graph[vert]:
              if not visited[neighbour]:
                     component += find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       return component


def 	UpperCamelCase__(       UpperCamelCase__			:       dict[int, list[int]]  )->list[list[int]]:
       A__											=						len(UpperCamelCase__  ) * [False]
       A__											=						{vert: [] for vert in range(len(UpperCamelCase__  )  )}

       for vert, neighbours in graph.items():
              for neighbour in neighbours:
                     reversed_graph[neighbour].append(UpperCamelCase__  )

       A__											=						[]
       for i, was_visited in enumerate(UpperCamelCase__  ):
              if not was_visited:
                     order += topology_sort(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       A__											=						[]
       A__											=						len(UpperCamelCase__  ) * [False]

       for i in range(len(UpperCamelCase__  )  ):
              A__											=						order[len(UpperCamelCase__  ) - i - 1]
              if not visited[vert]:
                     A__											=						find_components(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     components_list.append(UpperCamelCase__  )

       return components_list





