





'''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__				:   str      =re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
A__				:   Any      =10
A__				:   Optional[int]      =2_56
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      if len(lowerCAmelCase  ) < MIN_NUM_TOKENS:
            return None
      _lowerCAmelCase							     =							MinHash(num_perm=lowerCAmelCase  )
      for token in set(lowerCAmelCase  ):
            min_hash.update(token.encode()  )
      return min_hash
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      return {t for t in NON_ALPHA.split(lowerCAmelCase  ) if len(t.strip()  ) > 0}




class  UpperCAmelCase     :




      def __init__(			self			: Any			,			*,
          __snake_case			: float = 0.85			,			)				->      List[Any]:
            _lowerCAmelCase							     =							duplication_jaccard_threshold
            _lowerCAmelCase							     =							NUM_PERM
            _lowerCAmelCase							     =							MinHashLSH(threshold=self._duplication_jaccard_threshold			,			num_perm=self._num_perm  )

            _lowerCAmelCase							     =							defaultdict(__snake_case  )




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: Tuple			,			__snake_case			: MinHash  )				->      None:
            _lowerCAmelCase							     =							self._index.query(__snake_case  )
            if code_key in self._index.keys:
                  print(f"Duplicate key {code_key}"  )
                  return

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




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




      def 				lowercase__    (			self			: str			,			__snake_case			: Any  )				->      None:
            _lowerCAmelCase							     =							self.get_duplicate_clusters()
            with open(__snake_case			,			"""w"""  ) as f:
                  json.dump(__snake_case			,			__snake_case  )




def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase     ,							_lowerCAmelCase							     =							element
      _lowerCAmelCase							     =							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__      (		lowerCAmelCase  ):





      """simple docstring"""


      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash							,	ThreadedIterator(lowerCAmelCase							,	max_queue_size=1_00_00  )							,	chunksize=1_00							,	):
                  if data is not None:
                        yield data
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase  )

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

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							get_tokens(lowerCAmelCase  )
      _lowerCAmelCase							     =							get_tokens(lowerCAmelCase  )
      return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


A__				:   Tuple      =None
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      for elementa in cluster:
            _lowerCAmelCase							     =							_shared_dataset[elementa["""base_index"""]]["""content"""]
            for elementa in extremes:
                  _lowerCAmelCase							     =							_shared_dataset[elementa["""base_index"""]]["""content"""]
                  if jaccard_similarity(lowerCAmelCase							,	lowerCAmelCase  ) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _lowerCAmelCase							     =							1
                  extremes.append(lowerCAmelCase  )
      return extremes
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      global _shared_dataset
      _lowerCAmelCase							     =							dataset
      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							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    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase = 0.85  ):





      """simple docstring"""


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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



