


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


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


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							['input_features', 'attention_mask']



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							List[str]=8_0     ,					_lowerCAmelCase							:							Optional[Any]=1_6_0_0_0     ,					_lowerCAmelCase							:							Dict=0.0     ,					_lowerCAmelCase							:							Optional[int]=1_0     ,					_lowerCAmelCase							:							Union[str, Any]=2_5     ,					_lowerCAmelCase							:							Tuple="hamming_window"     ,					_lowerCAmelCase							:							Optional[Any]=32_768.0     ,					_lowerCAmelCase							:							List[Any]=0.97     ,					_lowerCAmelCase							:							Any=1.0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Optional[Any]=True     ,					_lowerCAmelCase							:							int=False     ,					**_lowerCAmelCase							:							Any     ,					) ->			Any:




     """simple docstring"""
     super().__init__(feature_size=_lowerCAmelCase     ,					sampling_rate=_lowerCAmelCase     ,					padding_value=_lowerCAmelCase     ,					**_lowerCAmelCase	)

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

     snake_case_            =       win_length * sampling_rate // 1_0_0_0
     snake_case_            =       hop_length * sampling_rate // 1_0_0_0

     snake_case_            =       optimal_fft_length(self.sample_size	)
     snake_case_            =       (self.n_fft // 2) + 1



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							np.array	) ->			np.ndarray:




     """simple docstring"""
     if self.win_function == "hamming_window":
        snake_case_            =       window_function(window_length=self.sample_size     ,					name=self.win_function     ,					periodic=_lowerCAmelCase	)
     else:
        snake_case_            =       window_function(window_length=self.sample_size     ,					name=self.win_function	)

     snake_case_            =       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     ,					)

     snake_case_            =       spectrogram(
         one_waveform * self.frame_signal_scale     ,					window=_lowerCAmelCase     ,					frame_length=self.sample_size     ,					hop_length=self.sample_stride     ,					fft_length=self.n_fft     ,					center=_lowerCAmelCase     ,					preemphasis=self.preemphasis_coeff     ,					mel_filters=_lowerCAmelCase     ,					mel_floor=self.mel_floor     ,					log_mel="log"     ,					)
     return msfc_features.T



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							int	) ->			Any:




     """simple docstring"""
     # make sure we normalize float32 arrays
     if self.normalize_means:
        snake_case_            =       x[:input_length].mean(axis=0	)
        snake_case_            =       np.subtract(_lowerCAmelCase     ,					_lowerCAmelCase	)
     if self.normalize_vars:
        snake_case_            =       x[:input_length].std(axis=0	)
        snake_case_            =       np.divide(_lowerCAmelCase     ,					_lowerCAmelCase	)

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

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

     return x



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[np.ndarray]     ,					_lowerCAmelCase							:							Optional[np.ndarray] = None	) ->			List[np.ndarray]:




     """simple docstring"""
     snake_case_            =       attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(_lowerCAmelCase     ,					_lowerCAmelCase     ,					self.padding_value	) for x, n in zip(_lowerCAmelCase     ,					_lowerCAmelCase	)]



  def __call__(  self							:							str     ,					_lowerCAmelCase							:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,					_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							:							List[str]     ,					) ->			BatchFeature:




     """simple docstring"""

     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."	)

     snake_case_            =       isinstance(_lowerCAmelCase     ,					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}'''	)
     snake_case_            =       is_batched_numpy or (
         isinstance(_lowerCAmelCase     ,					(list, tuple)	) and (isinstance(raw_speech[0]     ,					(np.ndarray, tuple, list)	))
     )

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

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

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

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

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

     snake_case_            =       padded_inputs.get("attention_mask"	)
     if attention_mask is not None:
        snake_case_            =       [np.asarray(_lowerCAmelCase     ,					dtype=np.intaa	) for array in attention_mask]

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

     if return_tensors is not None:
        snake_case_            =       padded_inputs.convert_to_tensors(_lowerCAmelCase	)

     return padded_inputs








def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->str:
   '''simple docstring'''






   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      raise ValueError("iterations must be defined as integers"				)
   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				) or not number >= 1:
      raise ValueError(
          "starting number must be\n                         and integer and be more than 0"				)
   if not iterations >= 1:
      raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

   snake_case_            =       ""
   while number <= iterations:
      if number % 3 == 0:
         out += "Fizz"
      if number % 5 == 0:
         out += "Buzz"
      if 0 not in (number % 3, number % 5):
         out += str(lowerCAmelCase_				)

      # print(out)
      number += 1
      out += " "
   return out


if __name__ == "__main__":
   import doctest

   doctest.testmod()




