






"""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


lowercase    			=       logging.get_logger(__name__)

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''input_features''', '''is_longer''']







				def __init__(							self							:						str						,    __lowerCamelCase							:						Dict=6_4						,    __lowerCamelCase							:						Any=4_8_0_0_0						,    __lowerCamelCase							:						Optional[int]=4_8_0						,    __lowerCamelCase							:						Optional[Any]=1_0						,    __lowerCamelCase							:						Union[str, Any]=1_0_2_4						,    __lowerCamelCase							:						Tuple=0.0						,    __lowerCamelCase							:						List[str]=False						,    __lowerCamelCase							:						float = 0						,    __lowerCamelCase							:						float = 1_4_0_0_0						,    __lowerCamelCase							:						int = None						,    __lowerCamelCase							:						str = "fusion"						,    __lowerCamelCase							:						str = "repeatpad"						,    **__lowerCamelCase							:						Union[str, Any]						,    )				->					Union[str, Any]:
										super().__init__(
										    feature_size=__lowerCamelCase						,    sampling_rate=__lowerCamelCase						,    padding_value=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    **__lowerCamelCase						,    )
										__magic_name__            =     top_db
										__magic_name__            =     truncation
										__magic_name__            =     padding
										__magic_name__            =     fft_window_size
										__magic_name__            =     (fft_window_size >> 1) + 1
										__magic_name__            =     hop_length
										__magic_name__            =     max_length_s
										__magic_name__            =     max_length_s * sampling_rate
										__magic_name__            =     sampling_rate
										__magic_name__            =     frequency_min
										__magic_name__            =     frequency_max
										__magic_name__            =     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"						,    )
										__magic_name__            =     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     _snake_case					(							self							:						List[Any]	)				->					Dict[str, Any]:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										__magic_name__            =     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							:						List[str]						,    __lowerCamelCase							:						np.array						,    __lowerCamelCase							:						Optional[np.array] = None	)				->					np.ndarray:
										__magic_name__            =     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     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						str	)				->					int:
										__magic_name__            =     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
																__magic_name__            =     [0]
										if len(ranges[2]	) == 0:
																# if the audio is too short, we just use the first chunk
																__magic_name__            =     [0]
										# randomly choose index for each part
										__magic_name__            =     np.random.choice(ranges[0]	)
										__magic_name__            =     np.random.choice(ranges[1]	)
										__magic_name__            =     np.random.choice(ranges[2]	)

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

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







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

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

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

										return input_mel, longer







				def __call__(							self							:						Optional[Any]						,    __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							:						Dict						,    )				->					BatchFeature:
										__magic_name__            =     truncation if truncation is not None else self.truncation
										__magic_name__            =     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."	)

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

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

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

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

										__magic_name__            =     []
										__magic_name__            =     []
										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
																__magic_name__            =     np.random.randint(0						,    len(__lowerCamelCase	)	)
																__magic_name__            =     True

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

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

										__magic_name__            =     {"input_features": input_mel, "is_longer": is_longer}
										__magic_name__            =     BatchFeature(__lowerCamelCase	)

										if return_tensors is not None:
																__magic_name__            =     input_features.convert_to_tensors(__lowerCamelCase	)

										return input_features











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list   ):

						'''simple docstring'''






						__magic_name__            =     len(__lowerCamelCase   )
						for i in range(1 ,						__lowerCamelCase   ):
												__magic_name__            =     collection[i]
												__magic_name__            =     0
												__magic_name__            =     i - 1

												while low <= high:
																		__magic_name__            =     (low + high) // 2
																		if val < collection[mid]:
																								__magic_name__            =     mid - 1
																		else:
																								__magic_name__            =     mid + 1
												for j in range(__lowerCamelCase ,						__lowerCamelCase ,						-1   ):
																		__magic_name__            =     collection[j - 1]
												__magic_name__            =     val
						return collection


if __name__ == "__main__":
			lowercase    			=       input('''Enter numbers separated by a comma:\n''').strip()
			lowercase    			=       [int(item) for item in user_input.split(''',''')]
			print(binary_insertion_sort(unsorted))



