


"""simple docstring"""


from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_snake_case          =							logging.get_logger(__name__)



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Tuple             =    ['input_features', 'attention_mask']
    def __init__( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Tuple=80							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=1_60_00							,					SCREAMING_SNAKE_CASE__							:					List[str]=0.0							,					SCREAMING_SNAKE_CASE__							:					Tuple=10							,					SCREAMING_SNAKE_CASE__							:					int=25							,					SCREAMING_SNAKE_CASE__							:					str="hamming_window"							,					SCREAMING_SNAKE_CASE__							:					List[str]=3_27_68.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=0.97							,					SCREAMING_SNAKE_CASE__							:					int=1.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=False							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(feature_size=SCREAMING_SNAKE_CASE__							,					sampling_rate=SCREAMING_SNAKE_CASE__							,					padding_value=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		feature_size
          lowerCamelCase__           =		sampling_rate
          lowerCamelCase__           =		padding_value
          lowerCamelCase__           =		hop_length
          lowerCamelCase__           =		win_length
          lowerCamelCase__           =		frame_signal_scale
          lowerCamelCase__           =		preemphasis_coeff
          lowerCamelCase__           =		mel_floor
          lowerCamelCase__           =		normalize_means
          lowerCamelCase__           =		normalize_vars
          lowerCamelCase__           =		win_function
          lowerCamelCase__           =		return_attention_mask

          lowerCamelCase__           =		win_length * sampling_rate // 10_00
          lowerCamelCase__           =		hop_length * sampling_rate // 10_00

          lowerCamelCase__           =		optimal_fft_length(self.sample_size				)
          lowerCamelCase__           =		(self.n_fft // 2) + 1
    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					np.array				):
          if self.win_function == "hamming_window":
                lowerCamelCase__           =		window_function(window_length=self.sample_size							,					name=self.win_function							,					periodic=SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		window_function(window_length=self.sample_size							,					name=self.win_function				)

          lowerCamelCase__           =		mel_filter_bank(
              num_frequency_bins=self.n_freqs							,					num_mel_filters=self.feature_size							,					min_frequency=0.0							,					max_frequency=self.sampling_rate / 2.0							,					sampling_rate=self.sampling_rate							,					)

          lowerCamelCase__           =		spectrogram(
              one_waveform * self.frame_signal_scale							,					window=SCREAMING_SNAKE_CASE__							,					frame_length=self.sample_size							,					hop_length=self.sample_stride							,					fft_length=self.n_fft							,					center=SCREAMING_SNAKE_CASE__							,					preemphasis=self.preemphasis_coeff							,					mel_filters=SCREAMING_SNAKE_CASE__							,					mel_floor=self.mel_floor							,					log_mel='log'							,					)
          return msfc_features.T
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Any				):
          # make sure we normalize float32 arrays
          if self.normalize_means:
                lowerCamelCase__           =		x[:input_length].mean(axis=0				)
                lowerCamelCase__           =		np.subtract(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          if self.normalize_vars:
                lowerCamelCase__           =		x[:input_length].std(axis=0				)
                lowerCamelCase__           =		np.divide(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          if input_length < x.shape[0]:
                lowerCamelCase__           =		padding_value

          # make sure array is in float32
          lowerCamelCase__           =		x.astype(np.floataa				)

          return x
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					List[np.ndarray]							,					SCREAMING_SNAKE_CASE__							:					Optional[np.ndarray] = None				):
          lowerCamelCase__           =		attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					self.padding_value				) for x, n in zip(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)]


    def __call__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,					SCREAMING_SNAKE_CASE__							:					Union[bool, str, PaddingStrategy] = False							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					bool = False							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[bool] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[str, TensorType]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					**SCREAMING_SNAKE_CASE__							:					Any							,					):

          if sampling_rate is not None:
                if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                          F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                          F' {self.sampling_rate} and not {sampling_rate}.'				)
          else:
                logger.warning(
                    'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
                    'Failing to do so can result in silent errors that might be hard to debug.'				)

          lowerCamelCase__           =		isinstance(SCREAMING_SNAKE_CASE__							,					np.ndarray				) and len(raw_speech.shape				) > 1
          if is_batched_numpy and len(raw_speech.shape				) > 2:
                raise ValueError(F'Only mono-channel audio is supported for input to {self}'				)
          lowerCamelCase__           =		is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE__							,					(list, tuple)				) and (isinstance(raw_speech[0]							,					(np.ndarray, tuple, list)				))
          )

          if is_batched:
                lowerCamelCase__           =		[np.asarray(SCREAMING_SNAKE_CASE__							,					dtype=np.floataa				) for speech in raw_speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__							,					np.ndarray				):
                lowerCamelCase__           =		np.asarray(SCREAMING_SNAKE_CASE__							,					dtype=np.floataa				)
          elif isinstance(SCREAMING_SNAKE_CASE__							,					np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
                lowerCamelCase__           =		raw_speech.astype(np.floataa				)

