


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


snake_case							: Optional[Any]      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					int					   =    ['''input_features''', '''is_longer''']
					def __init__(   self     :Union[str, Any]    ,__snake_case     :Optional[Any]=64    ,__snake_case     :Dict=4_80_00    ,__snake_case     :Any=4_80    ,__snake_case     :Union[str, Any]=10    ,__snake_case     :List[Any]=10_24    ,__snake_case     :Dict=0.0    ,__snake_case     :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     :Optional[int]    ,)      ->					Optional[int]:
												super().__init__(
												    feature_size=__snake_case    ,sampling_rate=__snake_case    ,padding_value=__snake_case    ,return_attention_mask=__snake_case    ,**__snake_case    ,)
												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=__snake_case    ,min_frequency=__snake_case    ,max_frequency=__snake_case    ,sampling_rate=__snake_case    ,norm=__snake_case    ,mel_scale='htk'    ,)
												a__    =  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 				lowerCamelCase__(   self     :str   )      ->					Dict[str, Any]:
												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 				lowerCamelCase__(   self     :int    ,__snake_case     :np.array    ,__snake_case     :Optional[np.array] = None   )      ->					np.ndarray:
												a__    =  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 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Union[str, Any]    ,__snake_case     :Union[str, Any]    ,__snake_case     :Dict   )      ->					Optional[Any]:
												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(
												    __snake_case    ,size=[chunk_frames, 64]    ,mode='bilinear'    ,align_corners=__snake_case   )
												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 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :np.array    ,__snake_case     :Tuple    ,__snake_case     :Optional[Any]    ,__snake_case     :Dict   )      ->					np.array:
												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(__snake_case   ) - max_length
																										a__    =  np.random.randint(0    ,overflow + 1   )
																										a__    =  waveform[idx : idx + max_length]
																										a__    =  self._np_extract_fbank_features(__snake_case    ,self.mel_filters_slaney   )[None, :]
																			elif truncation == "fusion":
																										a__    =  self._np_extract_fbank_features(__snake_case    ,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(__snake_case    ,__snake_case    ,__snake_case   )
																																	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(__snake_case   )   )
																																	a__    =  np.stack(np.tile(__snake_case    ,n_repeat + 1   )   )[:max_length]
																										if padding == "repeatpad":
																																	a__    =  int(max_length / len(__snake_case   )   )
																																	a__    =  np.stack(np.tile(__snake_case    ,__snake_case   )   )
																										a__    =  np.pad(__snake_case    ,(0, max_length - waveform.shape[0])    ,mode='constant'    ,constant_values=0   )

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

												return input_mel, longer



					def __call__(   self     :Optional[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]    ,)      ->					BatchFeature:
												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(__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}'   )
												a__    =  is_batched_numpy or (
												    isinstance(__snake_case    ,(list, tuple)   ) and (isinstance(raw_speech[0]    ,(np.ndarray, tuple, list)   ))
												)

												if is_batched:
																			a__    =  [np.asarray(__snake_case    ,dtype=np.floataa   ) for speech in raw_speech]
												elif not is_batched and not isinstance(__snake_case    ,np.ndarray   ):
																			a__    =  np.asarray(__snake_case    ,dtype=np.floataa   )
												elif isinstance(__snake_case    ,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(__snake_case   )]

												# convert to mel spectrogram, truncate and pad if needed.
												a__    =  [
												    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
												]

												a__    =  []
												a__    =  []
												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
																			a__    =  np.random.randint(0    ,len(__snake_case   )   )
																			a__    =  True

												if isinstance(input_mel[0]    ,__snake_case   ):
																			a__    =  [np.asarray(__snake_case    ,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(__snake_case   )

												if return_tensors is not None:
																			a__    =  input_features.convert_to_tensors(__snake_case   )

												return input_features








from math import ceil, sqrt






def 							__lowercase     (				__lowerCAmelCase							:       int = 1_0_0_0_0_0_0     ):
							a__    =  0

							for outer_width in range(3      ,					(limit // 4) + 2     ):
														if outer_width**2 > limit:
																					a__    =  max(ceil(sqrt(outer_width**2 - limit     )     )      ,					1     )
														else:
																					a__    =  1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


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




