


'''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


A				:					List[Any]    						=			re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
A				:					Dict    						=			10
A				:					Optional[Any]    						=			256






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]       ):
       if len(lowerCamelCase       ) < MIN_NUM_TOKENS:
              return None
       _A					:     Dict     = MinHash(num_perm=lowerCamelCase       )
       for token in set(lowerCamelCase       ):
              min_hash.update(token.encode()       )
       return min_hash






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       return {t for t in NON_ALPHA.split(lowerCamelCase       ) if len(t.strip()       ) > 0}


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[str]				,							*,
        SCREAMING_SNAKE_CASE      :							float = 0.85				,							):
           _A					:     int     = duplication_jaccard_threshold
           _A					:     int     = NUM_PERM
           _A					:     Dict     = MinHashLSH(threshold=self._duplication_jaccard_threshold				,							num_perm=self._num_perm)

           _A					:     Optional[Any]     = defaultdict(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							MinHash):
           _A					:     str     = self._index.query(SCREAMING_SNAKE_CASE)
           if code_key in self._index.keys:
                  print(F'Duplicate key {code_key}')
                  return

           self._index.insert(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           if len(SCREAMING_SNAKE_CASE) > 0:
                  for base_duplicate in close_duplicates:
                         if base_duplicate in self._duplicate_clusters:
                                self._duplicate_clusters[base_duplicate].add(SCREAMING_SNAKE_CASE)
                                break
                  else:
                         self._duplicate_clusters[close_duplicates[0]].add(SCREAMING_SNAKE_CASE)


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


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           _A					:     List[Any]     = self.get_duplicate_clusters()
           with open(SCREAMING_SNAKE_CASE				,							'w') as f:
                  json.dump(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       _A					:     List[str]     = element
       _A					:     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 				lowerCAmelCase__	(    lowerCamelCase					:      Type[Dataset]       ):
       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash  ,ThreadedIterator(lowerCamelCase  ,max_queue_size=10000       )  ,chunksize=100  ,):
                     if data is not None:
                            yield data






def 				lowerCAmelCase__	(    lowerCamelCase					:      Type[Dataset]  ,lowerCamelCase					:      float       ):
       _A					:     str     = DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase       )

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

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      str       ):
       _A					:     Union[str, Any]     = get_tokens(lowerCamelCase       )
       _A					:     Optional[int]     = get_tokens(lowerCamelCase       )
       return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


A				:					Optional[Any]    						=			None






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Union[str, Any]       ):
       _A					:     Any     = []
       for elementa in cluster:
              _A					:     List[Any]     = _shared_dataset[elementa['base_index']]['content']
              for elementa in extremes:
                     _A					:     Optional[Any]     = _shared_dataset[elementa['base_index']]['content']
                     if jaccard_similarity(lowerCamelCase  ,lowerCamelCase       ) >= jaccard_threshold:
                            elementa["copies"] += 1
                            break
              else:
                     _A					:     Optional[Any]     = 1
                     extremes.append(lowerCamelCase       )
       return extremes






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






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

       # update duplicate_clusters
       for cluster in duplicate_clusters:
              for element in cluster:
                     _A					:     Optional[int]     = element['base_index'] in extreme_dict
                     if element["is_extreme"]:
                            _A					:     Union[str, Any]     = extreme_dict[element['base_index']]['copies']

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

       return ds_filter, duplicate_clusters










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:					Optional[int]    						=			{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Tuple    						=			['''Mask2FormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Mask2FormerForUniversalSegmentation''',
       '''Mask2FormerModel''',
       '''Mask2FormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   A				:					Optional[Any]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)






