

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


_SCREAMING_SNAKE_CASE  :						str         =    re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
_SCREAMING_SNAKE_CASE  :						Tuple         =    10
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    256
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if len(_A       ) < MIN_NUM_TOKENS:
          return None
     SCREAMING_SNAKE_CASE__					=					MinHash(num_perm=_A       )
     for token in set(_A       ):
          min_hash.update(token.encode()       )
     return min_hash
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	List[str]			,     *,
      __lowerCamelCase		:	float = 0.85			,     )	->							Optional[int]:
       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(__lowerCamelCase      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	MinHash      )	->							None:
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	List[Any]      )	->							List[List[Dict]]:
       SCREAMING_SNAKE_CASE__					=					[]
       for base, duplicates in self._duplicate_clusters.items():
            SCREAMING_SNAKE_CASE__					=					[base] + list(__lowerCamelCase      )
            # 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(__lowerCamelCase      )
       return duplicate_clusters







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[Any]      )	->							None:
       SCREAMING_SNAKE_CASE__					=					self.get_duplicate_clusters()
       with open(__lowerCamelCase			,     '''w'''      ) as f:
            json.dump(__lowerCamelCase			,     __lowerCamelCase      )



def        UpperCAmelCase_     (		_A       ):





     '''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        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					DuplicationIndex(duplication_jaccard_threshold=_A       )

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

     # Returns a List[Cluster] where Cluster is List[str] with the filenames.
     return di.get_duplicate_clusters()
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_tokens(_A       )
     SCREAMING_SNAKE_CASE__					=					get_tokens(_A       )
     return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_SCREAMING_SNAKE_CASE  :						List[Any]         =    None
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''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(_A   ,	_A       ) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               SCREAMING_SNAKE_CASE__					=					1
               extremes.append(_A       )
     return extremes
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     global _shared_dataset
     SCREAMING_SNAKE_CASE__					=					dataset
     SCREAMING_SNAKE_CASE__					=					[]
     SCREAMING_SNAKE_CASE__					=					partial(_find_cluster_extremes_shared   ,	jaccard_threshold=_A       )
     with mp.Pool() as pool:
          for extremes in tqdm(
              pool.imap_unordered(
                  _A   ,	_A   ,	)   ,	total=len(_A       )   ,	):
               extremes_list.append(_A       )
     return extremes_list







def        UpperCAmelCase_     (		_A   ,	_A = 0.8_5       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					make_duplicate_clusters(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     SCREAMING_SNAKE_CASE__					=					{}
     SCREAMING_SNAKE_CASE__					=					find_extremes(_A   ,	_A   ,	_A       )
     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 _A   ,	_A       : idx not in remove_indices   ,	with_indices=_A       )

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

     return ds_filter, duplicate_clusters





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						int         =    {'''vocab_file''': '''spiece.model'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE  :						Optional[int]         =    0
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    1
_SCREAMING_SNAKE_CASE  :						int         =    2
_SCREAMING_SNAKE_CASE  :						str         =    3
_SCREAMING_SNAKE_CASE  :						str         =    4






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      "left"






  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]=False			,     __lowerCamelCase		:	Optional[Any]=True			,     __lowerCamelCase		:	Optional[Any]=False			,     __lowerCamelCase		:	List[str]="<s>"			,     __lowerCamelCase		:	Any="</s>"			,     __lowerCamelCase		:	Tuple="<unk>"			,     __lowerCamelCase		:	List[str]="<sep>"			,     __lowerCamelCase		:	List[str]="<pad>"			,     __lowerCamelCase		:	int="<cls>"			,     __lowerCamelCase		:	Any="<mask>"			,     __lowerCamelCase		:	Dict=["<eop>", "<eod>"]			,     __lowerCamelCase		:	Optional[Dict[str, Any]] = None			,     **__lowerCamelCase		:	int			,     )	->							None:
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE__					=					AddedToken(__lowerCamelCase			,     lstrip=__lowerCamelCase			,     rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase			,     __lowerCamelCase      ) else mask_token

       SCREAMING_SNAKE_CASE__					=					{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=__lowerCamelCase			,     remove_space=__lowerCamelCase			,     keep_accents=__lowerCamelCase			,     bos_token=__lowerCamelCase			,     eos_token=__lowerCamelCase			,     unk_token=__lowerCamelCase			,     sep_token=__lowerCamelCase			,     pad_token=__lowerCamelCase			,     cls_token=__lowerCamelCase			,     mask_token=__lowerCamelCase			,     additional_special_tokens=__lowerCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__lowerCamelCase			,     )

