






'''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


lowerCamelCase_				   =   logging.get_logger(__name__)

class   _UpperCAmelCase     (	snake_case_		):




   """simple docstring"""

   snake_case       							=		["""input_features""", """is_longer"""]

   def __init__(						self					:      Tuple , __UpperCAmelCase					:      Any=64 , __UpperCAmelCase					:      Optional[Any]=48000 , __UpperCAmelCase					:      Union[str, Any]=480 , __UpperCAmelCase					:      str=10 , __UpperCAmelCase					:      Union[str, Any]=1024 , __UpperCAmelCase					:      List[str]=0.0 , __UpperCAmelCase					:      Optional[Any]=False , __UpperCAmelCase					:      float = 0 , __UpperCAmelCase					:      float = 14000 , __UpperCAmelCase					:      int = None , __UpperCAmelCase					:      str = "fusion" , __UpperCAmelCase					:      str = "repeatpad" , **__UpperCAmelCase					:      List[str] , ):





         '''simple docstring'''





         super().__init__(
             feature_size=a_ , sampling_rate=a_ , padding_value=a_ , return_attention_mask=a_ , **a_ , )
         _A     	=      top_db
         _A     	=      truncation
         _A     	=      padding
         _A     	=      fft_window_size
         _A     	=      (fft_window_size >> 1) + 1
         _A     	=      hop_length
         _A     	=      max_length_s
         _A     	=      max_length_s * sampling_rate
         _A     	=      sampling_rate
         _A     	=      frequency_min
         _A     	=      frequency_max
         _A     	=      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" , )
         _A     	=      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 		lowerCAmelCase					(						self					:      int						):





         '''simple docstring'''





         _A     	=      copy.deepcopy(self.__dict__						)
         _A     	=      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 		lowerCAmelCase					(						self					:      Tuple , __UpperCAmelCase					:      np.array , __UpperCAmelCase					:      Optional[np.array] = None						):





         '''simple docstring'''





         _A     	=      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 		lowerCAmelCase					(						self					:      Optional[int] , __UpperCAmelCase					:      int , __UpperCAmelCase					:      Union[str, Any] , __UpperCAmelCase					:      Union[str, Any]						):





         '''simple docstring'''





         _A     	=      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
               _A     	=      [0]
         if len(ranges[2]						) == 0:
               # if the audio is too short, we just use the first chunk
               _A     	=      [0]
         # randomly choose index for each part
         _A     	=      np.random.choice(ranges[0]						)
         _A     	=      np.random.choice(ranges[1]						)
         _A     	=      np.random.choice(ranges[2]						)

         _A     	=      mel[idx_front : idx_front + chunk_frames, :]
         _A     	=      mel[idx_middle : idx_middle + chunk_frames, :]
         _A     	=      mel[idx_back : idx_back + chunk_frames, :]

         _A     	=      torch.tensor(mel[None, None, :]						)
         _A     	=      torch.nn.functional.interpolate(
             a_ , size=[chunk_frames, 64] , mode="bilinear" , align_corners=a_						)
         _A     	=      mel_shrink[0][0].numpy()
         _A     	=      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0						)
         return mel_fusion

   def 		lowerCAmelCase					(						self					:      Any , __UpperCAmelCase					:      np.array , __UpperCAmelCase					:      Dict , __UpperCAmelCase					:      Tuple , __UpperCAmelCase					:      Any						):





         '''simple docstring'''





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

         else:
               _A     	=      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":
                           _A     	=      int(max_length / len(a_						)						)
                           _A     	=      np.stack(np.tile(a_ , n_repeat + 1						)						)[:max_length]
                     if padding == "repeatpad":
                           _A     	=      int(max_length / len(a_						)						)
                           _A     	=      np.stack(np.tile(a_ , a_						)						)
                     _A     	=      np.pad(a_ , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0						)

               if truncation == "fusion":
                     _A     	=      self._np_extract_fbank_features(a_ , self.mel_filters						)
                     _A     	=      np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0						)
               else:
                     _A     	=      self._np_extract_fbank_features(a_ , 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					:      Dict , ):





         '''simple docstring'''





         _A     	=      truncation if truncation is not None else self.truncation
         _A     	=      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."						)

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

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

         # always return batch
         if not is_batched:
               _A     	=      [np.asarray(a_						)]

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

         _A     	=      []
         _A     	=      []
         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
               _A     	=      np.random.randint(0 , len(a_						)						)
               _A     	=      True

         if isinstance(input_mel[0] , a_						):
               _A     	=      [np.asarray(a_ , dtype=np.floataa						) for feature in input_mel]

         # is_longer is a list of bool
         _A     	=      [[longer] for longer in is_longer]

         _A     	=      {'input_features': input_mel, 'is_longer': is_longer}
         _A     	=      BatchFeature(a_						)

         if return_tensors is not None:
               _A     	=      input_features.convert_to_tensors(a_						)

         return input_features










'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




