

'''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


lowerCamelCase :      List[str]					 =     re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowerCamelCase :      Any					 =     1_0
lowerCamelCase :      Tuple					 =     2_5_6


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[MinHash]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			Set[str]:
     """simple docstring"""




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







class 							A__    :





  def __init__(		self      :	Dict							,					*,
      _a      :	float = 0.85							,					)	->    List[str]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    =defaultdict(_a )





  def 						A  (		self      :	Tuple							,					_a      :	Tuple							,					_a      :	MinHash )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =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 						A  (		self      :	int )	->    List[List[Dict]]:





       '''simple docstring'''






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





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Optional[Any] )	->    None:





       '''simple docstring'''






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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =element
     _SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     Type[Dataset]				)      ->			List[Any]:
     """simple docstring"""




     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash							, ThreadedIterator(_UpperCamelCase							, max_queue_size=1_00_00				)							, chunksize=1_00							, ):
               if data is not None:
                    yield data


def        _lowerCAmelCase					(						_UpperCamelCase						:     Type[Dataset]							, _UpperCamelCase						:     float				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =DuplicationIndex(duplication_jaccard_threshold=_UpperCamelCase				)

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

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str				)      ->			float:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =get_tokens(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =get_tokens(_UpperCamelCase				)
     return len(tokensa & tokensa				) / len(tokensa | tokensa				)


lowerCamelCase :      Union[str, Any]					 =     None


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Tuple				)      ->			List[str]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     str							, _UpperCamelCase						:     Any				)      ->			Optional[Any]:
     """simple docstring"""




     global _shared_dataset
     _SCREAMING_SNAKE_CASE							    =dataset
     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     Type[Dataset]							, _UpperCamelCase						:     float = 0.85				)      ->			Tuple[Type[Dataset], List[List[Dict]]]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =make_duplicate_clusters(_UpperCamelCase							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    ={x['base_index'] for cluster in duplicate_clusters for x in cluster}
     _SCREAMING_SNAKE_CASE							    ={}
     _SCREAMING_SNAKE_CASE							    =find_extremes(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)
     for extremes in extremes_clusters:
          for element in extremes:
               _SCREAMING_SNAKE_CASE							    =element
     _SCREAMING_SNAKE_CASE							    =duplicate_indices - set(extreme_dict.keys()				)
     _SCREAMING_SNAKE_CASE							    =dataset.filter(lambda _UpperCamelCase							, _UpperCamelCase				: idx not in remove_indices							, with_indices=_UpperCamelCase				)

     # update duplicate_clusters
     for cluster in duplicate_clusters:
          for element in cluster:
               _SCREAMING_SNAKE_CASE							    =element['base_index'] in extreme_dict
               if element["is_extreme"]:
                    _SCREAMING_SNAKE_CASE							    =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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							A__    (       A__     ,   A__  ):
  A__		 =				'convnextv2'





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=3							,					_a      :	Any=4							,					_a      :	int=4							,					_a      :	Union[str, Any]=None							,					_a      :	List[str]=None							,					_a      :	Optional[Any]="gelu"							,					_a      :	Any=0.02							,					_a      :	Any=1e-12							,					_a      :	Tuple=0.0							,					_a      :	int=224							,					_a      :	Any=None							,					_a      :	Optional[int]=None							,					**_a      :	List[str]							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =patch_size
       _SCREAMING_SNAKE_CASE							    =num_stages
       _SCREAMING_SNAKE_CASE							    =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       _SCREAMING_SNAKE_CASE							    =[3, 3, 9, 3] if depths is None else depths
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =drop_path_rate
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =['stem'] + [f"stage{idx}" for idx in range(1							,					len(self.depths ) + 1 )]
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_aligned_output_features_output_indices(
           out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names )
