'''simple docstring'''

import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase_ : Dict						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['input_values', 'attention_mask']


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	int = 1   ,		__lowerCAmelCase				:	int = 1_6000   ,		__lowerCAmelCase				:	float = 0.0   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	int = 80   ,		__lowerCAmelCase				:	int = 16   ,		__lowerCAmelCase				:	int = 64   ,		__lowerCAmelCase				:	str = "hann_window"   ,		__lowerCAmelCase				:	float = 1.0   ,		__lowerCAmelCase				:	float = 80   ,		__lowerCAmelCase				:	float = 7600   ,		__lowerCAmelCase				:	float = 1e-10   ,		__lowerCAmelCase				:	int = 2   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	List[Any]   ,		)      ->					Any:
         super().__init__(feature_size=__lowerCAmelCase   ,		sampling_rate=__lowerCAmelCase   ,		padding_value=__lowerCAmelCase   ,		**__lowerCAmelCase)
         lowercase_     =      do_normalize
         lowercase_     =      return_attention_mask

         lowercase_     =      num_mel_bins
         lowercase_     =      hop_length
         lowercase_     =      win_length
         lowercase_     =      win_function
         lowercase_     =      frame_signal_scale
         lowercase_     =      fmin
         lowercase_     =      fmax
         lowercase_     =      mel_floor
         lowercase_     =      reduction_factor

         lowercase_     =      win_length * sampling_rate // 1000
         lowercase_     =      hop_length * sampling_rate // 1000
         lowercase_     =      optimal_fft_length(self.sample_size)
         lowercase_     =      (self.n_fft // 2) + 1

         lowercase_     =      window_function(window_length=self.sample_size   ,		name=self.win_function   ,		periodic=__lowerCAmelCase)

         lowercase_     =      mel_filter_bank(
             num_frequency_bins=self.n_freqs   ,		num_mel_filters=self.num_mel_bins   ,		min_frequency=self.fmin   ,		max_frequency=self.fmax   ,		sampling_rate=self.sampling_rate   ,		norm="slaney"   ,		mel_scale="slaney"   ,		)

         if frame_signal_scale != 1.0:
               warnings.warn(
                   "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"   ,		__lowerCAmelCase   ,		)
         if reduction_factor != 2.0:
               warnings.warn(
                   "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"   ,		__lowerCAmelCase   ,		)


   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def __UpperCAmelCase       (   __lowerCAmelCase				:	List[np.ndarray]   ,		__lowerCAmelCase				:	List[np.ndarray]   ,		__lowerCAmelCase				:	float = 0.0)      ->					List[np.ndarray]:
         if attention_mask is not None:
               lowercase_     =      np.array(__lowerCAmelCase   ,		np.intaa)
               lowercase_     =      []

               for vector, length in zip(__lowerCAmelCase   ,		attention_mask.sum(-1)):
                     lowercase_     =      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                     if length < normed_slice.shape[0]:
                           lowercase_     =      padding_value

                     normed_input_values.append(__lowerCAmelCase)
         else:
               lowercase_     =      [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

         return normed_input_values


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	np.ndarray   ,		)      ->					np.ndarray:
         lowercase_     =      spectrogram(
             __lowerCAmelCase   ,		window=self.window   ,		frame_length=self.sample_size   ,		hop_length=self.sample_stride   ,		fft_length=self.n_fft   ,		mel_filters=self.mel_filters   ,		mel_floor=self.mel_floor   ,		log_mel="log10"   ,		)
         return log_mel_spec.T


   def __call__(   self				:	int   ,		__lowerCAmelCase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None   ,		__lowerCAmelCase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None   ,		__lowerCAmelCase				:	Union[bool, str, PaddingStrategy] = False   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[str, TensorType]] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					BatchFeature:
         if audio is None and audio_target is None:
               raise ValueError("You must provide either `audio` or `audio_target` values.")

