





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		:  Optional[int]			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						List[str]						    =					['input_features', 'is_longer']



					def __init__(		self     :     int       ,      lowerCamelCase     :     str=64       ,      lowerCamelCase     :     str=4_80_00       ,      lowerCamelCase     :     Any=4_80       ,      lowerCamelCase     :     List[str]=10       ,      lowerCamelCase     :     List[Any]=10_24       ,      lowerCamelCase     :     Union[str, Any]=0.0       ,      lowerCamelCase     :     Tuple=False       ,      lowerCamelCase     :     float = 0       ,      lowerCamelCase     :     float = 1_40_00       ,      lowerCamelCase     :     int = None       ,      lowerCamelCase     :     str = "fusion"       ,      lowerCamelCase     :     str = "repeatpad"       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							super().__init__(
							    feature_size=lowerCamelCase       ,      sampling_rate=lowerCamelCase       ,      padding_value=lowerCamelCase       ,      return_attention_mask=lowerCamelCase       ,      **lowerCamelCase       ,      )
							lowerCamelCase_	:     List[Any] 						=      top_db
							lowerCamelCase_	:     Union[str, Any] 						=      truncation
							lowerCamelCase_	:     str 						=      padding
							lowerCamelCase_	:     Union[str, Any] 						=      fft_window_size
							lowerCamelCase_	:     int 						=      (fft_window_size >> 1) + 1
							lowerCamelCase_	:     Tuple 						=      hop_length
							lowerCamelCase_	:     Optional[int] 						=      max_length_s
							lowerCamelCase_	:     Optional[Any] 						=      max_length_s * sampling_rate
							lowerCamelCase_	:     int 						=      sampling_rate
							lowerCamelCase_	:     Union[str, Any] 						=      frequency_min
							lowerCamelCase_	:     str 						=      frequency_max
							lowerCamelCase_	:     str 						=      mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins       ,      num_mel_filters=lowerCamelCase       ,      min_frequency=lowerCamelCase       ,      max_frequency=lowerCamelCase       ,      sampling_rate=lowerCamelCase       ,      norm=lowerCamelCase       ,      mel_scale='htk'       ,      )
							lowerCamelCase_	:     Optional[Any] 						=      mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins       ,      num_mel_filters=lowerCamelCase       ,      min_frequency=lowerCamelCase       ,      max_frequency=lowerCamelCase       ,      sampling_rate=lowerCamelCase       ,      norm='slaney'       ,      mel_scale='slaney'       ,      )



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     str 						=      copy.deepcopy(self.__dict__						)
							lowerCamelCase_	:     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   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     np.array       ,      lowerCamelCase     :     Optional[np.array] = None						):
							lowerCamelCase_	:     Dict 						=      spectrogram(
							    lowerCamelCase       ,      window_function(self.fft_window_size       ,      'hann'						)       ,      frame_length=self.fft_window_size       ,      hop_length=self.hop_length       ,      power=2.0       ,      mel_filters=lowerCamelCase       ,      log_mel='dB'       ,      )
							return log_mel_spectrogram.T



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

							lowerCamelCase_	:     str 						=      mel[idx_front : idx_front + chunk_frames, :]
							lowerCamelCase_	:     Dict 						=      mel[idx_middle : idx_middle + chunk_frames, :]
							lowerCamelCase_	:     List[str] 						=      mel[idx_back : idx_back + chunk_frames, :]

							lowerCamelCase_	:     str 						=      torch.tensor(mel[None, None, :]						)
							lowerCamelCase_	:     Union[str, Any] 						=      torch.nn.functional.interpolate(
							    lowerCamelCase       ,      size=[chunk_frames, 64]       ,      mode='bilinear'       ,      align_corners=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      mel_shrink[0][0].numpy()
							lowerCamelCase_	:     Dict 						=      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,      axis=0						)
							return mel_fusion



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

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

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

							return input_mel, longer





					def __call__(		self     :     Tuple       ,      lowerCamelCase     :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,      lowerCamelCase     :     str = None       ,      lowerCamelCase     :     Optional[str] = None       ,      lowerCamelCase     :     Optional[int] = None       ,      lowerCamelCase     :     Optional[int] = None       ,      lowerCamelCase     :     Optional[Union[str, TensorType]] = None       ,      **lowerCamelCase     :     int       ,      ):
							lowerCamelCase_	:     Any 						=      truncation if truncation is not None else self.truncation
							lowerCamelCase_	:     int 						=      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.'						)

							lowerCamelCase_	:     Tuple 						=      isinstance(lowerCamelCase       ,      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}"						)
							lowerCamelCase_	:     List[Any] 						=      is_batched_numpy or (
							    isinstance(lowerCamelCase       ,      (list, tuple)						) and (isinstance(raw_speech[0]       ,      (np.ndarray, tuple, list)						))
							)

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

							# always return batch
							if not is_batched:
									lowerCamelCase_	:     Optional[int] 						=      [np.asarray(lowerCamelCase						)]

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

							lowerCamelCase_	:     List[Any] 						=      []
							lowerCamelCase_	:     List[Any] 						=      []
							for mel, longer in padded_inputs:
									input_mel.append(lowerCamelCase						)
									is_longer.append(lowerCamelCase						)

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

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

							# is_longer is a list of bool
							lowerCamelCase_	:     Dict 						=      [[longer] for longer in is_longer]

							lowerCamelCase_	:     Optional[int] 						=      {'input_features': input_mel, 'is_longer': is_longer}
							lowerCamelCase_	:     str 						=      BatchFeature(lowerCamelCase						)

							if return_tensors is not None:
									lowerCamelCase_	:     str 						=      input_features.convert_to_tensors(lowerCamelCase						)

							return input_features







_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


if __name__ == "__main__":
		print(F'''{solution() = }''')
