



'''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											=						re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
_UpperCamelCase											=						10
_UpperCamelCase											=						256

def        a_     (							_lowerCAmelCase       )      ->      Optional[MinHash]:
  if len(_lowerCAmelCase       ) < MIN_NUM_TOKENS:
    return None
  __lowerCamelCase       :       Tuple           = MinHash(num_perm=_lowerCAmelCase       )
  for token in set(_lowerCAmelCase       ):
    min_hash.update(token.encode()       )
  return min_hash

def        a_     (							_lowerCAmelCase       )      ->      Set[str]:
  return {t for t in NON_ALPHA.split(_lowerCAmelCase       ) if len(t.strip()       ) > 0}






class 					lowerCamelCase_    :
  """simple docstring"""




  def __init__( self      :	int			,	*,
      _a      :	float = 0.85			,	)					->					str:
    __lowerCamelCase       :       List[str]           = duplication_jaccard_threshold
    __lowerCamelCase       :       str           = NUM_PERM
    __lowerCamelCase       :       List[str]           = MinHashLSH(threshold=self._duplication_jaccard_threshold			,	num_perm=self._num_perm   )

    __lowerCamelCase       :       Union[str, Any]           = defaultdict(_a   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Tuple			,	_a      :	MinHash   )					->					None:
    __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 						_lowercase     ( self      :	Any   )					->					List[List[Dict]]:
    __lowerCamelCase       :       Optional[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       :       Optional[int]           = [{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
      duplicate_clusters.append(_a   )
    return duplicate_clusters




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




def        a_     (							_lowerCAmelCase       )      ->      Optional[int]:
  __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = element
  __lowerCamelCase       :       List[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        a_     (							_lowerCAmelCase       )      ->      Dict:
  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       ,_lowerCAmelCase       )      ->      Dict:
  __lowerCamelCase       :       Union[str, 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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Dict           = get_tokens(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = get_tokens(_lowerCAmelCase       )
  return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_UpperCamelCase											=						None

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

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





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_mbart import MBartTokenizer
else:
   _UpperCamelCase											=						None


_UpperCamelCase											=						logging.get_logger(__name__)


_UpperCamelCase											=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase											=						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
_UpperCamelCase											=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=["""input_ids""", """attention_mask"""]
  a_												=MBartTokenizer

  a_												=[]
  a_												=[]




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

    super().__init__(
        vocab_file=_a			,	tokenizer_file=_a			,	bos_token=_a			,	eos_token=_a			,	sep_token=_a			,	cls_token=_a			,	unk_token=_a			,	pad_token=_a			,	mask_token=_a			,	src_lang=_a			,	tgt_lang=_a			,	additional_special_tokens=_a			,	**_a			,	)

    __lowerCamelCase       :       Optional[Any]           = vocab_file
    __lowerCamelCase       :       List[str]           = False if not self.vocab_file else True

    __lowerCamelCase       :       str           = FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

    self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
    __lowerCamelCase       :       Optional[Any]           = {
        lang_code: self.convert_tokens_to_ids(_a   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    __lowerCamelCase       :       Optional[Any]           = src_lang if src_lang is not None else 'en_XX'
    __lowerCamelCase       :       int           = self.convert_tokens_to_ids(self._src_lang   )
    __lowerCamelCase       :       List[str]           = tgt_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  @property
  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    return self._src_lang




  @src_lang.setter
  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = new_src_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




  def 						_lowercase     ( self      :	int			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    __lowerCamelCase       :       Optional[int]           = [self.sep_token_id]
    __lowerCamelCase       :       Union[str, Any]           = [self.cls_token_id]

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




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Optional[Any]			,	_a      :	str			,	_a      :	Optional[str]			,	_a      :	Optional[str]			,	**_a      :	Optional[int]   )					->					Any:
    if src_lang is None or tgt_lang is None:
      raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
    __lowerCamelCase       :       Optional[Any]           = src_lang
    __lowerCamelCase       :       Dict           = self(_a			,	add_special_tokens=_a			,	return_tensors=_a			,	**_a   )
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = tgt_lang_id
    return inputs




  def 						_lowercase     ( self      :	Any			,	_a      :	List[str]			,	_a      :	str = "en_XX"			,	_a      :	Optional[List[str]] = None			,	_a      :	str = "ro_RO"			,	**_a      :	Tuple			,	)					->					BatchEncoding:
    __lowerCamelCase       :       List[Any]           = src_lang
    __lowerCamelCase       :       str           = tgt_lang
    return super().prepare_seqaseq_batch(_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    return self.set_src_lang_special_tokens(self.src_lang   )




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	List[str]   )					->					None:
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       Dict           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Optional[Any]           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Union[str, Any]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       int           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       int           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Any           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Optional[int]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Any			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    if not self.can_save_slow_tokenizer:
      raise ValueError(
          'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
          'tokenizer.'   )

    if not os.path.isdir(_a   ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory.'   )
      return
    __lowerCamelCase       :       List[str]           = os.path.join(
        _a			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

    if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ):
      copyfile(self.vocab_file			,	_a   )

    return (out_vocab_file,)






