


"""simple docstring"""





import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
 import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__						:   Optional[int]			 = logging.get_logger(__name__)

snake_case__						:   Optional[Any]			 = {'''vocab_file''': '''vocab.txt'''}

snake_case__						:   Any			 = {
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

snake_case__						:   Tuple			 = {
    '''openbmb/cpm-ant-10b''': 1_024,
}


def    _snake_case  (  _snake_case :	int	):
      lowerCAmelCase    :		List[str]				=      collections.OrderedDict()
      with open(_snake_case       ,      '''r'''       ,      encoding='''utf-8'''	) as reader:
            lowerCAmelCase    :		List[Any]				=      reader.readlines()
      for index, token in enumerate(_snake_case	):
            lowerCAmelCase    :		List[Any]				=      token.rstrip('''\n'''	)
            lowerCAmelCase    :		Tuple				=      index
      return vocab



class 						snake_case_(				a__	):






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    List[str]="<unk>"    , UpperCamelCase_     :    Any=2_0_0 ):
           lowerCAmelCase    :		Any				=      vocab
           lowerCAmelCase    :		List[Any]				=      unk_token
           lowerCAmelCase    :		Dict				=      max_input_chars_per_word






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Tuple ):
           lowerCAmelCase    :		Any				=      list(UpperCamelCase_ )
           if len(UpperCamelCase_ ) > self.max_input_chars_per_word:
                 return [self.unk_token]

           lowerCAmelCase    :		Tuple				=      0
           lowerCAmelCase    :		Optional[int]				=      []
           while start < len(UpperCamelCase_ ):
                 lowerCAmelCase    :		List[str]				=      len(UpperCamelCase_ )
                 lowerCAmelCase    :		Dict				=      None
                 while start < end:
                       lowerCAmelCase    :		Union[str, Any]				=      ''''''.join(chars[start:end] )
                       if substr in self.vocab:
                             lowerCAmelCase    :		Any				=      substr
                             break
                       end -= 1
                 if cur_substr is None:
                       sub_tokens.append(self.unk_token )
                       start += 1
                 else:
                       sub_tokens.append(UpperCamelCase_ )
                       lowerCAmelCase    :		List[Any]				=      end

           return sub_tokens



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase     =    ['''input_ids''', '''attention_mask''']
     __UpperCamelCase     =    False






     def __init__(		self     :    int    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Union[str, Any]="<d>"    , UpperCamelCase_     :    int="</d>"    , UpperCamelCase_     :    str="<s>"    , UpperCamelCase_     :    str="</s>"    , UpperCamelCase_     :    Optional[int]="<pad>"    , UpperCamelCase_     :    Optional[int]="<unk>"    , UpperCamelCase_     :    Any="</n>"    , UpperCamelCase_     :    List[Any]="</_>"    , UpperCamelCase_     :    int="left"    , **UpperCamelCase_     :    List[str]    , ):
           requires_backends(self    , ['''jieba'''] )
           super().__init__(
               bod_token=UpperCamelCase_    , eod_token=UpperCamelCase_    , bos_token=UpperCamelCase_    , eos_token=UpperCamelCase_    , pad_token=UpperCamelCase_    , unk_token=UpperCamelCase_    , line_token=UpperCamelCase_    , space_token=UpperCamelCase_    , padding_side=UpperCamelCase_    , **UpperCamelCase_    , )
           lowerCAmelCase    :		Any				=      bod_token
           lowerCAmelCase    :		Union[str, Any]				=      eod_token
           lowerCAmelCase    :		Optional[Any]				=      load_vocab(UpperCamelCase_ )
           lowerCAmelCase    :		Union[str, Any]				=      self.encoder[space_token]
           lowerCAmelCase    :		Union[str, Any]				=      self.encoder[line_token]

           del self.encoder[space_token]
           del self.encoder[line_token]

           lowerCAmelCase    :		Any				=      collections.OrderedDict(sorted(self.encoder.items()    , key=lambda UpperCamelCase_ : x[1] ) )
           lowerCAmelCase    :		Tuple				=      {v: k for k, v in self.encoder.items()}

           lowerCAmelCase    :		Any				=      WordpieceTokenizer(vocab=self.encoder    , unk_token=self.unk_token )






     @property
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           return self.encoder[self.bod_token]






     @property
     def    lowerCamelCase__   (		self     :    List[str] ):
           return self.encoder[self.eod_token]






     @property
     def    lowerCamelCase__   (		self     :    Any ):
           return self.encoder["\n"]






     @property
     def    lowerCamelCase__   (		self     :    List[Any] ):
           return len(self.encoder )






     def    lowerCamelCase__   (		self     :    str ):
           return dict(self.encoder    , **self.added_tokens_encoder )






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    List[Any] ):
           lowerCAmelCase    :		Dict				=      []
           for x in jieba.cut(UpperCamelCase_    , cut_all=UpperCamelCase_ ):
                 output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase_ ) )
           return output_tokens






