



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)





class  __magic_name__   (							A__						):
		lowercase      :       Optional[int]          =['''audio_values''', '''audio_mask''']






		def __init__(						self      :     Dict				,		UpperCamelCase__      :     List[str]=20_48				,		UpperCamelCase__      :     Optional[Any]=1				,		UpperCamelCase__      :     int=[16, 16]				,		UpperCamelCase__      :     str=1_28				,		UpperCamelCase__      :     List[Any]=4_41_00				,		UpperCamelCase__      :     Dict=86				,		UpperCamelCase__      :     List[str]=20_48				,		UpperCamelCase__      :     Dict=0.0				,		**UpperCamelCase__      :     List[Any]				,		)				->					Tuple:
								'''simple docstring'''







								super().__init__(
								    feature_size=UpperCamelCase__				,		sampling_rate=UpperCamelCase__				,		padding_value=UpperCamelCase__				,		**UpperCamelCase__				,		)

								UpperCAmelCase         =     spectrogram_length
								UpperCAmelCase         =     num_channels
								UpperCAmelCase         =     patch_size
								UpperCAmelCase         =     feature_size // self.patch_size[1]
								UpperCAmelCase         =     n_fft
								UpperCAmelCase         =     sampling_rate // hop_length_to_sampling_rate
								UpperCAmelCase         =     sampling_rate
								UpperCAmelCase         =     padding_value
								UpperCAmelCase         =     mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2				,		num_mel_filters=UpperCamelCase__				,		min_frequency=0.0				,		max_frequency=2_20_50.0				,		sampling_rate=UpperCamelCase__				,		norm="slaney"				,		mel_scale="slaney"				,		).T






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     np.array      )				->					np.ndarray:
								'''simple docstring'''







								UpperCAmelCase         =     spectrogram(
								    UpperCamelCase__				,		window_function(self.n_fft				,		"hann"      )				,		frame_length=self.n_fft				,		hop_length=self.hop_length				,		power=2.0				,		mel_filters=self.mel_filters.T				,		log_mel="dB"				,		db_range=80.0				,		)
								UpperCAmelCase         =     log_spec[:, :-1]
								UpperCAmelCase         =     log_spec - 20.0
								UpperCAmelCase         =     np.clip(log_spec / 40.0				,		-2.0				,		0.0      ) + 1.0
								return log_spec







		def __call__(						self      :     Any				,		UpperCamelCase__      :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,		UpperCamelCase__      :     Optional[Union[str, TensorType]] = None				,		UpperCamelCase__      :     Optional[bool] = True				,		UpperCamelCase__      :     Optional[int] = None				,		UpperCamelCase__      :     bool = False				,		UpperCamelCase__      :     bool = False				,		**UpperCamelCase__      :     int				,		)				->					BatchFeature:
								'''simple docstring'''







								if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																				raise ValueError(
																				    "This feature extractor is set to support sampling rate"
																				    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
																				    F' 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         =     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}'      )
								UpperCAmelCase         =     is_batched_numpy or (
								    isinstance(UpperCamelCase__				,		(list, tuple)      ) and (isinstance(raw_speech[0]				,		(np.ndarray, tuple, list)      ))
								)
								if is_batched:
														UpperCAmelCase         =     [np.asarray([speech]				,		dtype=np.floataa      ).T for speech in raw_speech]
								elif not is_batched and not isinstance(UpperCamelCase__				,		np.ndarray      ):
														UpperCAmelCase         =     np.asarray(UpperCamelCase__				,		dtype=np.floataa      )
								elif isinstance(UpperCamelCase__				,		np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
														UpperCAmelCase         =     raw_speech.astype(np.floataa      )
								# always return batch
								if not is_batched:
														UpperCAmelCase         =     [np.asarray([raw_speech]      ).T]

								# Convert audio signals to log mel spectrograms, truncate by time axis
								UpperCAmelCase         =     [
								    self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
								]
								if isinstance(audio_features[0]				,		UpperCamelCase__      ):
														UpperCAmelCase         =     [np.asarray(UpperCamelCase__				,		dtype=np.floataa      ) for feature in audio_features]

								# Create audio attention mask
								UpperCAmelCase         =     max(
								    [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
								if return_attention_mask:
														UpperCAmelCase         =     [
														    (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
														    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
														    for feature in audio_features
														]
														UpperCAmelCase         =     np.array(UpperCamelCase__      ).astype(np.floataa      )

								# convert into correct format for padding
								UpperCAmelCase         =     max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
								UpperCAmelCase         =     np.ones([len(UpperCamelCase__      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
								UpperCAmelCase         =     padded_audio_features * self.padding_value
								for i in range(len(UpperCamelCase__      )      ):
														UpperCAmelCase         =     audio_features[i]
														UpperCAmelCase         =     feature

								# return as BatchFeature
								if return_attention_mask:
														UpperCAmelCase         =     {"audio_values": padded_audio_features, "audio_mask": audio_mask}
								else:
														UpperCAmelCase         =     {"audio_values": padded_audio_features}

								UpperCAmelCase         =     BatchFeature(data=UpperCamelCase__				,		tensor_type=UpperCamelCase__      )
								return encoded_inputs








from ..utils import DummyObject, requires_backends





class  __magic_name__   (							metaclass=A__						):
		lowercase      :       int          =['''note_seq''']






		def __init__(						self      :     List[Any]				,		*UpperCamelCase__      :     Tuple				,		**UpperCamelCase__      :     Dict      )				->					int:
								'''simple docstring'''







								requires_backends(self				,		["note_seq"]      )






		@classmethod
		def SCREAMING_SNAKE_CASE_			(						cls      :     List[str]				,		*UpperCamelCase__      :     Dict				,		**UpperCamelCase__      :     str      )				->					Optional[int]:
								'''simple docstring'''







								requires_backends(cls				,		["note_seq"]      )







		@classmethod
		def SCREAMING_SNAKE_CASE_			(						cls      :     List[Any]				,		*UpperCamelCase__      :     Optional[int]				,		**UpperCamelCase__      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







								requires_backends(cls				,		["note_seq"]      )



