




'''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_ 		=      2_56


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  if len(a_			) < MIN_NUM_TOKENS:
    return None
  __a			=					MinHash(num_perm=a_			)
  for token in set(a_			):
    min_hash.update(token.encode()			)
  return min_hash


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  return {t for t in NON_ALPHA.split(a_			) if len(t.strip()			) > 0}





class    __lowercase						:







   def __init__(	self      ,							*,
       UpperCamelCase = 0.85      ,							)				->    Optional[Any]:
     __a			=					duplication_jaccard_threshold
     __a			=					NUM_PERM
     __a			=					MinHashLSH(threshold=self._duplication_jaccard_threshold      ,							num_perm=self._num_perm      )

     __a			=					defaultdict(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    None:
     __a			=					self._index.query(UpperCamelCase      )
     if code_key in self._index.keys:
       print(f"Duplicate key {code_key}"      )
       return

     self._index.insert(UpperCamelCase      ,							UpperCamelCase      )
     if len(UpperCamelCase      ) > 0:
       for base_duplicate in close_duplicates:
         if base_duplicate in self._duplicate_clusters:
           self._duplicate_clusters[base_duplicate].add(UpperCamelCase      )
           break
       else:
         self._duplicate_clusters[close_duplicates[0]].add(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[List[Dict]]:
     __a			=					[]
     for base, duplicates in self._duplicate_clusters.items():
       __a			=					[base] + list(UpperCamelCase      )
       # reformat the cluster to be a list of dict
       __a			=					[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
       duplicate_clusters.append(UpperCamelCase      )
     return duplicate_clusters







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.get_duplicate_clusters()
     with open(UpperCamelCase      ,							'w'      ) as f:
       json.dump(UpperCamelCase      ,							UpperCamelCase      )


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]			):
  __a	,       __a			=					element
  __a			=					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       (      a_	:							Type[Dataset]			):
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash				,					ThreadedIterator(a_				,					max_queue_size=1_0000			)				,					chunksize=100				,					):
      if data is not None:
        yield data


def 						SCREAMING_SNAKE_CASE       (      a_	:							Type[Dataset]				,					a_	:							float			):
  __a			=					DuplicationIndex(duplication_jaccard_threshold=a_			)

  for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a_			)			)				,					max_queue_size=100			)			):
    di.add(a_				,					a_			)

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							str			):
  __a			=					get_tokens(a_			)
  __a			=					get_tokens(a_			)
  return len(tokensa & tokensa			) / len(tokensa | tokensa			)


UpperCAmelCase_ 		=      None


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]				,					a_	:							str			):
  __a			=					[]
  for elementa in cluster:
    __a			=					_shared_dataset[elementa['base_index']]['content']
    for elementa in extremes:
      __a			=					_shared_dataset[elementa['base_index']]['content']
      if jaccard_similarity(a_				,					a_			) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      __a			=					1
      extremes.append(a_			)
  return extremes


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


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

import sentencepiece as spm

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      "▁"

UpperCAmelCase_ 		=      {"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
UpperCAmelCase_ 		=      ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      ["""input_ids""", """attention_mask"""]

   _a							=      []
   _a							=      []







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="<unk>"      ,							UpperCamelCase="<pad>"      ,							UpperCamelCase="<mask>"      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase = None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     __a			=					AddedToken(UpperCamelCase      ,							lstrip=UpperCamelCase      ,							rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase      ,							UpperCamelCase      ) else mask_token

     __a			=					{} if sp_model_kwargs is None else sp_model_kwargs
     __a			=					legacy_behaviour

     super().__init__(
         bos_token=UpperCamelCase      ,							eos_token=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							src_lang=UpperCamelCase      ,							tgt_lang=UpperCamelCase      ,							additional_special_tokens=UpperCamelCase      ,							sp_model_kwargs=self.sp_model_kwargs      ,							legacy_behaviour=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(str(UpperCamelCase      )      )
     __a			=					vocab_file
     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
     # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
     # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

     # Mimic fairseq token-to-id alignment for the first 4 token
     __a			=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     __a			=					1

     __a			=					len(self.sp_model      )
     __a			=					{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase      )
     }
     __a			=					{v: k for k, v in self.lang_code_to_id.items()}
     __a			=					len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
     __a			=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     __a			=					list(self.lang_code_to_id.keys()      )

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

     __a			=					src_lang if src_lang is not None else 'eng_Latn'
     __a			=					self.lang_code_to_id[self._src_lang]
     __a			=					tgt_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def __getstate__(	self      )				->    Union[str, Any]:
     __a			=					self.__dict__.copy()
     __a			=					None
     __a			=					self.sp_model.serialized_model_proto()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    str:
     __a			=					d

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token







   @property
   def 		UpperCamelCase__			(	self      )				->    str:
     return self._src_lang







   @src_lang.setter
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					new_src_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    List[int]:

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

     __a			=					[1] * len(self.prefix_tokens      )
     __a			=					[1] * len(self.suffix_tokens      )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(UpperCamelCase      )) + suffix_ones
     return prefix_ones + ([0] * len(UpperCamelCase      )) + ([0] * len(UpperCamelCase      )) + suffix_ones







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Dict:
     if src_lang is None or tgt_lang is None:
       raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'      )
     __a			=					src_lang
     __a			=					self(UpperCamelCase      ,							add_special_tokens=UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     __a			=					self.convert_tokens_to_ids(UpperCamelCase      )
     __a			=					tgt_lang_id
     return inputs







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					{self.convert_ids_to_tokens(UpperCamelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     return self.sp_model.encode(UpperCamelCase      ,							out_type=UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __a			=					self.sp_model.PieceToId(UpperCamelCase      )

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:
     __a			=					''.join(UpperCamelCase      ).replace(UpperCamelCase      ,							' '      ).strip()
     return out_string







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

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

     return (out_vocab_file,)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = "eng_Latn"      ,							UpperCamelCase = None      ,							UpperCamelCase = "fra_Latn"      ,							**UpperCamelCase      ,							)				->    BatchEncoding:
     __a			=					src_lang
     __a			=					tgt_lang
     return super().prepare_seqaseq_batch(UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return self.set_src_lang_special_tokens(self.src_lang      )







   def 		UpperCamelCase__			(	self      )				->    int:
     return self.set_tgt_lang_special_tokens(self.tgt_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[src_lang]
     if self.legacy_behaviour:
       __a			=					[]
       __a			=					[self.eos_token_id, self.cur_lang_code]
     else:
       __a			=					[self.cur_lang_code]
       __a			=					[self.eos_token_id]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.lang_code_to_id[lang]
     if self.legacy_behaviour:
       __a			=					[]
       __a			=					[self.eos_token_id, self.cur_lang_code]
     else:
       __a			=					[self.cur_lang_code]
       __a			=					[self.eos_token_id]



