






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def   _A   (				SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	List[str]=0.9_99     ,  SCREAMING_SNAKE_CASE__  :	Tuple="cosine"     ,  ):







    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE__  :	Optional[int]       ):
            return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2       ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE__  :	Optional[int]       ):
            return math.exp(t * -12.0       )

    else:
        raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''       )

    UpperCamelCase       :str								=   []
    for i in range(_UpperCAmelCase       ):
        UpperCamelCase       :int								=   i / num_diffusion_timesteps
        UpperCamelCase       :Union[str, Any]								=   (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase       ) / alpha_bar_fn(_UpperCAmelCase       )     ,  _UpperCAmelCase       )       )
    return torch.tensor(_UpperCAmelCase     ,  dtype=torch.floataa       )







class 			UpperCAmelCase_						(					snake_case__,	snake_case__ ):



      """simple docstring"""
      UpperCamelCase_				:					int           =[e.name for e in KarrasDiffusionSchedulers]
      UpperCamelCase_				:					List[Any]           =2
      @register_to_config
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_ = 1000    ,				SCREAMING_SNAKE_CASE_ = 0.0_0085    ,				SCREAMING_SNAKE_CASE_ = 0.012    ,				SCREAMING_SNAKE_CASE_ = "linear"    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = "epsilon"    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = 1.0    ,				SCREAMING_SNAKE_CASE_ = "linspace"    ,				SCREAMING_SNAKE_CASE_ = 0    ,				)				->      List[Any]:
          if trained_betas is not None:
              UpperCamelCase       :Optional[Any]								=   torch.tensor(SCREAMING_SNAKE_CASE_    ,				dtype=torch.floataa )
          elif beta_schedule == "linear":
              UpperCamelCase       :Dict								=   torch.linspace(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCamelCase       :int								=   (
                  torch.linspace(beta_start**0.5    ,				beta_end**0.5    ,				SCREAMING_SNAKE_CASE_    ,				dtype=torch.floataa ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              UpperCamelCase       :Optional[Any]								=   betas_for_alpha_bar(SCREAMING_SNAKE_CASE_    ,				alpha_transform_type='''cosine''' )
          elif beta_schedule == "exp":
              UpperCamelCase       :List[str]								=   betas_for_alpha_bar(SCREAMING_SNAKE_CASE_    ,				alpha_transform_type='''exp''' )
          else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

          UpperCamelCase       :Optional[int]								=   1.0 - self.betas
          UpperCamelCase       :Tuple								=   torch.cumprod(self.alphas    ,				dim=0 )

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Union[str, Any]								=   use_karras_sigmas
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=None )				->      str:
          if schedule_timesteps is None:
              UpperCamelCase       :Dict								=   self.timesteps

          UpperCamelCase       :Union[str, Any]								=   (schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter ) == 0:
              UpperCamelCase       :Optional[int]								=   1 if len(SCREAMING_SNAKE_CASE_ ) > 1 else 0
          else:
              UpperCamelCase       :List[str]								=   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
              UpperCamelCase       :Any								=   self._index_counter[timestep_int]

          return indices[pos].item()
      @property
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      int:
          UpperCamelCase       :List[str]								=   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :int								=   self.sigmas[step_index]
          UpperCamelCase       :Union[str, Any]								=   sample / ((sigma**2 + 1) ** 0.5)
          return sample
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				)				->      Union[str, Any]:
          UpperCamelCase       :str								=   num_inference_steps

          UpperCamelCase       :Union[str, Any]								=   num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              UpperCamelCase       :List[str]								=   np.linspace(0    ,				num_train_timesteps - 1    ,				SCREAMING_SNAKE_CASE_    ,				dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
              UpperCamelCase       :Union[str, Any]								=   num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              UpperCamelCase       :Tuple								=   (np.arange(0    ,				SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              UpperCamelCase       :Tuple								=   num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              UpperCamelCase       :Optional[Any]								=   (np.arange(SCREAMING_SNAKE_CASE_    ,				0    ,				-step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
              timesteps -= 1
          else:
              raise ValueError(
                  F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

