
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


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

class __snake_case		(				lowerCAmelCase					):
    _a		:						List[Any]=    ["input_features", "is_longer"]
    def __init__(  self		,snake_case=64		,snake_case=48000		,snake_case=480		,snake_case=10		,snake_case=1024		,snake_case=0.0		,snake_case=False		,snake_case = 0		,snake_case = 14000		,snake_case = None		,snake_case = "fusion"		,snake_case = "repeatpad"		,**snake_case		,):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				copy.deepcopy(self.__dict__				)
           lowercase							:					Tuple	     =				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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					int	     =				spectrogram(
               snake_case		,window_function(self.fft_window_size		,"""hann"""				)		,frame_length=self.fft_window_size		,hop_length=self.hop_length		,power=2.0		,mel_filters=snake_case		,log_mel="""dB"""		,)
           return log_mel_spectrogram.T
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







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

           lowercase							:					Union[str, Any]	     =				mel[idx_front : idx_front + chunk_frames, :]
           lowercase							:					Optional[Any]	     =				mel[idx_middle : idx_middle + chunk_frames, :]
           lowercase							:					List[Any]	     =				mel[idx_back : idx_back + chunk_frames, :]

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



           '''simple docstring'''







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

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

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

           return input_mel, longer
    def __call__(  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Tuple	     =				truncation if truncation is not None else self.truncation
           lowercase							:					List[Any]	     =				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."""				)

           lowercase							:					int	     =				isinstance(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}"				)
           lowercase							:					Any	     =				is_batched_numpy or (
               isinstance(snake_case		,(list, tuple)				) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)				))
           )

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

           # always return batch
           if not is_batched:
                  lowercase							:					Union[str, Any]	     =				[np.asarray(snake_case				)]

           # convert to mel spectrogram, truncate and pad if needed.
           lowercase							:					List[str]	     =				[
               self._get_input_mel(snake_case		,max_length if max_length else self.nb_max_samples		,snake_case		,snake_case				)
               for waveform in raw_speech
           ]

           lowercase							:					List[Any]	     =				[]
           lowercase							:					str	     =				[]
           for mel, longer in padded_inputs:
                  input_mel.append(snake_case				)
                  is_longer.append(snake_case				)

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

           if isinstance(input_mel[0]		,snake_case				):
                  lowercase							:					Optional[int]	     =				[np.asarray(snake_case		,dtype=np.floataa				) for feature in input_mel]

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

           lowercase							:					int	     =				{"""input_features""": input_mel, """is_longer""": is_longer}
           lowercase							:					Optional[Any]	     =				BatchFeature(snake_case				)

           if return_tensors is not None:
                  lowercase							:					int	     =				input_features.convert_to_tensors(snake_case				)

           return input_features


print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