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Dict    , **UpperCamelCase_     :    Union[str, Any] ):
           lowerCAmelCase    :		Tuple				=      [i for i in token_ids if i >= 0]
           lowerCAmelCase    :		Dict				=      [
               x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
           ]
           return super()._decode(UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    int    , UpperCamelCase_     :    str ):
           return token in self.encoder






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    List[str] ):
           return "".join(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    int ):
           return self.encoder.get(UpperCamelCase_    , self.encoder.get(self.unk_token ) )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Dict ):
           return self.decoder.get(UpperCamelCase_    , self.unk_token )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           if os.path.isdir(UpperCamelCase_ ):
                 lowerCAmelCase    :		Optional[int]				=      os.path.join(
                     UpperCamelCase_    , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
           else:
                 lowerCAmelCase    :		int				=      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
           lowerCAmelCase    :		Any				=      0
           if " " in self.encoder:
                 lowerCAmelCase    :		int				=      self.encoder[''' ''']
                 del self.encoder[" "]
           if "\n" in self.encoder:
                 lowerCAmelCase    :		Optional[Any]				=      self.encoder['''\n''']
                 del self.encoder["\n"]
           lowerCAmelCase    :		List[Any]				=      collections.OrderedDict(sorted(self.encoder.items()    , key=lambda UpperCamelCase_ : x[1] ) )
           with open(UpperCamelCase_    , '''w'''    , encoding='''utf-8''' ) as writer:
                 for token, token_index in self.encoder.items():
                       if index != token_index:
                             logger.warning(
                                 F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                                 ''' Please check that the vocabulary is not corrupted!''' )
                             lowerCAmelCase    :		str				=      token_index
                       writer.write(token + '''\n''' )
                       index += 1
           return (vocab_file,)






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    List[int] = None ):
           if token_ids_a is None:
                 return [self.bos_token_id] + token_ids_a
           return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None    , UpperCamelCase_     :    bool = False ):

           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=UpperCamelCase_    , token_ids_a=UpperCamelCase_    , already_has_special_tokens=UpperCamelCase_ )

           if token_ids_a is not None:
                 return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ ))
           return [1] + ([0] * len(UpperCamelCase_ ))



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





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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Set[str]:
  return {t for t in NON_ALPHA.split(_lowerCamelCase	) if len(t.strip()	) > 0}
class 						__A							:






     def __init__(		self				,     *,
         a__ = 0.8_5				,     ):
       _lowerCAmelCase						:       List[Any]  			=     duplication_jaccard_threshold
       _lowerCAmelCase						:       Union[str, Any]  			=     NUM_PERM
       _lowerCAmelCase						:       Optional[int]  			=     MinHashLSH(threshold=self._duplication_jaccard_threshold				,     num_perm=self._num_perm  )

       _lowerCAmelCase						:       Optional[int]  			=     defaultdict(a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     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  ):
       _lowerCAmelCase						:       int  			=     []
       for base, duplicates in self._duplicate_clusters.items():
         _lowerCAmelCase						:       List[str]  			=     [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     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     self.get_duplicate_clusters()
       with open(a__				,     """w"""  ) as f:
         json.dump(a__				,     a__  )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      Tuple:
  _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       Tuple  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]	)       ->      Optional[Any]:
  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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float	)       ->      List[str]:
  _lowerCAmelCase						:       Optional[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      float:
  _lowerCAmelCase						:       Any  			=     get_tokens(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     get_tokens(_lowerCamelCase	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


_a      :		str           =   None





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]	)       ->      Dict:
  _lowerCAmelCase						:       int  			=     []
  for elementa in cluster:
    _lowerCAmelCase						:       Dict  			=     _shared_dataset[elementa["""base_index"""]]["""content"""]
    for elementa in extremes:
      _lowerCAmelCase						:       Any  			=     _shared_dataset[elementa["""base_index"""]]["""content"""]
      if jaccard_similarity(_lowerCamelCase			,_lowerCamelCase	) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      _lowerCAmelCase						:       Any  			=     1
      extremes.append(_lowerCamelCase	)
  return extremes





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Any	)       ->      str:
  global _shared_dataset
  _lowerCAmelCase						:       Tuple  			=     dataset
  _lowerCAmelCase						:       Optional[Any]  			=     []
  _lowerCAmelCase						:       Optional[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float = 0.85	)       ->      Tuple[Type[Dataset], List[List[Dict]]]:
  _lowerCAmelCase						:       Tuple  			=     make_duplicate_clusters(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
  _lowerCAmelCase						:       Optional[int]  			=     {}
  _lowerCAmelCase						:       Tuple  			=     find_extremes(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
  for extremes in extremes_clusters:
    for element in extremes:
      _lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       List[Any]  			=     duplicate_indices - set(extreme_dict.keys()	)
  _lowerCAmelCase						:       List[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:
      _lowerCAmelCase						:       Tuple  			=     element["""base_index"""] in extreme_dict
      if element["is_extreme"]:
        _lowerCAmelCase						:       Dict  			=     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




