



'''simple docstring'''





import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

_lowerCAmelCase      						=	{
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=False							,    UpperCamelCase=True			):



    """simple docstring"""





    if model_type not in MODEL_CLASSES:
        raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        lowerCAmelCase__ :  Tuple							      =					cached_file(UpperCamelCase							,    UpperCamelCase							,    force_download=not use_cached_models			)
    lowerCAmelCase__ :  List[Any]							      =					config_class.from_json_file(UpperCamelCase			)
    lowerCAmelCase__ :  str							      =					True
    lowerCAmelCase__ :  int							      =					True
    print(f"""Building TensorFlow model from configuration: {config}"""			)
    lowerCAmelCase__ :  Optional[int]							      =					model_class(UpperCamelCase			)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        lowerCAmelCase__ :  int							      =					cached_file(
            UpperCamelCase							,    UpperCamelCase							,    force_download=not use_cached_models			)
    # Load PyTorch checkpoint in tf2 model:
    lowerCAmelCase__ :  Any							      =					load_pytorch_checkpoint_in_tfa_model(UpperCamelCase							,    UpperCamelCase			)

    if compare_with_pt_model:
        lowerCAmelCase__ :  Optional[Any]							      =					tf_model(tf_model.dummy_inputs							,    training=UpperCamelCase			)  # build the network

        lowerCAmelCase__ :  List[Any]							      =					torch.load(UpperCamelCase							,    map_location="""cpu"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					pt_model_class.from_pretrained(
            pretrained_model_name_or_path=UpperCamelCase							,    config=UpperCamelCase							,    state_dict=UpperCamelCase			)

        with torch.no_grad():
            lowerCAmelCase__ :  int							      =					pt_model(**pt_model.dummy_inputs			)

        lowerCAmelCase__ :  Dict							      =					pto[0].numpy()
        lowerCAmelCase__ :  Union[str, Any]							      =					tfo[0].numpy()
        lowerCAmelCase__ :  Any							      =					np.amax(np.abs(np_pt - np_tf			)			)
        print(f"""Max absolute difference between models outputs {diff}"""			)
        assert diff <= 2e-2, f"""Error, model absolute difference is >2e-2: {diff}"""

    # Save pytorch-model
    print(f"""Save TensorFlow model to {tf_dump_path}"""			)
    tf_model.save_weights(UpperCamelCase							,    save_format="""h5"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=False							,    UpperCamelCase=False							,    UpperCamelCase=False							,    UpperCamelCase=False							,    ):



    """simple docstring"""





    if args_model_type is None:
        lowerCAmelCase__ :  int							      =					list(MODEL_CLASSES.keys()			)
    else:
        lowerCAmelCase__ :  List[str]							      =					[args_model_type]

    for j, model_type in enumerate(UpperCamelCase							,    start=1			):
        print("""=""" * 100			)
        print(f""" Converting model type {j}/{len(UpperCamelCase			)}: {model_type}"""			)
        print("""=""" * 100			)
        if model_type not in MODEL_CLASSES:
            raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}."""			)

        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            lowerCAmelCase__ :  List[str]							      =					list(aws_model_maps.keys()			)
        if config_shortcut_names_or_path is None:
            lowerCAmelCase__ :  int							      =					model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(UpperCamelCase							,    UpperCamelCase			)							,    start=1			):
            print("""-""" * 100			)
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""			)
                    continue
                lowerCAmelCase__ :  str							      =					model_shortcut_name
            elif only_convert_finetuned_models:
                print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""			)
                continue
            print(
                f"""    Converting checkpoint {i}/{len(UpperCamelCase			)}: {model_shortcut_name} - model_type {model_type}"""			)
            print("""-""" * 100			)

            if config_shortcut_name in aws_config_map:
                lowerCAmelCase__ :  List[Any]							      =					cached_file(UpperCamelCase							,    UpperCamelCase							,    force_download=not use_cached_models			)
            else:
                lowerCAmelCase__ :  Tuple							      =					config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                lowerCAmelCase__ :  List[Any]							      =					cached_file(UpperCamelCase							,    UpperCamelCase							,    force_download=not use_cached_models			)
            else:
                lowerCAmelCase__ :  Optional[int]							      =					model_shortcut_name

            if os.path.isfile(UpperCamelCase			):
                lowerCAmelCase__ :  int							      =					"""converted_model"""

            convert_pt_checkpoint_to_tf(
                model_type=UpperCamelCase							,    pytorch_checkpoint_path=UpperCamelCase							,    config_file=UpperCamelCase							,    tf_dump_path=os.path.join(UpperCamelCase							,    model_shortcut_name + """-tf_model.h5"""			)							,    compare_with_pt_model=UpperCamelCase							,    )
            if remove_cached_files:
                os.remove(UpperCamelCase			)
                os.remove(UpperCamelCase			)


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
 )
 parser.add_argument(
     '''--model_type''',
     default=None,
     type=str,
     help=(
         F"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
         '''convert all the models from AWS.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_checkpoint_path''',
     default=None,
     type=str,
     help=(
         '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
         '''If not given, will download and convert all the checkpoints from AWS.'''
     ),
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     help=(
         '''The config json file corresponding to the pre-trained model. \n'''
         '''This specifies the model architecture. If not given and '''
         '''--pytorch_checkpoint_path is not given or is a shortcut name '''
         '''use the configuration associated to the shortcut name on the AWS'''
     ),
 )
 parser.add_argument(
     '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
 )
 parser.add_argument(
     '''--use_cached_models''',
     action='''store_true''',
     help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
 )
 parser.add_argument(
     '''--remove_cached_files''',
     action='''store_true''',
     help='''Remove pytorch models after conversion (save memory when converting in batches).''',
 )
 parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
 _lowerCAmelCase      						=	parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )



"""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      :		Union[str, Any]           =   re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
_a      :		List[str]           =   10
_a      :		List[Any]           =   256





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Optional[MinHash]:
  if len(_lowerCamelCase	) < MIN_NUM_TOKENS:
    return None
  _lowerCAmelCase						:       Optional[Any]  			=     MinHash(num_perm=_lowerCamelCase	)
  for token in set(_lowerCamelCase	):
    min_hash.update(token.encode()	)
  return min_hash





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Set[str]:
  return {t for t in NON_ALPHA.split(_lowerCamelCase	) if len(t.strip()	) > 0}
class 						__A							:






     def __init__(		self				,     *,
         a__ = 0.8_5				,     ):
       _lowerCAmelCase						:       List[Any]  			=     duplication_jaccard_threshold
       _lowerCAmelCase						:       Union[str, Any]  			=     NUM_PERM
       _lowerCAmelCase						:       Optional[int]  			=     MinHashLSH(threshold=self._duplication_jaccard_threshold				,     num_perm=self._num_perm  )

       _lowerCAmelCase						:       Optional[int]  			=     defaultdict(a__  )






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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     self.get_duplicate_clusters()
       with open(a__				,     """w"""  ) as f:
         json.dump(a__				,     a__  )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      Tuple:
  _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       Tuple  			=     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     (			_lowerCamelCase						:       Type[Dataset]	)       ->      Optional[Any]:
  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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float	)       ->      List[str]:
  _lowerCAmelCase						:       Optional[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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      float:
  _lowerCAmelCase						:       Any  			=     get_tokens(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     get_tokens(_lowerCamelCase	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


_a      :		str           =   None





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





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




