

'''simple docstring'''




from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}






class   _SCREAMING_SNAKE_CASE   (   UpperCAmelCase__     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					'efficientnet'



 def __init__(   self		:   str			,				a__		:   int = 3			,				a__		:   Union[str, Any] = 600			,				a__		:   List[str] = 2.0			,				a__		:   Optional[int] = 3.1			,				a__		:   Union[str, Any] = 8			,				a__		:   Dict = [3, 3, 5, 3, 5, 5, 3]			,				a__		:   List[Any] = [32, 16, 24, 40, 80, 112, 192]			,				a__		:   Optional[int] = [16, 24, 40, 80, 112, 192, 320]			,				a__		:   List[Any] = []			,				a__		:   Tuple = [1, 2, 2, 2, 1, 2, 1]			,				a__		:   Union[str, Any] = [1, 2, 2, 3, 3, 4, 1]			,				a__		:   Optional[Any] = [1, 6, 6, 6, 6, 6, 6]			,				a__		:   Optional[Any] = 0.25			,				a__		:   Union[str, Any] = "swish"			,				a__		:   Dict = 2560			,				a__		:   str = "mean"			,				a__		:   List[Any] = 0.02			,				a__		:   List[Any] = 0.001			,				a__		:   Any = 0.99			,				a__		:   Tuple = 0.5			,				a__		:   Union[str, Any] = 0.2			,				**a__		:   Optional[Any]			,				):
      super().__init__(**_SCREAMING_SNAKE_CASE      )

      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    width_coefficient
      __magic_name__						       =    depth_coefficient
      __magic_name__						       =    depth_divisor
      __magic_name__						       =    kernel_sizes
      __magic_name__						       =    in_channels
      __magic_name__						       =    out_channels
      __magic_name__						       =    depthwise_padding
      __magic_name__						       =    strides
      __magic_name__						       =    num_block_repeats
      __magic_name__						       =    expand_ratios
      __magic_name__						       =    squeeze_expansion_ratio
      __magic_name__						       =    hidden_act
      __magic_name__						       =    hidden_dim
      __magic_name__						       =    pooling_type
      __magic_name__						       =    initializer_range
      __magic_name__						       =    batch_norm_eps
      __magic_name__						       =    batch_norm_momentum
      __magic_name__						       =    dropout_rate
      __magic_name__						       =    drop_connect_rate
      __magic_name__						       =    sum(_SCREAMING_SNAKE_CASE      ) * 4






class   _SCREAMING_SNAKE_CASE   (   UpperCAmelCase__     ):
 __SCREAMING_SNAKE_CASE					:Dict      	=					version.parse("""1.11"""     )



 @property
 def 			snake_case__  (   self		:   Optional[int]      ):
      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]      )



 @property
 def 			snake_case__  (   self		:   int      ):
      return 1E-5









'''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