          UpperCamelCase       :Dict								=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          UpperCamelCase       :List[Any]								=   np.log(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :int								=   np.interp(SCREAMING_SNAKE_CASE_    ,				np.arange(0    ,				len(SCREAMING_SNAKE_CASE_ ) )    ,				SCREAMING_SNAKE_CASE_ )

          if self.config.use_karras_sigmas:
              UpperCamelCase       :List[str]								=   self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_    ,				num_inference_steps=self.num_inference_steps )
              UpperCamelCase       :str								=   np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) for sigma in sigmas] )

          UpperCamelCase       :Optional[Any]								=   np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
          UpperCamelCase       :List[str]								=   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Tuple								=   torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

          UpperCamelCase       :Optional[Any]								=   torch.from_numpy(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Optional[Any]								=   torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

          if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
              # mps does not support float64
              UpperCamelCase       :Union[str, Any]								=   timesteps.to(SCREAMING_SNAKE_CASE_    ,				dtype=torch.floataa )
          else:
              UpperCamelCase       :List[str]								=   timesteps.to(device=SCREAMING_SNAKE_CASE_ )

          # empty dt and derivative
          UpperCamelCase       :Optional[int]								=   None
          UpperCamelCase       :Optional[Any]								=   None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase       :int								=   defaultdict(SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:
          # get log sigma
          UpperCamelCase       :Dict								=   np.log(SCREAMING_SNAKE_CASE_ )

          # get distribution
          UpperCamelCase       :Union[str, Any]								=   log_sigma - log_sigmas[:, np.newaxis]

          # get sigmas range
          UpperCamelCase       :Union[str, Any]								=   np.cumsum((dists >= 0)    ,				axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
          UpperCamelCase       :List[Any]								=   low_idx + 1

          UpperCamelCase       :List[Any]								=   log_sigmas[low_idx]
          UpperCamelCase       :Optional[Any]								=   log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase       :Optional[Any]								=   (low - log_sigma) / (low - high)
          UpperCamelCase       :List[str]								=   np.clip(SCREAMING_SNAKE_CASE_    ,				0    ,				1 )

          # transform interpolation to time range
          UpperCamelCase       :Tuple								=   (1 - w) * low_idx + w * high_idx
          UpperCamelCase       :Optional[Any]								=   t.reshape(sigma.shape )
          return t
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )				->      List[str]:
          UpperCamelCase       :float								=   in_sigmas[-1].item()
          UpperCamelCase       :float								=   in_sigmas[0].item()

          UpperCamelCase       :Union[str, Any]								=   7.0  # 7.0 is the value used in the paper
          UpperCamelCase       :List[Any]								=   np.linspace(0    ,				1    ,				SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   sigma_min ** (1 / rho)
          UpperCamelCase       :Tuple								=   sigma_max ** (1 / rho)
          UpperCamelCase       :List[Any]								=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas
      @property
      def     UpperCAmelCase						(				self )				->      List[Any]:
          return self.dt is None
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = True    ,				)				->      Tuple:
          UpperCamelCase       :Dict								=   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

          # advance index counter by 1
          UpperCamelCase       :int								=   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
              UpperCamelCase       :Any								=   self.sigmas[step_index]
              UpperCamelCase       :Any								=   self.sigmas[step_index + 1]
          else:
              # 2nd order / Heun's method
              UpperCamelCase       :Optional[int]								=   self.sigmas[step_index - 1]
              UpperCamelCase       :List[str]								=   self.sigmas[step_index]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          UpperCamelCase       :Union[str, Any]								=   0
          UpperCamelCase       :str								=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              UpperCamelCase       :str								=   sigma_hat if self.state_in_first_order else sigma_next
              UpperCamelCase       :Tuple								=   sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
              UpperCamelCase       :int								=   sigma_hat if self.state_in_first_order else sigma_next
              UpperCamelCase       :str								=   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
          elif self.config.prediction_type == "sample":
              UpperCamelCase       :Tuple								=   model_output
          else:
              raise ValueError(
                  F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

          if self.config.clip_sample:
              UpperCamelCase       :Optional[int]								=   pred_original_sample.clamp(
                  -self.config.clip_sample_range    ,				self.config.clip_sample_range )

          if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              UpperCamelCase       :Optional[Any]								=   (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              UpperCamelCase       :List[str]								=   sigma_next - sigma_hat

