





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







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["input_features", "is_longer"]


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  List[str]=64       ,     __snake_case	:  List[Any]=4_80_00       ,     __snake_case	:  Any=4_80       ,     __snake_case	:  Optional[int]=10       ,     __snake_case	:  Optional[Any]=10_24       ,     __snake_case	:  List[Any]=0.0       ,     __snake_case	:  Union[str, Any]=False       ,     __snake_case	:  float = 0       ,     __snake_case	:  float = 1_40_00       ,     __snake_case	:  int = None       ,     __snake_case	:  str = "fusion"       ,     __snake_case	:  str = "repeatpad"       ,     **__snake_case	:  List[Any]       ,     ):





											'''simple docstring'''





											super().__init__(
											    feature_size=__snake_case       ,     sampling_rate=__snake_case       ,     padding_value=__snake_case       ,     return_attention_mask=__snake_case       ,     **__snake_case       ,     )
											_snake_case:   Dict  					=		top_db
											_snake_case:   Union[str, Any]  					=		truncation
											_snake_case:   Dict  					=		padding
											_snake_case:   Optional[int]  					=		fft_window_size
											_snake_case:   List[str]  					=		(fft_window_size >> 1) + 1
											_snake_case:   List[str]  					=		hop_length
											_snake_case:   str  					=		max_length_s
											_snake_case:   Union[str, Any]  					=		max_length_s * sampling_rate
											_snake_case:   Dict  					=		sampling_rate
											_snake_case:   Any  					=		frequency_min
											_snake_case:   Union[str, Any]  					=		frequency_max
											_snake_case:   List[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=__snake_case       ,     mel_scale='htk'       ,     )
											_snake_case:   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='slaney'       ,     mel_scale='slaney'       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		copy.deepcopy(self.__dict__       )
											_snake_case:   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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  np.array       ,     __snake_case	:  Optional[np.array] = None       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		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	:  Dict       ,     __snake_case	:  Any       ,     __snake_case	:  str       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		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:   Optional[Any]  					=		[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:   Union[str, Any]  					=		np.random.choice(ranges[0]       )
											_snake_case:   Tuple  					=		np.random.choice(ranges[1]       )
											_snake_case:   Any  					=		np.random.choice(ranges[2]       )

											_snake_case:   Optional[int]  					=		mel[idx_front : idx_front + chunk_frames, :]
											_snake_case:   Optional[int]  					=		mel[idx_middle : idx_middle + chunk_frames, :]
											_snake_case:   Dict  					=		mel[idx_back : idx_back + chunk_frames, :]

											_snake_case:   Optional[int]  					=		torch.tensor(mel[None, None, :]       )
											_snake_case:   List[Any]  					=		torch.nn.functional.interpolate(
											    __snake_case       ,     size=[chunk_frames, 64]       ,     mode='bilinear'       ,     align_corners=__snake_case       )
											_snake_case:   Any  					=		mel_shrink[0][0].numpy()
											_snake_case:   Optional[int]  					=		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,     axis=0       )
											return mel_fusion


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  np.array       ,     __snake_case	:  List[str]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  int       ):





											'''simple docstring'''





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

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

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

											return input_mel, longer




					def __call__(					self	:  List[Any]       ,     __snake_case	:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,     __snake_case	:  str = None       ,     __snake_case	:  Optional[str] = None       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Optional[int] = None       ,     __snake_case	:  Optional[Union[str, TensorType]] = None       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		truncation if truncation is not None else self.truncation
											_snake_case:   Tuple  					=		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:   Any  					=		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}'''       )
											_snake_case:   Union[str, Any]  					=		is_batched_numpy or (
											    isinstance(__snake_case       ,     (list, tuple)       ) and (isinstance(raw_speech[0]       ,     (np.ndarray, tuple, list)       ))
											)

											if is_batched:
																	_snake_case:   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       ):
																	_snake_case:   Union[str, Any]  					=		np.asarray(__snake_case       ,     dtype=np.floataa       )
											elif isinstance(__snake_case       ,     np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
																	_snake_case:   Dict  					=		raw_speech.astype(np.floataa       )

											# always return batch
											if not is_batched:
																	_snake_case:   List[str]  					=		[np.asarray(__snake_case       )]

											# convert to mel spectrogram, truncate and pad if needed.
											_snake_case:   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
											]

											_snake_case:   Optional[Any]  					=		[]
											_snake_case:   Any  					=		[]
											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
																	_snake_case:   Optional[int]  					=		np.random.randint(0       ,     len(__snake_case       )       )
																	_snake_case:   Optional[int]  					=		True

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

											# is_longer is a list of bool
											_snake_case:   Any  					=		[[longer] for longer in is_longer]

											_snake_case:   List[str]  					=		{'input_features': input_mel, 'is_longer': is_longer}
											_snake_case:   List[str]  					=		BatchFeature(__snake_case       )

											if return_tensors is not None:
																	_snake_case:   Optional[int]  					=		input_features.convert_to_tensors(__snake_case       )

											return input_features












'''simple docstring'''






def       lowercase_      (		lowercase__ = 50						)   ->int:
						_snake_case:   Union[str, Any]  					=		[[0] * 3 for _ in range(length + 1						)]

						for row_length in range(length + 1						):
												for tile_length in range(2				,  5						):
																		for tile_start in range(row_length - tile_length + 1						):
																								different_colour_ways_number[row_length][tile_length - 2] += (
																								    different_colour_ways_number[row_length - tile_start - tile_length][
																								        tile_length - 2
																								    ]
																								    + 1
																								)

						return sum(different_colour_ways_number[length]						)


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





