

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase         =						{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      pass

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoCBertForCausalLM",
          "RoCBertForMaskedLM",
          "RoCBertForMultipleChoice",
          "RoCBertForPreTraining",
          "RoCBertForQuestionAnswering",
          "RoCBertForSequenceClassification",
          "RoCBertForTokenClassification",
          "RoCBertLayer",
          "RoCBertModel",
          "RoCBertPreTrainedModel",
          "load_tf_weights_in_roc_bert",
      ]

if TYPE_CHECKING:
      from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
      from .tokenization_roc_bert import RoCBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            raise OptionalDependencyNotAvailable()

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roc_bert import (
                ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoCBertForCausalLM,
                RoCBertForMaskedLM,
                RoCBertForMultipleChoice,
                RoCBertForPreTraining,
                RoCBertForQuestionAnswering,
                RoCBertForSequenceClassification,
                RoCBertForTokenClassification,
                RoCBertLayer,
                RoCBertModel,
                RoCBertPreTrainedModel,
                load_tf_weights_in_roc_bert,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''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


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




def 						UpperCamelCase	(			a			)				->     Optional[MinHash]:






     '''simple docstring'''
     if len(a			) < MIN_NUM_TOKENS:
          return None
     __magic_name__						       =    MinHash(num_perm=a			)
     for token in set(a			):
          min_hash.update(token.encode()			)
     return min_hash




def 						UpperCamelCase	(			a			)				->     Set[str]:






     '''simple docstring'''
     return {t for t in NON_ALPHA.split(a			) if len(t.strip()			) > 0}






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Any			,				*,
     a__		:   float = 0.85			,				):
      __magic_name__						       =    duplication_jaccard_threshold
      __magic_name__						       =    NUM_PERM
      __magic_name__						       =    MinHashLSH(threshold=self._duplication_jaccard_threshold			,				num_perm=self._num_perm      )

      __magic_name__						       =    defaultdict(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   MinHash      ):
      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    []
      for base, duplicates in self._duplicate_clusters.items():
           __magic_name__						       =    [base] + list(a__      )
           # reformat the cluster to be a list of dict
           __magic_name__						       =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(a__      )
      return duplicate_clusters



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      __magic_name__						       =    self.get_duplicate_clusters()
      with open(a__			,				'''w'''      ) as f:
           json.dump(a__			,				a__      )




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    element
     __magic_name__						       =    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			)				->     List[Any]:






     '''simple docstring'''
     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 						UpperCamelCase	(			a		,						a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     float:






     '''simple docstring'''
     __magic_name__						       =    get_tokens(a			)
     __magic_name__						       =    get_tokens(a			)
     return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCAmelCase         =						None




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    []
     for elementa in cluster:
          __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(a		,						a			) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               __magic_name__						       =    1
               extremes.append(a			)
     return extremes




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     global _shared_dataset
     __magic_name__						       =    dataset
     __magic_name__						       =    []
     __magic_name__						       =    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 = 0.85			)				->     Tuple[Type[Dataset], List[List[Dict]]]:






     '''simple docstring'''
     __magic_name__						       =    make_duplicate_clusters(a		,						a			)
     __magic_name__						       =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     __magic_name__						       =    {}
     __magic_name__						       =    find_extremes(a		,						a		,						a			)
     for extremes in extremes_clusters:
          for element in extremes:
               __magic_name__						       =    element
     __magic_name__						       =    duplicate_indices - set(extreme_dict.keys()			)
     __magic_name__						       =    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:
               __magic_name__						       =    element['''base_index'''] in extreme_dict
               if element["is_extreme"]:
                    __magic_name__						       =    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






