





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


lowerCAmelCase_						   =    logging.get_logger(__name__)



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Any      					=["""input_features""", """is_longer"""]







  def __init__(      self      :					Tuple      ,				UpperCamelCase      :					Tuple=64      ,				UpperCamelCase      :					int=4_80_00      ,				UpperCamelCase      :					List[Any]=4_80      ,				UpperCamelCase      :					List[Any]=10      ,				UpperCamelCase      :					Any=10_24      ,				UpperCamelCase      :					Optional[Any]=0.0      ,				UpperCamelCase      :					Optional[int]=False      ,				UpperCamelCase      :					float = 0      ,				UpperCamelCase      :					float = 1_40_00      ,				UpperCamelCase      :					int = None      ,				UpperCamelCase      :					str = "fusion"      ,				UpperCamelCase      :					str = "repeatpad"      ,				**UpperCamelCase      :					str      ,				):


    '''simple docstring'''





    super().__init__(
        feature_size=UpperCamelCase      ,				sampling_rate=UpperCamelCase      ,				padding_value=UpperCamelCase      ,				return_attention_mask=UpperCamelCase      ,				**UpperCamelCase      ,				)
    _snake_case   :				Tuple														=	top_db
    _snake_case   :				Dict														=	truncation
    _snake_case   :				Any														=	padding
    _snake_case   :				Tuple														=	fft_window_size
    _snake_case   :				Optional[Any]														=	(fft_window_size >> 1) + 1
    _snake_case   :				Optional[Any]														=	hop_length
    _snake_case   :				str														=	max_length_s
    _snake_case   :				Union[str, Any]														=	max_length_s * sampling_rate
    _snake_case   :				str														=	sampling_rate
    _snake_case   :				List[str]														=	frequency_min
    _snake_case   :				List[str]														=	frequency_max
    _snake_case   :				List[Any]														=	mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins      ,				num_mel_filters=UpperCamelCase      ,				min_frequency=UpperCamelCase      ,				max_frequency=UpperCamelCase      ,				sampling_rate=UpperCamelCase      ,				norm=UpperCamelCase      ,				mel_scale='htk'      ,				)
    _snake_case   :				Tuple														=	mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins      ,				num_mel_filters=UpperCamelCase      ,				min_frequency=UpperCamelCase      ,				max_frequency=UpperCamelCase      ,				sampling_rate=UpperCamelCase      ,				norm='slaney'      ,				mel_scale='slaney'      ,				)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	copy.deepcopy(self.__dict__							)
    _snake_case   :				Union[str, Any]														=	self.__class__.__name__
    if "mel_filters" in output:
      del output["mel_filters"]
    if "mel_filters_slaney" in output:
      del output["mel_filters_slaney"]
    return output







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					np.array      ,				UpperCamelCase      :					Optional[np.array] = None							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	spectrogram(
        UpperCamelCase      ,				window_function(self.fft_window_size      ,				'hann'							)      ,				frame_length=self.fft_window_size      ,				hop_length=self.hop_length      ,				power=2.0      ,				mel_filters=UpperCamelCase      ,				log_mel='dB'      ,				)
    return log_mel_spectrogram.T







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Dict														=	np.array_split(list(range(0      ,				total_frames - chunk_frames + 1							)							)      ,				3							)
    if len(ranges[1]							) == 0:
      # if the audio is too short, we just use the first chunk
      _snake_case   :				Tuple														=	[0]
    if len(ranges[2]							) == 0:
      # if the audio is too short, we just use the first chunk
      _snake_case   :				List[Any]														=	[0]
    # randomly choose index for each part
    _snake_case   :				Tuple														=	np.random.choice(ranges[0]							)
    _snake_case   :				Tuple														=	np.random.choice(ranges[1]							)
    _snake_case   :				Union[str, Any]														=	np.random.choice(ranges[2]							)

    _snake_case   :				Dict														=	mel[idx_front : idx_front + chunk_frames, :]
    _snake_case   :				Dict														=	mel[idx_middle : idx_middle + chunk_frames, :]
    _snake_case   :				Tuple														=	mel[idx_back : idx_back + chunk_frames, :]

