



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''camembert'''





   def __init__(	self       ,     _lowerCAmelCase=30_522       ,     _lowerCAmelCase=768       ,     _lowerCAmelCase=12       ,     _lowerCAmelCase=12       ,     _lowerCAmelCase=3_072       ,     _lowerCAmelCase="gelu"       ,     _lowerCAmelCase=0.1       ,     _lowerCAmelCase=0.1       ,     _lowerCAmelCase=512       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase=0.02       ,     _lowerCAmelCase=1E-12       ,     _lowerCAmelCase=1       ,     _lowerCAmelCase=0       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase="absolute"       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase       ,     ):
         super().__init__(pad_token_id=_lowerCAmelCase       ,     bos_token_id=_lowerCAmelCase       ,     eos_token_id=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         _lowerCAmelCase 			=  vocab_size
         _lowerCAmelCase 			=  hidden_size
         _lowerCAmelCase 			=  num_hidden_layers
         _lowerCAmelCase 			=  num_attention_heads
         _lowerCAmelCase 			=  hidden_act
         _lowerCAmelCase 			=  intermediate_size
         _lowerCAmelCase 			=  hidden_dropout_prob
         _lowerCAmelCase 			=  attention_probs_dropout_prob
         _lowerCAmelCase 			=  max_position_embeddings
         _lowerCAmelCase 			=  type_vocab_size
         _lowerCAmelCase 			=  initializer_range
         _lowerCAmelCase 			=  layer_norm_eps
         _lowerCAmelCase 			=  position_embedding_type
         _lowerCAmelCase 			=  use_cache
         _lowerCAmelCase 			=  classifier_dropout






class 		UpperCAmelCase						(						snake_case_				):





   @property
   def 	__lowerCAmelCase							(	self			):
         if self.task == "multiple-choice":
               _lowerCAmelCase 			=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
               _lowerCAmelCase 			=  {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]			)




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_									=							1_0
UpperCAmelCase_									=							2_5_6
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]				)->Optional[MinHash]:
      if len(_SCREAMING_SNAKE_CASE				) < MIN_NUM_TOKENS:
            return None
      _lowerCAmelCase 			=  MinHash(num_perm=_SCREAMING_SNAKE_CASE				)
      for token in set(_SCREAMING_SNAKE_CASE				):
            min_hash.update(token.encode()				)
      return min_hash
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str				)->Set[str]:
      return {t for t in NON_ALPHA.split(_SCREAMING_SNAKE_CASE				) if len(t.strip()				) > 0}





class 		UpperCAmelCase						:





   def __init__(	self       ,     *,
       _lowerCAmelCase = 0.85       ,     ):
         _lowerCAmelCase 			=  duplication_jaccard_threshold
         _lowerCAmelCase 			=  NUM_PERM
         _lowerCAmelCase 			=  MinHashLSH(threshold=self._duplication_jaccard_threshold       ,     num_perm=self._num_perm			)

         _lowerCAmelCase 			=  defaultdict(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self._index.query(_lowerCAmelCase			)
         if code_key in self._index.keys:
               print(F'''Duplicate key {code_key}'''			)
               return

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  []
         for base, duplicates in self._duplicate_clusters.items():
               _lowerCAmelCase 			=  [base] + list(_lowerCAmelCase			)
               # 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(_lowerCAmelCase			)
         return duplicate_clusters





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.get_duplicate_clusters()
         with open(_lowerCAmelCase       ,     '''w'''			) as f:
               json.dump(_lowerCAmelCase       ,     _lowerCAmelCase			)






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str				)->Optional[Any]:
      _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__		(				_SCREAMING_SNAKE_CASE   :	Type[Dataset]				)->Any:
      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash     ,				ThreadedIterator(_SCREAMING_SNAKE_CASE     ,				max_queue_size=1_0_0_0_0				)     ,				chunksize=1_0_0     ,				):
                  if data is not None:
                        yield data
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Type[Dataset]     ,				_SCREAMING_SNAKE_CASE   :	float				)->str:
      _lowerCAmelCase 			=  DuplicationIndex(duplication_jaccard_threshold=_SCREAMING_SNAKE_CASE				)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_SCREAMING_SNAKE_CASE				)				)     ,				max_queue_size=1_0_0				)				):
            di.add(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str				)->float:
      _lowerCAmelCase 			=  get_tokens(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  get_tokens(_SCREAMING_SNAKE_CASE				)
      return len(tokensa & tokensa				) / len(tokensa | tokensa				)


UpperCAmelCase_									=							None
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->List[Any]:
      _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(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _lowerCAmelCase 			=  1
                  extremes.append(_SCREAMING_SNAKE_CASE				)
      return extremes
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	str				)->Tuple:
      global _shared_dataset
      _lowerCAmelCase 			=  dataset
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  partial(_find_cluster_extremes_shared     ,				jaccard_threshold=_SCREAMING_SNAKE_CASE				)
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				)     ,				total=len(_SCREAMING_SNAKE_CASE				)     ,				):
                  extremes_list.append(_SCREAMING_SNAKE_CASE				)
      return extremes_list


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Type[Dataset]     ,				_SCREAMING_SNAKE_CASE   :	float = 0.85				)->Tuple[Type[Dataset], List[List[Dict]]]:
      _lowerCAmelCase 			=  make_duplicate_clusters(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
      _lowerCAmelCase 			=  {}
      _lowerCAmelCase 			=  find_extremes(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      for extremes in extremes_clusters:
            for element in extremes:
                  _lowerCAmelCase 			=  element
      _lowerCAmelCase 			=  duplicate_indices - set(extreme_dict.keys()				)
      _lowerCAmelCase 			=  dataset.filter(lambda _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				: idx not in remove_indices     ,				with_indices=_SCREAMING_SNAKE_CASE				)

      # 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(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Number of duplicate clusters: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Unique files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE				)}'''				)
      print(f'''Filtered dataset size: {len(_SCREAMING_SNAKE_CASE				)}'''				)

      return ds_filter, duplicate_clusters