              # store for 2nd order step
              UpperCamelCase       :Optional[int]								=   derivative
              UpperCamelCase       :List[str]								=   dt
              UpperCamelCase       :int								=   sample
          else:
              # 2. 2nd order / Heun's method
              UpperCamelCase       :str								=   (sample - pred_original_sample) / sigma_next
              UpperCamelCase       :Optional[int]								=   (self.prev_derivative + derivative) / 2

              # 3. take prev timestep & sample
              UpperCamelCase       :Union[str, Any]								=   self.dt
              UpperCamelCase       :Optional[Any]								=   self.sample

              # free dt and derivative
              # Note, this puts the scheduler in "first order mode"
              UpperCamelCase       :List[str]								=   None
              UpperCamelCase       :str								=   None
              UpperCamelCase       :List[str]								=   None

          UpperCamelCase       :Any								=   sample + derivative * dt

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      Dict:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase       :int								=   self.sigmas.to(device=original_samples.device    ,				dtype=original_samples.dtype )
          if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_ ):
              # mps does not support float64
              UpperCamelCase       :Tuple								=   self.timesteps.to(original_samples.device    ,				dtype=torch.floataa )
              UpperCamelCase       :int								=   timesteps.to(original_samples.device    ,				dtype=torch.floataa )
          else:
              UpperCamelCase       :Tuple								=   self.timesteps.to(original_samples.device )
              UpperCamelCase       :int								=   timesteps.to(original_samples.device )

          UpperCamelCase       :Optional[Any]								=   [self.index_for_timestep(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) for t in timesteps]

          UpperCamelCase       :Any								=   sigmas[step_indices].flatten()
          while len(sigma.shape ) < len(original_samples.shape ):
              UpperCamelCase       :Dict								=   sigma.unsqueeze(-1 )

          UpperCamelCase       :List[Any]								=   original_samples + noise * sigma
          return noisy_samples




      def __len__(				self )				->      List[str]:
          return self.config.num_train_timesteps






'''simple docstring'''




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


__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						["input_features", "is_longer"]




      def __init__(       self						:				str						,					A						:				int=64						,					A						:				Dict=48000						,					A						:				str=480						,					A						:				List[Any]=10						,					A						:				Optional[Any]=1024						,					A						:				Tuple=0.0						,					A						:				List[Any]=False						,					A						:				float = 0						,					A						:				float = 14000						,					A						:				int = None						,					A						:				str = "fusion"						,					A						:				str = "repeatpad"						,					**A						:				Dict						,					):
             super().__init__(
                 feature_size=A						,					sampling_rate=A						,					padding_value=A						,					return_attention_mask=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    top_db
             _UpperCAmelCase     :  Dict							      =    truncation
             _UpperCAmelCase     :  List[Any]							      =    padding
             _UpperCAmelCase     :  Optional[Any]							      =    fft_window_size
             _UpperCAmelCase     :  Dict							      =    (fft_window_size >> 1) + 1
             _UpperCAmelCase     :  Any							      =    hop_length
             _UpperCAmelCase     :  Tuple							      =    max_length_s
             _UpperCAmelCase     :  str							      =    max_length_s * sampling_rate
             _UpperCAmelCase     :  Any							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    frequency_min
             _UpperCAmelCase     :  str							      =    frequency_max
             _UpperCAmelCase     :  Union[str, Any]							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm=A						,					mel_scale="htk"						,					)
             _UpperCAmelCase     :  Tuple							      =    mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins						,					num_mel_filters=A						,					min_frequency=A						,					max_frequency=A						,					sampling_rate=A						,					norm="slaney"						,					mel_scale="slaney"						,					)




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    copy.deepcopy(self.__dict__       )
             _UpperCAmelCase     :  Dict							      =    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      _A	(       self						:				Optional[Any]						,					A						:				np.array						,					A						:				Optional[np.array] = None       ):
             _UpperCAmelCase     :  Dict							      =    spectrogram(
                 A						,					window_function(self.fft_window_size						,					"hann"       )						,					frame_length=self.fft_window_size						,					hop_length=self.hop_length						,					power=2.0						,					mel_filters=A						,					log_mel="dB"						,					)
             return log_mel_spectrogram.T




      def      _A	(       self						:				str						,					A						:				str						,					A						:				List[str]						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    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
                    _UpperCAmelCase     :  Optional[Any]							      =    [0]
             if len(ranges[2]       ) == 0:
                    # if the audio is too short, we just use the first chunk
                    _UpperCAmelCase     :  Tuple							      =    [0]
             # randomly choose index for each part
             _UpperCAmelCase     :  Dict							      =    np.random.choice(ranges[0]       )
             _UpperCAmelCase     :  str							      =    np.random.choice(ranges[1]       )
             _UpperCAmelCase     :  Tuple							      =    np.random.choice(ranges[2]       )

