
"""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


_snake_case          =						re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
_snake_case          =						1_0
_snake_case          =						2_5_6






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict						):






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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]						):
							return {t for t in NON_ALPHA.split(UpperCamelCase__						) if len(t.strip()						) > 0}
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				def __init__(					self	:			int				,     *,
				    UpperCAmelCase_	:			Dict = 0.85				,     )		->  str:







											"""simple docstring"""




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

											_lowerCAmelCase  	=			defaultdict(UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			List[str]  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[]
											for base, duplicates in self._duplicate_clusters.items():
																		_lowerCAmelCase  	=			[base] + list(UpperCAmelCase_  )
																		# 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(UpperCAmelCase_  )
											return duplicate_clusters







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Tuple  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_duplicate_clusters()
											with open(UpperCAmelCase_				,     'w'  ) as f:
																		json.dump(UpperCAmelCase_				,     UpperCAmelCase_  )






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]						):
							_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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[str]						):
							_lowerCAmelCase  	=			DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase__						)

							for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(UpperCamelCase__						)						)     ,  max_queue_size=100						)						):
														di.add(UpperCamelCase__     ,  UpperCamelCase__						)

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Dict						):
							_lowerCAmelCase  	=			get_tokens(UpperCamelCase__						)
							_lowerCAmelCase  	=			get_tokens(UpperCamelCase__						)
							return len(tokensa & tokensa						) / len(tokensa | tokensa						)


_snake_case          =						None






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Dict						):
							_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(UpperCamelCase__     ,  UpperCamelCase__						) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					_lowerCAmelCase  	=			1
																					extremes.append(UpperCamelCase__						)
							return extremes






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):
							global _shared_dataset
							_lowerCAmelCase  	=			dataset
							_lowerCAmelCase  	=			[]
							_lowerCAmelCase  	=			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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					str = 0.85						):
							_lowerCAmelCase  	=			make_duplicate_clusters(UpperCamelCase__     ,  UpperCamelCase__						)
							_lowerCAmelCase  	=			{x['base_index'] for cluster in duplicate_clusters for x in cluster}
							_lowerCAmelCase  	=			{}
							_lowerCAmelCase  	=			find_extremes(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__						)
							for extremes in extremes_clusters:
														for element in extremes:
																					_lowerCAmelCase  	=			element
							_lowerCAmelCase  	=			duplicate_indices - set(extreme_dict.keys()						)
							_lowerCAmelCase  	=			dataset.filter(lambda SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						: idx not in remove_indices     ,  with_indices=UpperCamelCase__						)

							# 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(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 unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			Any  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			10


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     []  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			''
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     []  )
											self.assertEqual(UpperCAmelCase_				,     []  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			(
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )

											_lowerCAmelCase  	=			[
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			['It was the best of times.']
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([1, 2, 3, 4]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     0  ).numpy()				,     expected.numpy()  )


				def 				__lowerCamelCase	(					self	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([1, 2, 3, 4, 23, 23, 23]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1, 0, 0, 0]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     23  ).numpy()				,     expected.numpy()  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([8, 2, 3, 4, 1, 1, 1]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1, 0, 0, 0]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     1  ).numpy()				,     expected.numpy()  )







				def 				__lowerCamelCase	(					self	:			str  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			101
											_lowerCAmelCase  	=			torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]  )
											_lowerCAmelCase  	=			torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]  )

											_lowerCAmelCase  	=			compute_token_type_ids(UpperCAmelCase_				,     UpperCAmelCase_  )
											np.testing.assert_array_equal(UpperCAmelCase_				,     UpperCAmelCase_  )






