


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


SCREAMING_SNAKE_CASE						:Optional[int]			=  re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
SCREAMING_SNAKE_CASE						:Tuple			=  10
SCREAMING_SNAKE_CASE						:Any			=  2_56



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]				)->Optional[MinHash]:
   '''simple docstring'''






   if len(lowerCAmelCase_				) < MIN_NUM_TOKENS:
      return None
   snake_case_            =       MinHash(num_perm=lowerCAmelCase_				)
   for token in set(lowerCAmelCase_				):
      min_hash.update(token.encode()				)
   return min_hash



def       _lowerCAmelCase    (				lowerCAmelCase_				:str				)->Set[str]:
   '''simple docstring'''






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


class      __lowerCAmelCase  :






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					*,
      _lowerCAmelCase							:							float = 0.85     ,					) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       duplication_jaccard_threshold
     snake_case_            =       NUM_PERM
     snake_case_            =       MinHashLSH(threshold=self._duplication_jaccard_threshold     ,					num_perm=self._num_perm	)

     snake_case_            =       defaultdict(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							MinHash	) ->			None:




     """simple docstring"""
     snake_case_            =       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							:							Tuple	) ->			List[List[Dict]]:




     """simple docstring"""
     snake_case_            =       []
     for base, duplicates in self._duplicate_clusters.items():
        snake_case_            =       [base] + list(_lowerCAmelCase	)
        # reformat the cluster to be a list of dict
        snake_case_            =       [{"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							:							Optional[int]     ,					_lowerCAmelCase							:							Any	) ->			None:




     """simple docstring"""
     snake_case_            =       self.get_duplicate_clusters()
     with open(_lowerCAmelCase     ,					"w"	) as f:
        json.dump(_lowerCAmelCase     ,					_lowerCAmelCase	)






def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]				)->int:
   '''simple docstring'''






   snake_case_     ,     snake_case_            =       element
   snake_case_            =       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    (				lowerCAmelCase_				:Type[Dataset]				)->Union[str, Any]:
   '''simple docstring'''






   with mp.Pool() as pool:
      for data in pool.imap_unordered(
          _compute_min_hash		,    ThreadedIterator(lowerCAmelCase_		,    max_queue_size=10_000				)		,    chunksize=100		,    ):
         if data is not None:
            yield data



def       _lowerCAmelCase    (				lowerCAmelCase_				:Type[Dataset]		,    lowerCAmelCase_				:float				)->Dict:
   '''simple docstring'''






   snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->float:
   '''simple docstring'''






   snake_case_            =       get_tokens(lowerCAmelCase_				)
   snake_case_            =       get_tokens(lowerCAmelCase_				)
   return len(tokensa & tokensa				) / len(tokensa | tokensa				)


SCREAMING_SNAKE_CASE						:int			=  None



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Dict				)->Tuple:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:int				)->Tuple:
   '''simple docstring'''






   global _shared_dataset
   snake_case_            =       dataset
   snake_case_            =       []
   snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:Type[Dataset]		,    lowerCAmelCase_				:float = 0.8_5				)->Tuple[Type[Dataset], List[List[Dict]]]:
   '''simple docstring'''






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








import functools
import operator

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


SCREAMING_SNAKE_CASE						:List[str]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wav2vec2'



