


'''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


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)


class  lowerCAmelCase_						(       lowercase__      ):





		"""simple docstring"""



		a_       :Optional[int]												=["""input_features""", """is_longer"""]

		def __init__(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[Any]=6_4				,							SCREAMING_SNAKE_CASE__     :							str=4_8_0_0_0				,							SCREAMING_SNAKE_CASE__     :							List[Any]=4_8_0				,							SCREAMING_SNAKE_CASE__     :							List[str]=1_0				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1_0_2_4				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=0.0				,							SCREAMING_SNAKE_CASE__     :							List[Any]=False				,							SCREAMING_SNAKE_CASE__     :							float = 0				,							SCREAMING_SNAKE_CASE__     :							float = 1_4_0_0_0				,							SCREAMING_SNAKE_CASE__     :							int = None				,							SCREAMING_SNAKE_CASE__     :							str = "fusion"				,							SCREAMING_SNAKE_CASE__     :							str = "repeatpad"				,							**SCREAMING_SNAKE_CASE__     :							int				,							):





							'''simple docstring'''

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

		def __a						(					self     :							int						):





							'''simple docstring'''

							__a       =   copy.deepcopy(self.__dict__						)
							__a       =   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     :							Any				,							SCREAMING_SNAKE_CASE__     :							np.array				,							SCREAMING_SNAKE_CASE__     :							Optional[np.array] = None						):





							'''simple docstring'''

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

		def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							str						):





							'''simple docstring'''

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

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

							__a       =   torch.tensor(mel[None, None, :]						)
							__a       =   torch.nn.functional.interpolate(
							    UpperCAmelCase__				,							size=[chunk_frames, 6_4]				,							mode="""bilinear"""				,							align_corners=UpperCAmelCase__						)
							__a       =   mel_shrink[0][0].numpy()
							__a       =   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,							axis=0						)
							return mel_fusion

		def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							np.array				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





							'''simple docstring'''

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

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

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

							return input_mel, longer


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





							'''simple docstring'''

							__a       =   truncation if truncation is not None else self.truncation
							__a       =   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."""						)

							__a       =   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}'''						)
							__a       =   is_batched_numpy or (
							    isinstance(UpperCAmelCase__				,							(list, tuple)						) and (isinstance(raw_speech[0]				,							(np.ndarray, tuple, list)						))
							)

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

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

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

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

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

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

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

							__a       =   {'''input_features''': input_mel, '''is_longer''': is_longer}
							__a       =   BatchFeature(UpperCAmelCase__						)

							if return_tensors is not None:
												__a       =   input_features.convert_to_tensors(UpperCAmelCase__						)

							return input_features










'''simple docstring'''







SCREAMING_SNAKE_CASE_      =			0  # The first color of the flag.
SCREAMING_SNAKE_CASE_      =			1  # The second color of the flag.
SCREAMING_SNAKE_CASE_      =			2  # The third color of the flag.
SCREAMING_SNAKE_CASE_      =			(red, white, blue)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					list:
     """simple docstring"""




     if not sequence:
          return []
     if len(__SCREAMING_SNAKE_CASE		) == 1:
          return list(__SCREAMING_SNAKE_CASE		)
     __a       =   0
     __a       =   len(__SCREAMING_SNAKE_CASE		) - 1
     __a       =   0
     while mid <= high:
          if sequence[mid] == colors[0]:
               __a ,						__a       =   sequence[mid], sequence[low]
               low += 1
               mid += 1
          elif sequence[mid] == colors[1]:
               mid += 1
          elif sequence[mid] == colors[2]:
               __a ,						__a       =   sequence[high], sequence[mid]
               high -= 1
          else:
               __a       =   F'''The elements inside the sequence must contains only {colors} values'''
               raise ValueError(__SCREAMING_SNAKE_CASE		)
     return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE_      =			input('Enter numbers separated by commas:\n').strip()
 SCREAMING_SNAKE_CASE_      =			[int(item.strip()) for item in user_input.split(',')]
 print(f"""{dutch_national_flag_sort(unsorted)}""")