             _UpperCAmelCase     :  str							      =    mel[idx_front : idx_front + chunk_frames, :]
             _UpperCAmelCase     :  str							      =    mel[idx_middle : idx_middle + chunk_frames, :]
             _UpperCAmelCase     :  List[Any]							      =    mel[idx_back : idx_back + chunk_frames, :]

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




      def      _A	(       self						:				List[Any]						,					A						:				np.array						,					A						:				List[str]						,					A						:				Any						,					A						:				Optional[int]       ):
             if waveform.shape[0] > max_length:
                    if truncation == "rand_trunc":
                           _UpperCAmelCase     :  int							      =    True
                           # random crop to max_length (for compatibility) -> this should be handled by self.pad
                           _UpperCAmelCase     :  str							      =    len(A       ) - max_length
                           _UpperCAmelCase     :  str							      =    np.random.randint(0						,					overflow + 1       )
                           _UpperCAmelCase     :  int							      =    waveform[idx : idx + max_length]
                           _UpperCAmelCase     :  Any							      =    self._np_extract_fbank_features(A						,					self.mel_filters_slaney       )[None, :]
                    elif truncation == "fusion":
                           _UpperCAmelCase     :  Tuple							      =    self._np_extract_fbank_features(A						,					self.mel_filters       )
                           _UpperCAmelCase     :  List[str]							      =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                           _UpperCAmelCase     :  Optional[Any]							      =    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.
                                  _UpperCAmelCase     :  Any							      =    np.stack([mel, mel, mel, mel]						,					axis=0       )
                                  _UpperCAmelCase     :  int							      =    False
                           else:
                                  _UpperCAmelCase     :  Tuple							      =    self._random_mel_fusion(A						,					A						,					A       )
                                  _UpperCAmelCase     :  Any							      =    True
                    else:
                           raise NotImplementedError(F"""data_truncating {truncation} not implemented"""       )

             else:
                    _UpperCAmelCase     :  Optional[Any]							      =    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":
                                  _UpperCAmelCase     :  str							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  Dict							      =    np.stack(np.tile(A						,					n_repeat + 1       )       )[:max_length]
                           if padding == "repeatpad":
                                  _UpperCAmelCase     :  Dict							      =    int(max_length / len(A       )       )
                                  _UpperCAmelCase     :  List[str]							      =    np.stack(np.tile(A						,					A       )       )
                           _UpperCAmelCase     :  Optional[Any]							      =    np.pad(A						,					(0, max_length - waveform.shape[0])						,					mode="constant"						,					constant_values=0       )

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

             return input_mel, longer





      def __call__(       self						:				Union[str, Any]						,					A						:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,					A						:				str = None						,					A						:				Optional[str] = None						,					A						:				Optional[int] = None						,					A						:				Optional[int] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					**A						:				List[str]						,					):
             _UpperCAmelCase     :  int							      =    truncation if truncation is not None else self.truncation
             _UpperCAmelCase     :  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."       )

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

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

             # always return batch
             if not is_batched:
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A       )]

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

             _UpperCAmelCase     :  int							      =    []
             _UpperCAmelCase     :  Optional[Any]							      =    []
             for mel, longer in padded_inputs:
                    input_mel.append(A       )
                    is_longer.append(A       )

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

             if isinstance(input_mel[0]						,					A       ):
                    _UpperCAmelCase     :  List[str]							      =    [np.asarray(A						,					dtype=np.floataa       ) for feature in input_mel]

             # is_longer is a list of bool
             _UpperCAmelCase     :  Tuple							      =    [[longer] for longer in is_longer]

             _UpperCAmelCase     :  Optional[Any]							      =    {"input_features": input_mel, "is_longer": is_longer}
             _UpperCAmelCase     :  Tuple							      =    BatchFeature(A       )

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

             return input_features

