code: 
  
  
  '''simple docstring'''
  
  
  
  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 TensorType, logging
  
  
  A__	:      Optional[int]				=						logging.get_logger(__name__)
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      List[str]							  =    ['input_values', 'attention_mask']
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_=1    ,  SCREAMING_SNAKE_CASE_=1_60_00    ,  SCREAMING_SNAKE_CASE_=1_28    ,  SCREAMING_SNAKE_CASE_=10_24    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=-4.2_6_7_7_3_9_3    ,  SCREAMING_SNAKE_CASE_=4.5_6_8_9_9_7_4    ,  SCREAMING_SNAKE_CASE_=False    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  Optional[Any]:
           super().__init__(feature_size=SCREAMING_SNAKE_CASE_    ,  sampling_rate=SCREAMING_SNAKE_CASE_    ,  padding_value=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Any              =	num_mel_bins
           __lowerCamelCase     :			Dict              =	max_length
           __lowerCamelCase     :			Union[str, Any]              =	do_normalize
           __lowerCamelCase     :			Any              =	mean
           __lowerCamelCase     :			int              =	std
           __lowerCamelCase     :			Dict              =	return_attention_mask
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  )       ->  np.ndarray:
           __lowerCamelCase     :			Any              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
           __lowerCamelCase     :			Any              =	ta_kaldi.fbank(
               SCREAMING_SNAKE_CASE_    ,  htk_compat=SCREAMING_SNAKE_CASE_    ,  sample_frequency=self.sampling_rate    ,  use_energy=SCREAMING_SNAKE_CASE_    ,  window_type='hanning'    ,  num_mel_bins=self.num_mel_bins    ,  dither=0.0    ,  frame_shift=10    ,  )
  
           __lowerCamelCase     :			Union[str, Any]              =	fbank.shape[0]
           __lowerCamelCase     :			Tuple              =	max_length - n_frames
  
           # pad or truncate, depending on difference
           if difference > 0:
                __lowerCamelCase     :			int              =	torch.nn.ZeroPadad((0, 0, 0, difference)			)
                __lowerCamelCase     :			Any              =	pad_module(SCREAMING_SNAKE_CASE_			)
           elif difference < 0:
                __lowerCamelCase     :			List[str]              =	fbank[0:max_length, :]
  
           __lowerCamelCase     :			List[str]              =	fbank.numpy()
  
           return fbank
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)
  
  
  
  
  
  
  
      def __call__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  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     :			List[str]              =	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     :			List[str]              =	is_batched_numpy or (
               isinstance(SCREAMING_SNAKE_CASE_    ,  (list, tuple)			) and (isinstance(raw_speech[0]    ,  (np.ndarray, tuple, list)			))
           )
  
           if is_batched:
                __lowerCamelCase     :			Optional[Any]              =	[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     :			List[str]              =	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     :			Any              =	raw_speech.astype(np.floataa			)
  
           # always return batch
           if not is_batched:
                __lowerCamelCase     :			int              =	[raw_speech]
  
           # extract fbank features and pad/truncate to max_length
           __lowerCamelCase     :			Dict              =	[self._extract_fbank_features(SCREAMING_SNAKE_CASE_    ,  max_length=self.max_length			) for waveform in raw_speech]
  
           # convert into BatchFeature
           __lowerCamelCase     :			Any              =	BatchFeature({'input_values': features}			)
  
           # make sure list is in array format
           __lowerCamelCase     :			Optional[int]              =	padded_inputs.get('input_values'			)
           if isinstance(input_values[0]    ,  SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase     :			List[Any]              =	[np.asarray(SCREAMING_SNAKE_CASE_    ,  dtype=np.floataa			) for feature in input_values]
  
           # normalization
           if self.do_normalize:
                __lowerCamelCase     :			Any              =	[self.normalize(SCREAMING_SNAKE_CASE_			) for feature in input_values]
  
           if return_tensors is not None:
                __lowerCamelCase     :			int              =	padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_			)
  
           return padded_inputs
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  