




from __future__ import annotations




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Dict							)					->     str:
							a    =			TypeError(
							    "Matrices must be formed from a list of zero or more lists containing at "
							    "least one and the same number of values, each of which must be of type "
							    "int or float."							)
							if len(lowerCamelCase_							) != 0:
										a    =			len(rows[0]							)
										if cols == 0:
													raise error
										for row in rows:
													if len(lowerCamelCase_							) != cols:
																raise error
													for value in row:
																if not isinstance(lowerCamelCase_					,      (int, float)							):
																			raise error
										a    =			rows
							else:
										a    =			[]





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Any:
							return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]





				@property
				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Dict:
							return len(self.rows							)





				@property
				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							return len(self.rows[0]							)





				@property
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Optional[int]:
							return (self.num_rows, self.num_columns)





				@property
				def 							__UpperCAmelCase							(      self				:     str							)					->     Optional[int]:
							return self.order[0] == self.order[1]





				def 							__UpperCAmelCase							(      self				:     str							)					->     List[Any]:
							a    =			[
							    [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
							    for row_num in range(self.num_rows							)
							]
							return Matrix(lowerCamelCase_							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[Any]:

							if not self.is_square:
										return 0
							if self.order == (0, 0):
										return 1
							if self.order == (1, 1):
										return int(self.rows[0][0]							)
							if self.order == (2, 2):
										return int(
										    (self.rows[0][0] * self.rows[1][1])
										    - (self.rows[0][1] * self.rows[1][0])							)
							else:
										return sum(
										    self.rows[0][column] * self.cofactors().rows[0][column]
										    for column in range(self.num_columns							)							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							return bool(self.determinant()							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     str							)					->     List[Any]:
							a    =			[
							    [
							        self.rows[other_row][other_column]
							        for other_column in range(self.num_columns							)
							        if other_column != column
							    ]
							    for other_row in range(self.num_rows							)
							    if other_row != row
							]
							return Matrix(lowerCamelCase_							).determinant()





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[Any]							)					->     Tuple:

							if (row + column) % 2 == 0:
										return self.get_minor(lowerCamelCase_					,      lowerCamelCase_							)
							return -1 * self.get_minor(lowerCamelCase_					,      lowerCamelCase_							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							return Matrix(
							    [
							        [self.get_minor(lowerCamelCase_					,      lowerCamelCase_							) for column in range(self.num_columns							)]
							        for row in range(self.num_rows							)
							    ]							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							return Matrix(
							    [
							        [
							            self.minors().rows[row][column]
							            if (row + column) % 2 == 0
							            else self.minors().rows[row][column] * -1
							            for column in range(self.minors().num_columns							)
							        ]
							        for row in range(self.minors().num_rows							)
							    ]							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Tuple:
							a    =			[
							    [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
							    for row in range(self.num_rows							)
							]
							return Matrix(lowerCamelCase_							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							a    =			self.determinant()
							if not determinant:
										raise TypeError("Only matrices with a non-zero determinant have an inverse"							)
							return self.adjugate() * (1 / determinant)





				def __repr__(      self				:     Any							)					->     Optional[Any]:
							return str(self.rows							)





				def __str__(      self				:     int							)					->     Union[str, Any]:

							if self.num_rows == 0:
										return "[]"
							if self.num_rows == 1:
										return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
							return (
							    "["
							    + "\n ".join(
							        [
							            "[" + ". ".join([str(lowerCamelCase_							) for value in row]							) + ".]"
							            for row in self.rows
							        ]							)
							    + "]"
							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Union[str, Any] = None							)					->     Any:
							a    =			TypeError("Row must be a list containing all ints and/or floats"							)
							if not isinstance(lowerCamelCase_					,      lowerCamelCase_							):
										raise type_error
							for value in row:
										if not isinstance(lowerCamelCase_					,      (int, float)							):
													raise type_error
							if len(lowerCamelCase_							) != self.num_columns:
										raise ValueError(
										    "Row must be equal in length to the other rows in the matrix"							)
							if position is None:
										self.rows.append(lowerCamelCase_							)
							else:
										a    =			self.rows[0:position] + [row] + self.rows[position:]





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[int] = None							)					->     List[str]:
							a    =			TypeError(
							    "Column must be a list containing all ints and/or floats"							)
							if not isinstance(lowerCamelCase_					,      lowerCamelCase_							):
										raise type_error
							for value in column:
										if not isinstance(lowerCamelCase_					,      (int, float)							):
													raise type_error
							if len(lowerCamelCase_							) != self.num_rows:
										raise ValueError(
										    "Column must be equal in length to the other columns in the matrix"							)
							if position is None:
										a    =			[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
							else:
										a    =			[
										    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
										    for i in range(self.num_rows							)
										]





				def __eq__(      self				:     Any					,      __lowerCamelCase				:     Tuple							)					->     Tuple:

							if not isinstance(lowerCamelCase_					,      lowerCamelCase_							):
										return NotImplemented
							return self.rows == other.rows





				def __ne__(      self				:     Any					,      __lowerCamelCase				:     List[str]							)					->     Union[str, Any]:
							return not self == other





				def __neg__(      self				:     Any							)					->     Optional[Any]:
							return self * -1





				def __add__(      self				:     str					,      __lowerCamelCase				:     Optional[Any]							)					->     str:

							if self.order != other.order:
										raise ValueError("Addition requires matrices of the same order"							)
							return Matrix(
							    [
							        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
							        for i in range(self.num_rows							)
							    ]							)





				def __sub__(      self				:     Dict					,      __lowerCamelCase				:     Optional[Any]							)					->     Optional[Any]:

							if self.order != other.order:
										raise ValueError("Subtraction requires matrices of the same order"							)
							return Matrix(
							    [
							        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
							        for i in range(self.num_rows							)
							    ]							)





				def __mul__(      self				:     Tuple					,      __lowerCamelCase				:     Any							)					->     List[Any]:

							if isinstance(lowerCamelCase_					,      (int, float)							):
										return Matrix(
										    [[int(element * other							) for element in row] for row in self.rows]							)
							elif isinstance(lowerCamelCase_					,      lowerCamelCase_							):
										if self.num_columns != other.num_rows:
													raise ValueError(
													    "The number of columns in the first matrix must "
													    "be equal to the number of rows in the second"							)
										return Matrix(
										    [
										        [Matrix.dot_product(lowerCamelCase_					,      lowerCamelCase_							) for column in other.columns()]
										        for row in self.rows
										    ]							)
							else:
										raise TypeError(
										    "A Matrix can only be multiplied by an int, float, or another matrix"							)





				def __pow__(      self				:     Optional[int]					,      __lowerCamelCase				:     str							)					->     Optional[int]:

							if not isinstance(lowerCamelCase_					,      lowerCamelCase_							):
										raise TypeError("A Matrix can only be raised to the power of an int"							)
							if not self.is_square:
										raise ValueError("Only square matrices can be raised to a power"							)
							if other == 0:
										return self.identity()
							if other < 0:
										if self.is_invertable():
													return self.inverse() ** (-other)
										raise ValueError(
										    "Only invertable matrices can be raised to a negative power"							)
							a    =			self
							for _ in range(other - 1							):
										result *= self
							return result





				@classmethod
				def 							__UpperCAmelCase							(      cls				:     Optional[int]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[int]							)					->     Optional[Any]:
							return sum(row[i] * column[i] for i in range(len(lowerCamelCase_							)							)							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()











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