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]=3_2     ,					_lowerCAmelCase							:							List[Any]=7_6_8     ,					_lowerCAmelCase							:							Dict=1_2     ,					_lowerCAmelCase							:							Tuple=1_2     ,					_lowerCAmelCase							:							Optional[int]=3_0_7_2     ,					_lowerCAmelCase							:							Any="gelu"     ,					_lowerCAmelCase							:							List[str]=0.1     ,					_lowerCAmelCase							:							Dict=0.1     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.0     ,					_lowerCAmelCase							:							Optional[int]=0.0     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.02     ,					_lowerCAmelCase							:							Any=1e-5     ,					_lowerCAmelCase							:							Any="group"     ,					_lowerCAmelCase							:							Union[str, Any]="gelu"     ,					_lowerCAmelCase							:							str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,					_lowerCAmelCase							:							Optional[Any]=(5, 2, 2, 2, 2, 2, 2)     ,					_lowerCAmelCase							:							Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,					_lowerCAmelCase							:							int=False     ,					_lowerCAmelCase							:							int=1_2_8     ,					_lowerCAmelCase							:							Union[str, Any]=1_6     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							Dict=0.05     ,					_lowerCAmelCase							:							List[str]=1_0     ,					_lowerCAmelCase							:							Tuple=2     ,					_lowerCAmelCase							:							str=0.0     ,					_lowerCAmelCase							:							Union[str, Any]=1_0     ,					_lowerCAmelCase							:							int=0     ,					_lowerCAmelCase							:							Optional[int]=3_2_0     ,					_lowerCAmelCase							:							Optional[Any]=2     ,					_lowerCAmelCase							:							str=0.1     ,					_lowerCAmelCase							:							Optional[Any]=1_0_0     ,					_lowerCAmelCase							:							Tuple=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Optional[Any]="sum"     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							List[str]=2_5_6     ,					_lowerCAmelCase							:							List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,					_lowerCAmelCase							:							List[str]=(5, 3, 3, 1, 1)     ,					_lowerCAmelCase							:							Union[str, Any]=(1, 2, 3, 1, 1)     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Optional[int]=1     ,					_lowerCAmelCase							:							List[str]=2     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							List[Any]=2     ,					_lowerCAmelCase							:							Optional[Any]=3     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase     ,					pad_token_id=_lowerCAmelCase     ,					bos_token_id=_lowerCAmelCase     ,					eos_token_id=_lowerCAmelCase	)
     snake_case_            =       hidden_size
     snake_case_            =       feat_extract_norm
     snake_case_            =       feat_extract_activation
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       conv_bias
     snake_case_            =       num_conv_pos_embeddings
     snake_case_            =       num_conv_pos_embedding_groups
     snake_case_            =       len(self.conv_dim	)
     snake_case_            =       num_hidden_layers
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       num_attention_heads
     snake_case_            =       hidden_dropout
     snake_case_            =       attention_dropout
     snake_case_            =       activation_dropout
     snake_case_            =       feat_proj_dropout
     snake_case_            =       final_dropout
     snake_case_            =       layerdrop
     snake_case_            =       layer_norm_eps
     snake_case_            =       initializer_range
     snake_case_            =       vocab_size
     snake_case_            =       do_stable_layer_norm
     snake_case_            =       use_weighted_layer_sum

     if (
         (len(self.conv_stride	) != self.num_feat_extract_layers)
         or (len(self.conv_kernel	) != self.num_feat_extract_layers)
         or (len(self.conv_dim	) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
            " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
            F''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
            F''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     snake_case_            =       apply_spec_augment
     snake_case_            =       mask_time_prob
     snake_case_            =       mask_time_length
     snake_case_            =       mask_time_min_masks
     snake_case_            =       mask_feature_prob
     snake_case_            =       mask_feature_length
     snake_case_            =       mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     snake_case_            =       num_codevectors_per_group
     snake_case_            =       num_codevector_groups
     snake_case_            =       contrastive_logits_temperature
     snake_case_            =       feat_quantizer_dropout
     snake_case_            =       num_negatives
     snake_case_            =       codevector_dim
     snake_case_            =       proj_codevector_dim
     snake_case_            =       diversity_loss_weight

     # ctc loss
     snake_case_            =       ctc_loss_reduction
     snake_case_            =       ctc_zero_infinity

     # adapter
     snake_case_            =       add_adapter
     snake_case_            =       adapter_kernel_size
     snake_case_            =       adapter_stride
     snake_case_            =       num_adapter_layers
     snake_case_            =       output_hidden_size or hidden_size
     snake_case_            =       adapter_attn_dim

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     snake_case_            =       classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       xvector_output_dim



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




     """simple docstring"""
     return functools.reduce(operator.mul     ,					self.conv_stride     ,					1	)




