

'''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=  re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
a=  1_0
a=  2_5_6







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    if len(_a			) < MIN_NUM_TOKENS:
        return None
    __UpperCamelCase	:	Optional[Any]		    =  MinHash(num_perm=_a			)
    for token in set(_a			):
        min_hash.update(token.encode()			)
    return min_hash







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



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






class 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			*,
         _lowerCamelCase = 0.8_5     ,			):
         __UpperCamelCase	:	List[Any]		    =  duplication_jaccard_threshold
         __UpperCamelCase	:	List[str]		    =  NUM_PERM
         __UpperCamelCase	:	str		    =  MinHashLSH(threshold=self._duplication_jaccard_threshold     ,			num_perm=self._num_perm    )

         __UpperCamelCase	:	Dict		    =  defaultdict(_lowerCamelCase    )







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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  self.get_duplicate_clusters()
         with open(_lowerCamelCase     ,			'w'    ) as f:
             json.dump(_lowerCamelCase     ,			_lowerCamelCase    )







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  element
    __UpperCamelCase	:	List[str]		    =  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	:	Type[Dataset]			):




    """simple docstring"""



    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash				,     ThreadedIterator(_a				,     max_queue_size=1_0_0_0_0			)				,     chunksize=1_0_0				,     ):
            if data is not None:
                yield data







def  _UpperCamelCase (     _a	:	Type[Dataset]				,     _a	:	float			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  DuplicationIndex(duplication_jaccard_threshold=_a			)

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_a			)			)				,     max_queue_size=1_0_0			)			):
        di.add(_a				,     _a			)

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







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  get_tokens(_a			)
    __UpperCamelCase	:	str		    =  get_tokens(_a			)
    return len(tokensa & tokensa			) / len(tokensa | tokensa			)


a=  None







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	Optional[Any]			):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	List[Any]				,     _a	:	Union[str, Any]				,     _a	:	Any			):




    """simple docstring"""



    global _shared_dataset
    __UpperCamelCase	:	Any		    =  dataset
    __UpperCamelCase	:	Any		    =  []
    __UpperCamelCase	:	List[str]		    =  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	:	Type[Dataset]				,     _a	:	float = 0.85			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  make_duplicate_clusters(_a				,     _a			)
    __UpperCamelCase	:	List[Any]		    =  {x['base_index'] for cluster in duplicate_clusters for x in cluster}
    __UpperCamelCase	:	Optional[Any]		    =  {}
    __UpperCamelCase	:	Any		    =  find_extremes(_a				,     _a				,     _a			)
    for extremes in extremes_clusters:
        for element in extremes:
            __UpperCamelCase	:	List[Any]		    =  element
    __UpperCamelCase	:	List[str]		    =  duplicate_indices - set(extreme_dict.keys()			)
    __UpperCamelCase	:	Optional[int]		    =  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:
            __UpperCamelCase	:	Optional[int]		    =  element['base_index'] in extreme_dict
            if element["is_extreme"]:
                __UpperCamelCase	:	Optional[Any]		    =  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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a=  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



