



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


_lowercase		:				Any      		=re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_lowercase		:				Union[str, Any]      		=10
_lowercase		:				Optional[Any]      		=256

def      lowerCAmelCase_					(  _lowercase    :       List[str])							->	Optional[MinHash]:







			"""simple docstring"""
			if len(a__) < MIN_NUM_TOKENS:
						return None
			a__       :						List[str]							=  MinHash(num_perm=a__)
			for token in set(a__):
						min_hash.update(token.encode())
			return min_hash

def      lowerCAmelCase_					(  _lowercase    :       str)							->	Set[str]:







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

class        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       *,
						    __lowercase = 0.8_5 ,       ) ->				Dict:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  duplication_jaccard_threshold
									a__       :						int							=  NUM_PERM
									a__       :						int							=  MinHashLSH(threshold=self._duplication_jaccard_threshold ,       num_perm=self._num_perm  )

									a__       :						Tuple							=  defaultdict(__UpperCamelCase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						Optional[int]							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[List[Dict]]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				None:
									"""simple docstring"""




									a__       :						Dict							=  self.get_duplicate_clusters()
									with open(__UpperCamelCase ,       """w"""  ) as f:
												json.dump(__UpperCamelCase ,       __UpperCamelCase  )




def      lowerCAmelCase_					(  _lowercase    :       List[str])							->	Optional[Any]:







			"""simple docstring"""
			a__	,						a__       :						int							=  element
			a__       :						int							=  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_					(  _lowercase    :       Type[Dataset])							->	int:







			"""simple docstring"""
			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash						,       ThreadedIterator(a__						,       max_queue_size=1_0000)						,       chunksize=100						,       ):
									if data is not None:
												yield data

def      lowerCAmelCase_					(  _lowercase    :       Type[Dataset]						,       _lowercase    :       float)							->	List[str]:







			"""simple docstring"""
			a__       :						str							=  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      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	float:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  get_tokens(a__)
			a__       :						Union[str, Any]							=  get_tokens(a__)
			return len(tokensa & tokensa) / len(tokensa | tokensa)


_lowercase		:				Optional[int]      		=None

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Any)							->	int:







			"""simple docstring"""
			a__       :						Optional[int]							=  []
			for elementa in cluster:
						a__       :						str							=  _shared_dataset[elementa["""base_index"""]]["""content"""]
						for elementa in extremes:
									a__       :						Optional[int]							=  _shared_dataset[elementa["""base_index"""]]["""content"""]
									if jaccard_similarity(a__						,       a__) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									a__       :						List[Any]							=  1
									extremes.append(a__)
			return extremes

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       Optional[int]						,       _lowercase    :       Optional[Any])							->	Union[str, Any]:







			"""simple docstring"""
			global _shared_dataset
			a__       :						int							=  dataset
			a__       :						Dict							=  []
			a__       :						Any							=  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      lowerCAmelCase_					(  _lowercase    :       Type[Dataset]						,       _lowercase    :       float = 0.85)							->	Tuple[Type[Dataset], List[List[Dict]]]:







			"""simple docstring"""
			a__       :						str							=  make_duplicate_clusters(a__						,       a__)
			a__       :						Optional[Any]							=  {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
			a__       :						List[Any]							=  {}
			a__       :						Union[str, Any]							=  find_extremes(a__						,       a__						,       a__)
			for extremes in extremes_clusters:
						for element in extremes:
									a__       :						str							=  element
			a__       :						str							=  duplicate_indices - set(extreme_dict.keys())
			a__       :						List[Any]							=  dataset.filter(lambda _lowercase						,       _lowercase: idx not in remove_indices						,       with_indices=a__)

			# update duplicate_clusters
			for cluster in duplicate_clusters:
						for element in cluster:
									a__       :						Tuple							=  element["""base_index"""] in extreme_dict
									if element["is_extreme"]:
												a__       :						List[str]							=  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









from __future__ import annotations

import requests

_lowercase		:				Dict      		=set(
    "approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports".split()
)

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       int = 1						,       _lowercase    :       str = "new"						,       _lowercase    :       list | None = None)							->	dict:







			"""simple docstring"""
			a__       :						Optional[Any]							=  wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(_lowercase) - valid_terms)):
						a__       :						Any							=  F'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(_lowercase)
			a__       :						int							=  requests.get(
			    F'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''						,       headers={"""User-agent""": """A random string"""}						,       )
			if response.status_code == 429:
						raise requests.HTTPError

			a__       :						int							=  response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(_lowercase)}

			a__       :						int							=  {}
			for id_ in range(_lowercase):
						a__       :						List[Any]							=  {
						    item: data["""data"""]["""children"""][id_]["""data"""][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
		# If you get Error 429, that means you are rate limited.Try after some time
		print(get_subreddit_data("learnpython", wanted_data=["title", "url", "selftext"]))




