



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


UpperCamelCase__ =						logging.get_logger(__name__)
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  str      =			["""input_features""", """is_longer"""]





			def __init__(				self	:       List[str]			,     lowercase	:       Tuple=64			,     lowercase	:       Any=48_000			,     lowercase	:       Tuple=480			,     lowercase	:       str=10			,     lowercase	:       Union[str, Any]=1_024			,     lowercase	:       Optional[int]=0.0			,     lowercase	:       Optional[int]=False			,     lowercase	:       float = 0			,     lowercase	:       float = 14_000			,     lowercase	:       int = None			,     lowercase	:       str = "fusion"			,     lowercase	:       str = "repeatpad"			,     **lowercase	:       int			,     )						->					Tuple:
									"""simple docstring"""







									super().__init__(
									    feature_size=lowercase			,     sampling_rate=lowercase			,     padding_value=lowercase			,     return_attention_mask=lowercase			,     **lowercase			,     )
									__lowercase					=				top_db
									__lowercase					=				truncation
									__lowercase					=				padding
									__lowercase					=				fft_window_size
									__lowercase					=				(fft_window_size >> 1) + 1
									__lowercase					=				hop_length
									__lowercase					=				max_length_s
									__lowercase					=				max_length_s * sampling_rate
									__lowercase					=				sampling_rate
									__lowercase					=				frequency_min
									__lowercase					=				frequency_max
									__lowercase					=				mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins			,     num_mel_filters=lowercase			,     min_frequency=lowercase			,     max_frequency=lowercase			,     sampling_rate=lowercase			,     norm=lowercase			,     mel_scale="""htk"""			,     )
									__lowercase					=				mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins			,     num_mel_filters=lowercase			,     min_frequency=lowercase			,     max_frequency=lowercase			,     sampling_rate=lowercase			,     norm="""slaney"""			,     mel_scale="""slaney"""			,     )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Dict[str, Any]:
									"""simple docstring"""







									__lowercase					=				copy.deepcopy(self.__dict__   )
									__lowercase					=				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 		snake_case__			(				self	:       str			,     lowercase	:       np.array			,     lowercase	:       Optional[np.array] = None   )						->					np.ndarray:
									"""simple docstring"""







									__lowercase					=				spectrogram(
									    lowercase			,     window_function(self.fft_window_size			,     """hann"""   )			,     frame_length=self.fft_window_size			,     hop_length=self.hop_length			,     power=2.0			,     mel_filters=lowercase			,     log_mel="""dB"""			,     )
									return log_mel_spectrogram.T





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       Union[str, Any]			,     lowercase	:       Any			,     lowercase	:       List[Any]   )						->					str:
									"""simple docstring"""







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

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

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





			def 		snake_case__			(				self	:       Any			,     lowercase	:       np.array			,     lowercase	:       List[str]			,     lowercase	:       Dict			,     lowercase	:       Optional[Any]   )						->					np.array:
									"""simple docstring"""







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

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

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

									return input_mel, longer







			def __call__(				self	:       Any			,     lowercase	:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,     lowercase	:       str = None			,     lowercase	:       Optional[str] = None			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[Union[str, TensorType]] = None			,     **lowercase	:       Dict			,     )						->					BatchFeature:
									"""simple docstring"""







									__lowercase					=				truncation if truncation is not None else self.truncation
									__lowercase					=				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					=				isinstance(lowercase			,     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					=				is_batched_numpy or (
									    isinstance(lowercase			,     (list, tuple)   ) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)   ))
									)

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

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

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

									__lowercase					=				[]
									__lowercase					=				[]
									for mel, longer in padded_inputs:
															input_mel.append(lowercase   )
															is_longer.append(lowercase   )

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

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

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

									__lowercase					=				{"""input_features""": input_mel, """is_longer""": is_longer}
									__lowercase					=				BatchFeature(lowercase   )

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

									return input_features










def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						__lowercase					=				(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
						# formula for sum of series
						return total






def  UpperCAmelCase__    (    ) ->      List[str]:
						print(sum_of_series(1						,       1						,       10   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