    _snake_case   :				Any														=	torch.tensor(mel[None, None, :]							)
    _snake_case   :				List[str]														=	torch.nn.functional.interpolate(
        UpperCamelCase      ,				size=[chunk_frames, 64]      ,				mode='bilinear'      ,				align_corners=UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	mel_shrink[0][0].numpy()
    _snake_case   :				Any														=	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,				axis=0							)
    return mel_fusion







  def UpperCamelCase_ (      self      :					str      ,				UpperCamelCase      :					np.array      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					str							):


    '''simple docstring'''





    if waveform.shape[0] > max_length:
      if truncation == "rand_trunc":
        _snake_case   :				Optional[Any]														=	True
        # random crop to max_length (for compatibility) -> this should be handled by self.pad
        _snake_case   :				Optional[Any]														=	len(UpperCamelCase							) - max_length
        _snake_case   :				Tuple														=	np.random.randint(0      ,				overflow + 1							)
        _snake_case   :				Any														=	waveform[idx : idx + max_length]
        _snake_case   :				Union[str, Any]														=	self._np_extract_fbank_features(UpperCamelCase      ,				self.mel_filters_slaney							)[None, :]
      elif truncation == "fusion":
        _snake_case   :				int														=	self._np_extract_fbank_features(UpperCamelCase      ,				self.mel_filters							)
        _snake_case   :				Dict														=	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
        _snake_case   :				Optional[int]														=	mel.shape[0]
        if chunk_frames == total_frames:
          # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
          # In this case, we just use the whole audio.
          _snake_case   :				List[str]														=	np.stack([mel, mel, mel, mel]      ,				axis=0							)
          _snake_case   :				int														=	False
        else:
          _snake_case   :				Optional[Any]														=	self._random_mel_fusion(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							)
          _snake_case   :				Dict														=	True
      else:
        raise NotImplementedError(f"""data_truncating {truncation} not implemented"""							)

    else:
      _snake_case   :				int														=	False
      # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
      if waveform.shape[0] < max_length:
        if padding == "repeat":
          _snake_case   :				Optional[Any]														=	int(max_length / len(UpperCamelCase							)							)
          _snake_case   :				Union[str, Any]														=	np.stack(np.tile(UpperCamelCase      ,				n_repeat + 1							)							)[:max_length]
        if padding == "repeatpad":
          _snake_case   :				Optional[int]														=	int(max_length / len(UpperCamelCase							)							)
          _snake_case   :				Union[str, Any]														=	np.stack(np.tile(UpperCamelCase      ,				UpperCamelCase							)							)
        _snake_case   :				List[Any]														=	np.pad(UpperCamelCase      ,				(0, max_length - waveform.shape[0])      ,				mode='constant'      ,				constant_values=0							)

      if truncation == "fusion":
        _snake_case   :				List[Any]														=	self._np_extract_fbank_features(UpperCamelCase      ,				self.mel_filters							)
        _snake_case   :				List[Any]														=	np.stack([input_mel, input_mel, input_mel, input_mel]      ,				axis=0							)
      else:
        _snake_case   :				Optional[int]														=	self._np_extract_fbank_features(UpperCamelCase      ,				self.mel_filters_slaney							)[None, :]

    return input_mel, longer







  def __call__(      self      :					List[str]      ,				UpperCamelCase      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,				UpperCamelCase      :					str = None      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[Union[str, TensorType]] = None      ,				**UpperCamelCase      :					Tuple      ,				):


    '''simple docstring'''





    _snake_case   :				Tuple														=	truncation if truncation is not None else self.truncation
    _snake_case   :				Optional[int]														=	padding if padding else self.padding

    if sampling_rate is not None:
      if sampling_rate != self.sampling_rate:
        raise ValueError(
            f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
            f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
            f""" was sampled with {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   :				int														=	isinstance(UpperCamelCase      ,				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   :				Union[str, Any]														=	is_batched_numpy or (
        isinstance(UpperCamelCase      ,				(list, tuple)							) and (isinstance(raw_speech[0]      ,				(np.ndarray, tuple, list)							))
    )

    if is_batched:
      _snake_case   :				Tuple														=	[np.asarray(UpperCamelCase      ,				dtype=np.floataa							) for speech in raw_speech]
    elif not is_batched and not isinstance(UpperCamelCase      ,				np.ndarray							):
      _snake_case   :				Optional[Any]														=	np.asarray(UpperCamelCase      ,				dtype=np.floataa							)
    elif isinstance(UpperCamelCase      ,				np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
      _snake_case   :				Optional[Any]														=	raw_speech.astype(np.floataa							)