         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 audio 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.")

         if audio is not None:
               lowercase_     =      self._process_audio(
                   __lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)
         else:
               lowercase_     =      None

         if audio_target is not None:
               lowercase_     =      self._process_audio(
                   __lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

               if inputs is None:
                     return inputs_target
               else:
                     lowercase_     =      inputs_target["input_values"]
                     lowercase_     =      inputs_target.get("attention_mask")
                     if decoder_attention_mask is not None:
                           lowercase_     =      decoder_attention_mask

         return inputs


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Union[bool, str, PaddingStrategy] = False   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[str, TensorType]] = None   ,		**__lowerCAmelCase				:	str   ,		)      ->					BatchFeature:
         lowercase_     =      isinstance(__lowerCAmelCase   ,		np.ndarray) and len(speech.shape) > 1
         if is_batched_numpy and len(speech.shape) > 2:
               raise ValueError(F'Only mono-channel audio is supported for input to {self}')
         lowercase_     =      is_batched_numpy or (
             isinstance(__lowerCAmelCase   ,		(list, tuple)) and (isinstance(speech[0]   ,		(np.ndarray, tuple, list)))
         )

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

         # always return batch
         if not is_batched:
               lowercase_     =      [speech]

         # needed to make pad() work on spectrogram inputs
         lowercase_     =      self.feature_size

         # convert into correct format for padding
         if is_target:
               lowercase_     =      [self._extract_mel_features(__lowerCAmelCase) for waveform in speech]
               lowercase_     =      BatchFeature({"input_values": features})
               lowercase_     =      self.num_mel_bins
         else:
               lowercase_     =      BatchFeature({"input_values": speech})

         lowercase_     =      self.pad(
             __lowerCAmelCase   ,		padding=__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		truncation=__lowerCAmelCase   ,		pad_to_multiple_of=__lowerCAmelCase   ,		return_attention_mask=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      feature_size_hack

         # convert input values to correct format
         lowercase_     =      padded_inputs["input_values"]
         if not isinstance(input_values[0]   ,		np.ndarray):
               lowercase_     =      [np.asarray(__lowerCAmelCase   ,		dtype=np.floataa) for array in input_values]
         elif (
             not isinstance(__lowerCAmelCase   ,		np.ndarray)
             and isinstance(input_values[0]   ,		np.ndarray)
             and input_values[0].dtype is np.dtype(np.floataa)
         ):
               lowercase_     =      [array.astype(np.floataa) for array in input_values]
         elif isinstance(__lowerCAmelCase   ,		np.ndarray) and input_values.dtype is np.dtype(np.floataa):
               lowercase_     =      input_values.astype(np.floataa)

         # convert attention_mask to correct format
         lowercase_     =      padded_inputs.get("attention_mask")
         if attention_mask is not None:
               lowercase_     =      [np.asarray(__lowerCAmelCase   ,		dtype=np.intaa) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if not is_target and self.do_normalize:
               lowercase_     =      (
                   attention_mask
                   if self._get_padding_strategies(__lowerCAmelCase   ,		max_length=__lowerCAmelCase) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               lowercase_     =      self.zero_mean_unit_var_norm(
                   padded_inputs["input_values"]   ,		attention_mask=__lowerCAmelCase   ,		padding_value=self.padding_value)

         if return_tensors is not None:
               lowercase_     =      padded_inputs.convert_to_tensors(__lowerCAmelCase)

         return padded_inputs


   def __UpperCAmelCase       (   self				:	str)      ->					Dict[str, Any]:
         lowercase_     =      super().to_dict()

         # Don't serialize these as they are derived from the other properties.
         lowercase_     =      ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
         for name in names:
               if name in output:
                     del output[name]

         return output







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						int				)	->							list[int]:
      '''simple docstring'''


      lowercase_     =      [True] * limit
      lowercase_     =      False
      lowercase_     =      False
      lowercase_     =      True

      for i in range(3 ,		int(limit**0.5 + 1				) ,		2				):
            lowercase_     =      i * 2
            while index < limit:
                  lowercase_     =      False
                  lowercase_     =      index + i

      lowercase_     =      [2]

      for i in range(3 ,		__lowerCamelCase ,		2				):
            if is_prime[i]:
                  primes.append(__lowerCamelCase				)

      return primes


def __a (	__lowerCamelCase			:						int = 1_000_000				)	->							int:
      '''simple docstring'''


      lowercase_     =      prime_sieve(__lowerCamelCase				)
      lowercase_     =      0
      lowercase_     =      0

      for i in range(len(__lowerCamelCase				)				):
            for j in range(i + length ,		len(__lowerCamelCase				)				):
                  lowercase_     =      sum(primes[i:j]				)
                  if sol >= ceiling:
                        break

                  if sol in primes:
                        lowercase_     =      j - i
                        lowercase_     =      sol

      return largest


if __name__ == "__main__":
       print(F'''{solution() = }''')