          # always return batch
          if not is_batched:
                lowerCamelCase__           =		[raw_speech]

          # extract fbank features
          lowerCamelCase__           =		[self._extract_mfsc_features(SCREAMING_SNAKE_CASE__				) for one_waveform in raw_speech]

          # convert into correct format for padding
          lowerCamelCase__           =		BatchFeature({'input_features': features}				)

          lowerCamelCase__           =		self.pad(
              SCREAMING_SNAKE_CASE__							,					padding=SCREAMING_SNAKE_CASE__							,					max_length=SCREAMING_SNAKE_CASE__							,					truncation=SCREAMING_SNAKE_CASE__							,					pad_to_multiple_of=SCREAMING_SNAKE_CASE__							,					return_attention_mask=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)
          # make sure list is in array format
          lowerCamelCase__           =		padded_inputs.get('input_features'				)
          if isinstance(input_features[0]							,					SCREAMING_SNAKE_CASE__				):
                lowerCamelCase__           =		[np.asarray(SCREAMING_SNAKE_CASE__							,					dtype=np.floataa				) for feature in input_features]

          lowerCamelCase__           =		padded_inputs.get('attention_mask'				)
          if attention_mask is not None:
                lowerCamelCase__           =		[np.asarray(SCREAMING_SNAKE_CASE__							,					dtype=np.intaa				) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
                lowerCamelCase__           =		(
                    np.array(SCREAMING_SNAKE_CASE__							,					dtype=np.intaa				)
                    if self._get_padding_strategies(SCREAMING_SNAKE_CASE__							,					max_length=SCREAMING_SNAKE_CASE__				) is not PaddingStrategy.DO_NOT_PAD
                    and padding
                    else None
                )
                lowerCamelCase__           =		self.normalize(
                    padded_inputs['input_features']							,					attention_mask=SCREAMING_SNAKE_CASE__				)

          if return_tensors is not None:
                lowerCamelCase__           =		padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__				)

          return padded_inputs







"""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


_snake_case          =							re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_snake_case          =							10
_snake_case          =							256



def   snake_case						(				_a:     List[str]							)->		Optional[MinHash]:
      '''simple docstring'''


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



def   snake_case						(				_a:     str							)->		Set[str]:
      '''simple docstring'''


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



class   _a      :
    def __init__( self							:					List[Any]							,					*,
        SCREAMING_SNAKE_CASE__							:					float = 0.85							,					):
          lowerCamelCase__           =		duplication_jaccard_threshold
          lowerCamelCase__           =		NUM_PERM
          lowerCamelCase__           =		MinHashLSH(threshold=self._duplication_jaccard_threshold							,					num_perm=self._num_perm				)

          lowerCamelCase__           =		defaultdict(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					MinHash				):
          lowerCamelCase__           =		self._index.query(SCREAMING_SNAKE_CASE__				)
          if code_key in self._index.keys:
                print(F'Duplicate key {code_key}'				)
                return

          self._index.insert(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          if len(SCREAMING_SNAKE_CASE__				) > 0:
                for base_duplicate in close_duplicates:
                      if base_duplicate in self._duplicate_clusters:
                            self._duplicate_clusters[base_duplicate].add(SCREAMING_SNAKE_CASE__				)
                            break
                else:
                      self._duplicate_clusters[close_duplicates[0]].add(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[]
          for base, duplicates in self._duplicate_clusters.items():
                lowerCamelCase__           =		[base] + list(SCREAMING_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(SCREAMING_SNAKE_CASE__				)
          return duplicate_clusters


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.get_duplicate_clusters()
          with open(SCREAMING_SNAKE_CASE__							,					'w'				) as f:
                json.dump(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)





def   snake_case						(				_a:     Union[str, Any]							)->		Optional[int]:
      '''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   snake_case						(				_a:     Type[Dataset]							)->		Tuple:
      '''simple docstring'''


      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash		,   ThreadedIterator(_a		,   max_queue_size=10000							)		,   chunksize=100		,   ):
                  if data is not None:
                        yield data



def   snake_case						(				_a:     Type[Dataset]		,   _a:     float							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		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   snake_case						(				_a:     str		,   _a:     str							)->		float:
      '''simple docstring'''


      lowerCamelCase__           =		get_tokens(_a							)
      lowerCamelCase__           =		get_tokens(_a							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_snake_case          =							None



def   snake_case						(				_a:     Dict		,   _a:     Union[str, Any]							)->		List[str]:
      '''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(_a		,   _a							) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  lowerCamelCase__           =		1
                  extremes.append(_a							)
      return extremes



def   snake_case						(				_a:     Any		,   _a:     Tuple		,   _a:     Dict							)->		Union[str, Any]:
      '''simple docstring'''


      global _shared_dataset
      lowerCamelCase__           =		dataset
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		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   snake_case						(				_a:     Type[Dataset]		,   _a:     float = 0.85							)->		Tuple[Type[Dataset], List[List[Dict]]]:
      '''simple docstring'''


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



