



'''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


a							   =				logging.get_logger(__name__)






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Any        =				["""input_features""", """is_longer"""]


							def __init__(				self   :						Tuple      ,						a_   :						Any=6_4      ,						a_   :						Optional[Any]=4_8_0_0_0      ,						a_   :						Union[str, Any]=4_8_0      ,						a_   :						str=1_0      ,						a_   :						Union[str, Any]=1_0_2_4      ,						a_   :						List[str]=0.0      ,						a_   :						Optional[Any]=False      ,						a_   :						float = 0      ,						a_   :						float = 1_4_0_0_0      ,						a_   :						int = None      ,						a_   :						str = "fusion"      ,						a_   :						str = "repeatpad"      ,						**a_   :						List[str]      ,						)			->					Tuple:
										super().__init__(
										    feature_size=a_      ,						sampling_rate=a_      ,						padding_value=a_      ,						return_attention_mask=a_      ,						**a_      ,						)
										snake_case:    Optional[Any]      							=top_db
										snake_case:    Union[str, Any]      							=truncation
										snake_case:    List[Any]      							=padding
										snake_case:    Optional[Any]      							=fft_window_size
										snake_case:    Optional[int]      							=(fft_window_size >> 1) + 1
										snake_case:    List[str]      							=hop_length
										snake_case:    Dict      							=max_length_s
										snake_case:    List[Any]      							=max_length_s * sampling_rate
										snake_case:    str      							=sampling_rate
										snake_case:    List[str]      							=frequency_min
										snake_case:    str      							=frequency_max
										snake_case:    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'      ,						)
										snake_case:    Dict      							=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    UpperCamelCase     (				self   :						int			)			->					Dict[str, Any]:
										snake_case:    Optional[Any]      							=copy.deepcopy(self.__dict__			)
										snake_case:    List[str]      							=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   :						Tuple      ,						a_   :						np.array      ,						a_   :						Optional[np.array] = None			)			->					np.ndarray:
										snake_case:    str      							=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    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						int      ,						a_   :						Union[str, Any]      ,						a_   :						Union[str, Any]			)			->					Dict:
										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:    str      							=[0]
										if len(ranges[2]			) == 0:
													# if the audio is too short, we just use the first chunk
													snake_case:    str      							=[0]
										# randomly choose index for each part
										snake_case:    str      							=np.random.choice(ranges[0]			)
										snake_case:    str      							=np.random.choice(ranges[1]			)
										snake_case:    Union[str, Any]      							=np.random.choice(ranges[2]			)

										snake_case:    Union[str, Any]      							=mel[idx_front : idx_front + chunk_frames, :]
										snake_case:    int      							=mel[idx_middle : idx_middle + chunk_frames, :]
										snake_case:    List[Any]      							=mel[idx_back : idx_back + chunk_frames, :]

										snake_case:    Tuple      							=torch.tensor(mel[None, None, :]			)
										snake_case:    Union[str, Any]      							=torch.nn.functional.interpolate(
										    a_      ,						size=[chunk_frames, 6_4]      ,						mode='bilinear'      ,						align_corners=a_			)
										snake_case:    Any      							=mel_shrink[0][0].numpy()
										snake_case:    int      							=np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,						axis=0			)
										return mel_fusion


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

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

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

										return input_mel, longer




							def __call__(				self   :						List[str]      ,						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_   :						Dict      ,						)			->					BatchFeature:
										snake_case:    Dict      							=truncation if truncation is not None else self.truncation
										snake_case:    str      							=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:    Tuple      							=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}'''			)
										snake_case:    Tuple      							=is_batched_numpy or (
										    isinstance(a_      ,						(list, tuple)			) and (isinstance(raw_speech[0]      ,						(np.ndarray, tuple, list)			))
										)

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

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

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

										snake_case:    Union[str, Any]      							=[]
										snake_case:    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
													snake_case:    Any      							=np.random.randint(0      ,						len(a_			)			)
													snake_case:    Any      							=True

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

										# is_longer is a list of bool
										snake_case:    List[str]      							=[[longer] for longer in is_longer]

										snake_case:    Optional[int]      							={'input_features': input_mel, 'is_longer': is_longer}
										snake_case:    List[Any]      							=BatchFeature(a_			)

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

										return input_features









'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




