





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A :   Optional[int]  			=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):






       """simple docstring"""




       _UpperCAmelCase				:  List[str]			   =   ["input_features", "attention_mask"]







       def __init__(				self					:     Any		, A					:     int=8_0		, A					:     List[Any]=1_6_0_0_0		, A					:     Dict=8_0		, A					:     List[str]=0.0		, A					:     Optional[Any]=True		, A					:     str=True		, A					:     Dict=True		, **A					:     Dict		, )       ->str:
          super().__init__(feature_size=A		, sampling_rate=A		, padding_value=A		, **A      )
          lowerCamelCase__       :						Any        =							num_mel_bins
          lowerCamelCase__       :						int        =							do_ceptral_normalize
          lowerCamelCase__       :						Optional[int]        =							normalize_means
          lowerCamelCase__       :						Union[str, Any]        =							normalize_vars
          lowerCamelCase__       :						Optional[Any]        =							True







       def 						__lowerCamelCase   (				self					:     Any		, A					:     np.ndarray		, )       ->np.ndarray:
          lowerCamelCase__       :						Tuple        =							waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
          lowerCamelCase__       :						str        =							torch.from_numpy(A      ).unsqueeze(0      )
          lowerCamelCase__       :						Any        =							ta_kaldi.fbank(A		, num_mel_bins=self.num_mel_bins		, sample_frequency=self.sampling_rate      )
          return features.numpy()







       @staticmethod
       def 						__lowerCamelCase   (				A					:     np.ndarray		, A					:     int		, A					:     Optional[bool] = True		, A					:     Optional[bool] = True		, A					:     float = 0.0		, )       ->np.ndarray:
          # make sure we normalize float32 arrays
          if normalize_means:
             lowerCamelCase__       :						Optional[Any]        =							x[:input_length].mean(axis=0      )
             lowerCamelCase__       :						Any        =							np.subtract(A		, A      )
          if normalize_vars:
             lowerCamelCase__       :						List[str]        =							x[:input_length].std(axis=0      )
             lowerCamelCase__       :						List[str]        =							np.divide(A		, A      )

          if input_length < x.shape[0]:
             lowerCamelCase__       :						Union[str, Any]        =							padding_value

          # make sure array is in float32
          lowerCamelCase__       :						List[str]        =							x.astype(np.floataa      )

          return x







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     List[np.ndarray]		, A					:     Optional[np.ndarray] = None      )       ->List[np.ndarray]:
          lowerCamelCase__       :						Union[str, Any]        =							attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [
              self.utterance_cmvn(A		, A		, self.normalize_means		, self.normalize_vars		, self.padding_value      )
              for x, n in zip(A		, A      )
          ]







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

          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__       :						Optional[Any]        =							isinstance(A		, 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__       :						str        =							is_batched_numpy or (
              isinstance(A		, (list, tuple)      ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list)      ))
          )

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

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

          # extract fbank features
          lowerCamelCase__       :						List[str]        =							[self._extract_fbank_features(A      ) for waveform in raw_speech]

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

          lowerCamelCase__       :						Union[str, Any]        =							self.pad(
              A		, padding=A		, max_length=A		, truncation=A		, pad_to_multiple_of=A		, return_attention_mask=A		, **A		, )

          # make sure list is in array format
          lowerCamelCase__       :						List[Any]        =							padded_inputs.get('''input_features'''      )
          if isinstance(input_features[0]		, A      ):
             lowerCamelCase__       :						Optional[Any]        =							[np.asarray(A		, dtype=np.floataa      ) for feature in input_features]

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

          # Utterance-level cepstral mean and variance normalization
          if self.do_ceptral_normalize:
             lowerCamelCase__       :						Optional[int]        =							(
                 np.array(A		, dtype=np.intaa      )
                 if self._get_padding_strategies(A		, max_length=A      ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             lowerCamelCase__       :						str        =							self.normalize(
                 padded_inputs['''input_features''']		, attention_mask=A      )

          if return_tensors is not None:
             lowerCamelCase__       :						List[Any]        =							padded_inputs.convert_to_tensors(A      )

          return padded_inputs










from math import ceil, sqrt



def 	_a						(   UpperCAmelCase = 1000000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							0

   for outer_width in range(3      ,  (limit // 4) + 2					):
      if outer_width**2 > limit:
         lowerCamelCase__       :						List[Any]        =							max(ceil(sqrt(outer_width**2 - limit					)					)      ,  1					)
      else:
         lowerCamelCase__       :						Union[str, Any]        =							1
      if (outer_width - hole_width_lower_bound) % 2:
         hole_width_lower_bound += 1

      answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

   return answer


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



