





"""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_        =						1_0
lowerCamelCase_        =						2_5_6



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



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






class    _SCREAMING_SNAKE_CASE:




       def __init__(		self						,*,
           SCREAMING_SNAKE_CASE__ = 0.8_5						,)     -> List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :Any													=    duplication_jaccard_threshold
             __SCREAMING_SNAKE_CASE  :str													=    NUM_PERM
             __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    MinHashLSH(threshold=self._duplication_jaccard_threshold						,num_perm=self._num_perm				)

             __SCREAMING_SNAKE_CASE  :Any													=    defaultdict(a_				)




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__						,SCREAMING_SNAKE_CASE__				)     -> None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :Dict													=    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        _UpperCamelCase	(		self				)     -> List[List[Dict]]:





             """simple docstring"""






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




       def        _UpperCamelCase	(		self						,SCREAMING_SNAKE_CASE__				)     -> None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE  :Tuple													=    self.get_duplicate_clusters()
             with open(a_						,'''w'''				) as f:
                   json.dump(a_						,a_				)




def 	__lowerCamelCase  (				a_	:						List[Any]				)     -> Dict:
      __SCREAMING_SNAKE_CASE  :List[str]													=    element
      __SCREAMING_SNAKE_CASE  :int													=    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 	__lowerCamelCase  (				a_	:						int				)     -> Any:
      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash					,  ThreadedIterator(a_					,  max_queue_size=1_00_00				)					,  chunksize=1_00					,  ):
                  if data is not None:
                        yield data



def 	__lowerCamelCase  (				a_	:						List[Any]					,  a_	:						Union[str, Any]				)     -> List[str]:
      __SCREAMING_SNAKE_CASE  :Dict													=    DuplicationIndex(duplication_jaccard_threshold=a_				)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a_				)				)					,  max_queue_size=1_00				)				):
            di.add(a_					,  a_				)

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



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


lowerCamelCase_        =						None



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



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


lowerCAmelCase   : List[Any]             =				{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Any             =				["""AlbertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[Any]             =				["""AlbertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : List[str]             =				[
					    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AlbertForMaskedLM""",
					    """AlbertForMultipleChoice""",
					    """AlbertForPreTraining""",
					    """AlbertForQuestionAnswering""",
					    """AlbertForSequenceClassification""",
					    """AlbertForTokenClassification""",
					    """AlbertModel""",
					    """AlbertPreTrainedModel""",
					    """load_tf_weights_in_albert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Tuple             =				[
					    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFAlbertForMaskedLM""",
					    """TFAlbertForMultipleChoice""",
					    """TFAlbertForPreTraining""",
					    """TFAlbertForQuestionAnswering""",
					    """TFAlbertForSequenceClassification""",
					    """TFAlbertForTokenClassification""",
					    """TFAlbertMainLayer""",
					    """TFAlbertModel""",
					    """TFAlbertPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[int]             =				[
					    """FlaxAlbertForMaskedLM""",
					    """FlaxAlbertForMultipleChoice""",
					    """FlaxAlbertForPreTraining""",
					    """FlaxAlbertForQuestionAnswering""",
					    """FlaxAlbertForSequenceClassification""",
					    """FlaxAlbertForTokenClassification""",
					    """FlaxAlbertModel""",
					    """FlaxAlbertPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase   : Union[str, Any]             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


