




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


a__		     =     re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
a__		     =     10
a__		     =     256





def 				A__ (snake_case   :		List[str]				)     ->						Optional[MinHash]:
					if len(snake_case				) < MIN_NUM_TOKENS:
										return None
					__UpperCamelCase					:	str             =      MinHash(num_perm=snake_case				)
					for token in set(snake_case				):
										min_hash.update(token.encode()				)
					return min_hash





def 				A__ (snake_case   :		str				)     ->						Set[str]:
					return {t for t in NON_ALPHA.split(snake_case				) if len(t.strip()				) > 0}


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						List[Any]					,     *,
	    lowerCAmelCase		:						float = 0.85					,     )	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      duplication_jaccard_threshold
						__UpperCamelCase					:	int             =      NUM_PERM
						__UpperCamelCase					:	int             =      MinHashLSH(threshold=self._duplication_jaccard_threshold					,     num_perm=self._num_perm	)

						__UpperCamelCase					:	List[str]             =      defaultdict(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						MinHash	)	->       None:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      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 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       List[List[Dict]]:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						Dict	)	->       None:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      self.get_duplicate_clusters()
						with open(lowerCAmelCase					,     """w"""	) as f:
											json.dump(lowerCAmelCase					,     lowerCAmelCase	)






def 				A__ (snake_case   :		Tuple				)     ->						str:
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      element
					__UpperCamelCase					:	Optional[int]             =      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__ (snake_case   :		Type[Dataset]				)     ->						Optional[Any]:
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash		,	ThreadedIterator(snake_case		,	max_queue_size=1_00_00				)		,	chunksize=1_00		,	):
															if data is not None:
																				yield data





def 				A__ (snake_case   :		Type[Dataset]		,	snake_case   :		float				)     ->						Union[str, Any]:
					__UpperCamelCase					:	List[str]             =      DuplicationIndex(duplication_jaccard_threshold=snake_case				)

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(snake_case				)				)		,	max_queue_size=1_00				)				):
										di.add(snake_case		,	snake_case				)

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





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						float:
					__UpperCamelCase					:	Dict             =      get_tokens(snake_case				)
					__UpperCamelCase					:	Union[str, Any]             =      get_tokens(snake_case				)
					return len(tokensa & tokensa				) / len(tokensa | tokensa				)


a__		     =     None





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		Tuple				)     ->						List[str]:
					__UpperCamelCase					:	Dict             =      []
					for elementa in cluster:
										__UpperCamelCase					:	Optional[Any]             =      _shared_dataset[elementa["""base_index"""]]["""content"""]
										for elementa in extremes:
															__UpperCamelCase					:	List[str]             =      _shared_dataset[elementa["""base_index"""]]["""content"""]
															if jaccard_similarity(snake_case		,	snake_case				) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															__UpperCamelCase					:	Optional[int]             =      1
															extremes.append(snake_case				)
					return extremes





def 				A__ (snake_case   :		str		,	snake_case   :		List[Any]		,	snake_case   :		List[Any]				)     ->						Optional[int]:
					global _shared_dataset
					__UpperCamelCase					:	Dict             =      dataset
					__UpperCamelCase					:	Union[str, Any]             =      []
					__UpperCamelCase					:	Dict             =      partial(_find_cluster_extremes_shared		,	jaccard_threshold=snake_case				)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        snake_case		,	snake_case		,	)		,	total=len(snake_case				)		,	):
															extremes_list.append(snake_case				)
					return extremes_list







def 				A__ (snake_case   :		Type[Dataset]		,	snake_case   :		float = 0.85				)     ->						Tuple[Type[Dataset], List[List[Dict]]]:
					__UpperCamelCase					:	Tuple             =      make_duplicate_clusters(snake_case		,	snake_case				)
					__UpperCamelCase					:	str             =      {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
					__UpperCamelCase					:	Dict             =      {}
					__UpperCamelCase					:	Union[str, Any]             =      find_extremes(snake_case		,	snake_case		,	snake_case				)
					for extremes in extremes_clusters:
										for element in extremes:
															__UpperCamelCase					:	Optional[Any]             =      element
					__UpperCamelCase					:	Tuple             =      duplicate_indices - set(extreme_dict.keys()				)
					__UpperCamelCase					:	int             =      dataset.filter(lambda snake_case		,	snake_case				: idx not in remove_indices		,	with_indices=snake_case				)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															__UpperCamelCase					:	List[Any]             =      element["""base_index"""] in extreme_dict
															if element["is_extreme"]:
																				__UpperCamelCase					:	List[Any]             =      extreme_dict[element["""base_index"""]]["""copies"""]

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

					return ds_filter, duplicate_clusters












import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		field(default='image-classification'	,  metadata={'include_in_asdict_even_if_is_default': True}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'image': Image()}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'labels': ClassLabel}   )
	__magic_name__   :       str      	=		"image"
	__magic_name__   :       str      	=		"labels"






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Optional[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						if self.label_column not in features:
											raise ValueError(F'''Column {self.label_column} is not present in features.'''	)
						if not isinstance(features[self.label_column]					,     lowerCAmelCase	):
											raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''	)
						__UpperCamelCase					:	List[str]             =      copy.deepcopy(self	)
						__UpperCamelCase					:	List[Any]             =      self.label_schema.copy()
						__UpperCamelCase					:	List[str]             =      features[self.label_column]
						__UpperCamelCase					:	List[str]             =      label_schema
						return task_template






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Dict[str, str]:







						"""simple docstring"""







						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}






