
'''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


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







def 		A_       (	_lowerCAmelCase   :    List[str]							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    str							):



      """simple docstring"""




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

class  UpperCAmelCase__						:



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

          _lowerCamelCase				:   List[Any]								=   defaultdict(__A     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Tuple,__A      :       MinHash     ):
          _lowerCamelCase				:   Optional[int]								=   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   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Union[str, Any]								=   []
          for base, duplicates in self._duplicate_clusters.items():
                _lowerCamelCase				:   List[str]								=   [base] + list(__A     )
                # reformat the cluster to be a list of dict
                _lowerCamelCase				:   Any								=   [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
                duplicate_clusters.append(__A     )
          return duplicate_clusters



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       Optional[Any]     ):
          _lowerCamelCase				:   List[str]								=   self.get_duplicate_clusters()
          with open(__A,"w"     ) as f:
                json.dump(__A,__A     )







def 		A_       (	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase						,		_lowerCamelCase				:   str								=   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 		A_       (	_lowerCAmelCase   :    Type[Dataset]							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    Type[Dataset]				,	_lowerCAmelCase   :    float							):



      """simple docstring"""




      _lowerCamelCase				:   List[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 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   get_tokens(_lowerCAmelCase							)
      _lowerCamelCase				:   Any								=   get_tokens(_lowerCAmelCase							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


UpperCAmelCase_       :   Dict   =       None







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      global _shared_dataset
      _lowerCamelCase				:   List[Any]								=   dataset
      _lowerCamelCase				:   List[Any]								=   []
      _lowerCamelCase				:   str								=   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 		A_       (	_lowerCAmelCase   :    Type[Dataset]				,	_lowerCAmelCase   :    float = 0.8_5							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   make_duplicate_clusters(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
      _lowerCamelCase				:   List[Any]								=   {}
      _lowerCamelCase				:   str								=   find_extremes(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
      for extremes in extremes_clusters:
            for element in extremes:
                  _lowerCamelCase				:   Tuple								=   element
      _lowerCamelCase				:   str								=   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				:   Optional[int]								=   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

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_       :   Tuple   =       {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 UpperCAmelCase_       :   Tuple   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)