
'''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


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




def lowercase_	(   _lowercase				)     ->							Optional[MinHash]:
					'''simple docstring'''

					if len(_lowercase				) < MIN_NUM_TOKENS:
										return None
					lowerCamelCase_					:      Union[str, Any]     =		MinHash(num_perm=_lowercase				)
					for token in set(_lowercase				):
										min_hash.update(token.encode()				)
					return min_hash




def lowercase_	(   _lowercase				)     ->							Set[str]:
					'''simple docstring'''

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

class 	__lowercase				:

			def __init__(self			,   *,
			    A = 0.85			,   ):
								lowerCamelCase_					:      Optional[Any]     =		duplication_jaccard_threshold
								lowerCamelCase_					:      Any     =		NUM_PERM
								lowerCamelCase_					:      str     =		MinHashLSH(threshold=self._duplication_jaccard_threshold			,   num_perm=self._num_perm      )

								lowerCamelCase_					:      Optional[int]     =		defaultdict(A      )

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






			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Any     =		self.get_duplicate_clusters()
								with open(A			,   '''w'''      ) as f:
													json.dump(A			,   A      )




def lowercase_	(   _lowercase				)     ->							Any:
					'''simple docstring'''

					lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		element
					lowerCamelCase_					:      Optional[Any]     =		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 lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash			,       ThreadedIterator(_lowercase			,       max_queue_size=10_000				)			,       chunksize=100			,       ):
															if data is not None:
																				yield data




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		DuplicationIndex(duplication_jaccard_threshold=_lowercase				)

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

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




def lowercase_	(   _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		get_tokens(_lowercase				)
					lowerCamelCase_					:      Optional[int]     =		get_tokens(_lowercase				)
					return len(tokensa & tokensa				) / len(tokensa | tokensa				)


__lowercase							:   str					=     None




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

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




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Optional[int]:
					'''simple docstring'''

					global _shared_dataset
					lowerCamelCase_					:      Tuple     =		dataset
					lowerCamelCase_					:      Optional[Any]     =		[]
					lowerCamelCase_					:      List[str]     =		partial(_find_cluster_extremes_shared			,       jaccard_threshold=_lowercase				)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        _lowercase			,       _lowercase			,       )			,       total=len(_lowercase				)			,       ):
															extremes_list.append(_lowercase				)
					return extremes_list






def lowercase_	(   _lowercase			,       _lowercase = 0.85				)     ->							Tuple[Type[Dataset], List[List[Dict]]]:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		make_duplicate_clusters(_lowercase			,       _lowercase				)
					lowerCamelCase_					:      List[Any]     =		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowerCamelCase_					:      Optional[int]     =		{}
					lowerCamelCase_					:      Tuple     =		find_extremes(_lowercase			,       _lowercase			,       _lowercase				)
					for extremes in extremes_clusters:
										for element in extremes:
															lowerCamelCase_					:      str     =		element
					lowerCamelCase_					:      Dict     =		duplicate_indices - set(extreme_dict.keys()				)
					lowerCamelCase_					:      Tuple     =		dataset.filter(lambda _lowercase			,       _lowercase				: idx not in remove_indices			,       with_indices=_lowercase				)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															lowerCamelCase_					:      List[str]     =		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowerCamelCase_					:      Dict     =		extreme_dict[element['''base_index''']]['''copies''']

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

					return ds_filter, duplicate_clusters




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


