'''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


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





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	if len(UpperCamelCase__       ) < MIN_NUM_TOKENS:
		return None
	__UpperCAmelCase							=			MinHash(num_perm=UpperCamelCase__       )
	for token in set(UpperCamelCase__       ):
		min_hash.update(token.encode()       )
	return min_hash





def       lowerCAmelCase					(		UpperCamelCase__				:   str       ):


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






class 						A   :





							def __init__(      self				:							List[Any]     ,		*,
							    __a				:							float = 0.8_5     ,		)							->	Optional[int]:
								__UpperCAmelCase							=			duplication_jaccard_threshold
								__UpperCAmelCase							=			NUM_PERM
								__UpperCAmelCase							=			MinHashLSH(threshold=self._duplication_jaccard_threshold     ,		num_perm=self._num_perm	)

								__UpperCAmelCase							=			defaultdict(__a	)





							def       snake_case__						(      self				:							Dict     ,		__a				:							Tuple     ,		__a				:							MinHash	)							->	None:
								__UpperCAmelCase							=			self._index.query(__a	)
								if code_key in self._index.keys:
									print(f"""Duplicate key {code_key}"""	)
									return

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





							def       snake_case__						(      self				:							str	)							->	List[List[Dict]]:
								__UpperCAmelCase							=			[]
								for base, duplicates in self._duplicate_clusters.items():
									__UpperCAmelCase							=			[base] + list(__a	)
									# reformat the cluster to be a list of dict
									__UpperCAmelCase							=			[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
									duplicate_clusters.append(__a	)
								return duplicate_clusters






							def       snake_case__						(      self				:							int     ,		__a				:							str	)							->	None:
								__UpperCAmelCase							=			self.get_duplicate_clusters()
								with open(__a     ,		'''w'''	) as f:
									json.dump(__a     ,		__a	)





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase				,			__UpperCAmelCase							=			element
	__UpperCAmelCase							=			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					(		UpperCamelCase__				:   Type[Dataset]       ):


	"""simple docstring"""
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash    ,					ThreadedIterator(UpperCamelCase__    ,					max_queue_size=1_0_0_0_0       )    ,					chunksize=1_0_0    ,					):
			if data is not None:
				yield data





def       lowerCAmelCase					(		UpperCamelCase__				:   Type[Dataset]    ,					UpperCamelCase__				:   float       ):


	"""simple docstring"""
	__UpperCAmelCase							=			DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase__       )

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

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





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_tokens(UpperCamelCase__       )
	__UpperCAmelCase							=			get_tokens(UpperCamelCase__       )
	return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


__lowerCAmelCase	:       str						       =	None





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	for elementa in cluster:
		__UpperCAmelCase							=			_shared_dataset[elementa['''base_index''']]['''content''']
		for elementa in extremes:
			__UpperCAmelCase							=			_shared_dataset[elementa['''base_index''']]['''content''']
			if jaccard_similarity(UpperCamelCase__    ,					UpperCamelCase__       ) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			__UpperCAmelCase							=			1
			extremes.append(UpperCamelCase__       )
	return extremes





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	global _shared_dataset
	__UpperCAmelCase							=			dataset
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			partial(_find_cluster_extremes_shared    ,					jaccard_threshold=UpperCamelCase__       )
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        UpperCamelCase__    ,					UpperCamelCase__    ,					)    ,					total=len(UpperCamelCase__       )    ,					):
			extremes_list.append(UpperCamelCase__       )
	return extremes_list





def       lowerCAmelCase					(		UpperCamelCase__				:   Type[Dataset]    ,					UpperCamelCase__				:   float = 0.85       ):


	"""simple docstring"""
	__UpperCAmelCase							=			make_duplicate_clusters(UpperCamelCase__    ,					UpperCamelCase__       )
	__UpperCAmelCase							=			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
	__UpperCAmelCase							=			{}
	__UpperCAmelCase							=			find_extremes(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	for extremes in extremes_clusters:
		for element in extremes:
			__UpperCAmelCase							=			element
	__UpperCAmelCase							=			duplicate_indices - set(extreme_dict.keys()       )
	__UpperCAmelCase							=			dataset.filter(lambda UpperCamelCase__    ,					UpperCamelCase__       : idx not in remove_indices    ,					with_indices=UpperCamelCase__       )

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

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

	return ds_filter, duplicate_clusters






'''simple docstring'''


import requests
from bsa import BeautifulSoup





def       lowerCAmelCase					(		UpperCamelCase__				:   str = "AAPL"       ):


	"""simple docstring"""
	__UpperCAmelCase							=			f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
	__UpperCAmelCase							=			BeautifulSoup(requests.get(UpperCamelCase__       ).text    ,					'''html.parser'''       )
	__UpperCAmelCase							=			'''My(6px) Pos(r) smartphone_Mt(6px)'''
	return soup.find('''div'''    ,					class_=class_       ).find('''span'''       ).text


if __name__ == "__main__":
					for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
										print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