       SCREAMING_SNAKE_CASE__					=					3

       SCREAMING_SNAKE_CASE__					=					do_lower_case
       SCREAMING_SNAKE_CASE__					=					remove_space
       SCREAMING_SNAKE_CASE__					=					keep_accents
       SCREAMING_SNAKE_CASE__					=					vocab_file

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(__lowerCamelCase      )






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Union[str, Any]:
       return len(self.sp_model      )






  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					{self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab






  def __getstate__(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.__dict__.copy()
       SCREAMING_SNAKE_CASE__					=					None
       return state






  def __setstate__(							self		:	Any			,     __lowerCamelCase		:	Tuple      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					d

       # for backward compatibility
       if not hasattr(self			,     '''sp_model_kwargs'''      ):
            SCREAMING_SNAKE_CASE__					=					{}

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]      )	->							List[str]:
       if self.remove_space:
            SCREAMING_SNAKE_CASE__					=					''' '''.join(inputs.strip().split()      )
       else:
            SCREAMING_SNAKE_CASE__					=					inputs
       SCREAMING_SNAKE_CASE__					=					outputs.replace('''``'''			,     '''"'''      ).replace('''\'\''''			,     '''"'''      )

       if not self.keep_accents:
            SCREAMING_SNAKE_CASE__					=					unicodedata.normalize('''NFKD'''			,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					''''''.join([c for c in outputs if not unicodedata.combining(__lowerCamelCase      )]      )
       if self.do_lower_case:
            SCREAMING_SNAKE_CASE__					=					outputs.lower()

       return outputs






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	str      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					self.preprocess_text(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.sp_model.encode(__lowerCamelCase			,     out_type=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					[]
       for piece in pieces:
            if len(__lowerCamelCase      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase			,     ''''''      )      )
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                      if len(cur_pieces[0]      ) == 1:
                           SCREAMING_SNAKE_CASE__					=					cur_pieces[1:]
                      else:
                           SCREAMING_SNAKE_CASE__					=					cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]      )
                 new_pieces.extend(__lowerCamelCase      )
            else:
                 new_pieces.append(__lowerCamelCase      )

       return new_pieces






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[Any]:
       return self.sp_model.PieceToId(__lowerCamelCase      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							List[Any]:
       return self.sp_model.IdToPiece(__lowerCamelCase      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      ).replace(__lowerCamelCase			,     ''' '''      ).strip()
       return out_string






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	bool = True			,     **__lowerCamelCase		:	int			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''use_source_tokenizer'''			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.convert_ids_to_tokens(__lowerCamelCase			,     skip_special_tokens=__lowerCamelCase      )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       SCREAMING_SNAKE_CASE__					=					[]
       SCREAMING_SNAKE_CASE__					=					[]
       for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                 continue
            if token in self.added_tokens_encoder:
                 if current_sub_text:
                      sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )
                      SCREAMING_SNAKE_CASE__					=					[]
                 sub_texts.append(__lowerCamelCase      )
            else:
                 current_sub_text.append(__lowerCamelCase      )
       if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )

       # Mimic the behavior of the Rust tokenizer:
       # By default, there are no spaces between special tokens
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
            SCREAMING_SNAKE_CASE__					=					self.clean_up_tokenization(__lowerCamelCase      )
            return clean_text
       else:
            return text






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[self.cls_token_id]
       if token_ids_a is None:
            return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None			,     __lowerCamelCase		:	bool = False      )	->							List[int]:

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

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






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[2]

       if token_ids_a is None:
            return len(token_ids_a + sep      ) * [0] + cls_segment_id
       return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       if not os.path.isdir(__lowerCamelCase      ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
            return
       SCREAMING_SNAKE_CASE__					=					os.path.join(
           __lowerCamelCase			,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ) and os.path.isfile(self.vocab_file      ):
            copyfile(self.vocab_file			,     __lowerCamelCase      )
       elif not os.path.isfile(self.vocab_file      ):
            with open(__lowerCamelCase			,     '''wb'''      ) as fi:
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.serialized_model_proto()
                 fi.write(__lowerCamelCase      )

       return (out_vocab_file,)


