



from math import pow, sqrt






def     a(      *A       :    float       )		->       bool:
					"""simple docstring"""





					a					     =   len(A       ) > 0 and all(value > 0.0 for value in values       )
					return result






def     a(      A       :    float ,     A       :    float       )		->       float | ValueError:
					"""simple docstring"""





					return (
					    round(sqrt(molar_mass_a / molar_mass_a       ) ,     6       )
					    if validate(A ,     A       )
					    else ValueError("Input Error: Molar mass values must greater than 0."       )
					)






def     a(      A       :    float ,     A       :    float ,     A       :    float       )		->       float | ValueError:
					"""simple docstring"""





					return (
					    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a       ) ,     6       )
					    if validate(A ,     A ,     A       )
					    else ValueError(
					        "Input Error: Molar mass and effusion rate values must greater than 0."       )
					)






def     a(      A       :    float ,     A       :    float ,     A       :    float       )		->       float | ValueError:
					"""simple docstring"""





					return (
					    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a       ) ,     6       )
					    if validate(A ,     A ,     A       )
					    else ValueError(
					        "Input Error: Molar mass and effusion rate values must greater than 0."       )
					)






def     a(      A       :    float ,     A       :    float ,     A       :    float       )		->       float | ValueError:
					"""simple docstring"""





					return (
					    round(molar_mass / pow(effusion_rate_a / effusion_rate_a ,     2       ) ,     6       )
					    if validate(A ,     A ,     A       )
					    else ValueError(
					        "Input Error: Molar mass and effusion rate values must greater than 0."       )
					)







def     a(      A       :    float ,     A       :    float ,     A       :    float       )		->       float | ValueError:
					"""simple docstring"""





					return (
					    round(pow(effusion_rate_a / effusion_rate_a ,     2       ) / molar_mass ,     6       )
					    if validate(A ,     A ,     A       )
					    else ValueError(
					        "Input Error: Molar mass and effusion rate values must greater than 0."       )
					)





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






def     a(      A       :    List[str]       )		->       Optional[MinHash]:
					"""simple docstring"""





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






def     a(      A       :    str       )		->       Set[str]:
					"""simple docstring"""





					return {t for t in NON_ALPHA.split(A       ) if len(t.strip()       ) > 0}



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       *,
	    lowerCamelCase_ = 0.85		,       ):
						"""simple docstring"""

						a					     =   duplication_jaccard_threshold
						a					     =   NUM_PERM
						a					     =   MinHashLSH(threshold=self._duplication_jaccard_threshold		,       num_perm=self._num_perm		)

						a					     =   defaultdict(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self._index.query(lowerCamelCase_		)
						if code_key in self._index.keys:
											print(F'''Duplicate key {code_key}'''		)
											return

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.get_duplicate_clusters()
						with open(lowerCamelCase_		,       "w"		) as f:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)






def     a(      A       :    Any       )		->       List[Any]:
					"""simple docstring"""





					a     ,				a					     =   element
					a					     =   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       :    Type[Dataset]       )		->       List[Any]:
					"""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     a(      A       :    Type[Dataset] ,     A       :    float       )		->       Dict:
					"""simple docstring"""





					a					     =   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       :    str ,     A       :    str       )		->       float:
					"""simple docstring"""





					a					     =   get_tokens(A       )
					a					     =   get_tokens(A       )
					return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_lowercase: int      			=		None






def     a(      A       :    str ,     A       :    Tuple       )		->       int:
					"""simple docstring"""





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






def     a(      A       :    str ,     A       :    List[str] ,     A       :    int       )		->       Tuple:
					"""simple docstring"""





					global _shared_dataset
					a					     =   dataset
					a					     =   []
					a					     =   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       :    Type[Dataset] ,     A       :    float = 0.85       )		->       Tuple[Type[Dataset], List[List[Dict]]]:
					"""simple docstring"""





					a					     =   make_duplicate_clusters(A ,     A       )
					a					     =   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
					a					     =   {}
					a					     =   find_extremes(A ,     A ,     A       )
					for extremes in extremes_clusters:
										for element in extremes:
															a					     =   element
					a					     =   duplicate_indices - set(extreme_dict.keys()       )
					a					     =   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:
															a					     =   element["base_index"] in extreme_dict
															if element["is_extreme"]:
																				a					     =   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