    # always return batch
    if not is_batched:
      _snake_case   :				List[Any]														=	[np.asarray(UpperCamelCase							)]

    # convert to mel spectrogram, truncate and pad if needed.
    _snake_case   :				Any														=	[
        self._get_input_mel(UpperCamelCase      ,				max_length if max_length else self.nb_max_samples      ,				UpperCamelCase      ,				UpperCamelCase							)
        for waveform in raw_speech
    ]

    _snake_case   :				Dict														=	[]
    _snake_case   :				Dict														=	[]
    for mel, longer in padded_inputs:
      input_mel.append(UpperCamelCase							)
      is_longer.append(UpperCamelCase							)

    if truncation == "fusion" and sum(UpperCamelCase							) == 0:
      # if no audio is longer than 10s, then randomly select one audio to be longer
      _snake_case   :				str														=	np.random.randint(0      ,				len(UpperCamelCase							)							)
      _snake_case   :				List[Any]														=	True

    if isinstance(input_mel[0]      ,				UpperCamelCase							):
      _snake_case   :				Union[str, Any]														=	[np.asarray(UpperCamelCase      ,				dtype=np.floataa							) for feature in input_mel]

    # is_longer is a list of bool
    _snake_case   :				int														=	[[longer] for longer in is_longer]

    _snake_case   :				int														=	{'input_features': input_mel, 'is_longer': is_longer}
    _snake_case   :				Optional[Any]														=	BatchFeature(UpperCamelCase							)

    if return_tensors is not None:
      _snake_case   :				Any														=	input_features.convert_to_tensors(UpperCamelCase							)

    return input_features











from random import randint, random


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	int = 5    ,							)->		list:
  _snake_case   :				Dict														=	[[-1] * number_of_cells]  # Create a highway without any car
  _snake_case   :				List[str]														=	0
  _snake_case   :				List[str]														=	max(lowerCAmelCase    ,							0				)
  while i < number_of_cells:
    _snake_case   :				Optional[Any]														=	(
        randint(0    ,							lowerCAmelCase				) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1    ,							max_speed * 2				) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int				)->		int:
  _snake_case   :				Dict														=	0
  _snake_case   :				Optional[Any]														=	highway_now[car_index + 1 :]
  for cell in range(len(lowerCAmelCase				)				):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(lowerCAmelCase    ,							-1				)


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	len(lowerCAmelCase				)
  # Beforce calculations, the highway is empty
  _snake_case   :				List[Any]														=	[-1] * number_of_cells

  for car_index in range(lowerCAmelCase				):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _snake_case   :				int														=	min(highway_now[car_index] + 1    ,							lowerCAmelCase				)
      # Number of empty cell before the next car
      _snake_case   :				Tuple														=	get_distance(lowerCAmelCase    ,							lowerCAmelCase				) - 1
      # We can't have the car causing an accident
      _snake_case   :				Union[str, Any]														=	min(next_highway[car_index]    ,							lowerCAmelCase				)
      if random() < probability:
        # Randomly, a driver will slow down
        _snake_case   :				int														=	max(next_highway[car_index] - 1    ,							0				)
  return next_highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				Dict														=	len(highway[0]				)

  for i in range(lowerCAmelCase				):
    _snake_case   :				Any														=	update(highway[i]    ,							lowerCAmelCase    ,							lowerCAmelCase				)
    _snake_case   :				Tuple														=	[-1] * number_of_cells

    for car_index in range(lowerCAmelCase				):
      _snake_case   :				Union[str, Any]														=	next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _snake_case   :				Union[str, Any]														=	(car_index + speed) % number_of_cells
        # Commit the change of position
        _snake_case   :				Tuple														=	speed
    highway.append(lowerCAmelCase				)

  return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




