





"""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


__magic_name__       :					int						=			logging.get_logger(__name__)






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  ["""input_features""", """is_longer"""]







			def __init__(      self						,   _A=6_4						,   _A=4_8_0_0_0						,   _A=4_8_0						,   _A=1_0						,   _A=1_0_2_4						,   _A=0.0						,   _A=False						,   _A = 0						,   _A = 1_4_0_0_0						,   _A = None						,   _A = "fusion"						,   _A = "repeatpad"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    feature_size=_A						,   sampling_rate=_A						,   padding_value=_A						,   return_attention_mask=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[int]				   =			top_db
									UpperCamelCase				:		Tuple				   =			truncation
									UpperCamelCase				:		str				   =			padding
									UpperCamelCase				:		str				   =			fft_window_size
									UpperCamelCase				:		int				   =			(fft_window_size >> 1) + 1
									UpperCamelCase				:		Optional[int]				   =			hop_length
									UpperCamelCase				:		Tuple				   =			max_length_s
									UpperCamelCase				:		Optional[Any]				   =			max_length_s * sampling_rate
									UpperCamelCase				:		List[str]				   =			sampling_rate
									UpperCamelCase				:		List[Any]				   =			frequency_min
									UpperCamelCase				:		Union[str, Any]				   =			frequency_max
									UpperCamelCase				:		Optional[int]				   =			mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins						,   num_mel_filters=_A						,   min_frequency=_A						,   max_frequency=_A						,   sampling_rate=_A						,   norm=_A						,   mel_scale="""htk"""						,   )
									UpperCamelCase				:		str				   =			mel_filter_bank(
									    num_frequency_bins=self.nb_frequency_bins						,   num_mel_filters=_A						,   min_frequency=_A						,   max_frequency=_A						,   sampling_rate=_A						,   norm="""slaney"""						,   mel_scale="""slaney"""						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			copy.deepcopy(self.__dict__    )
									UpperCamelCase				:		Dict				   =			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						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			spectrogram(
									    _A						,   window_function(self.fft_window_size						,   """hann"""    )						,   frame_length=self.fft_window_size						,   hop_length=self.hop_length						,   power=2.0						,   mel_filters=_A						,   log_mel="""dB"""						,   )
									return log_mel_spectrogram.T







			def   _a      (      self						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







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

									UpperCamelCase				:		str				   =			mel[idx_front : idx_front + chunk_frames, :]
									UpperCamelCase				:		List[Any]				   =			mel[idx_middle : idx_middle + chunk_frames, :]
									UpperCamelCase				:		int				   =			mel[idx_back : idx_back + chunk_frames, :]

									UpperCamelCase				:		int				   =			torch.tensor(mel[None, None, :]    )
									UpperCamelCase				:		Union[str, Any]				   =			torch.nn.functional.interpolate(
									    _A						,   size=[chunk_frames, 6_4]						,   mode="""bilinear"""						,   align_corners=_A    )
									UpperCamelCase				:		List[Any]				   =			mel_shrink[0][0].numpy()
									UpperCamelCase				:		Optional[int]				   =			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,   axis=0    )
									return mel_fusion







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







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

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

															if truncation == "fusion":
																					UpperCamelCase				:		List[Any]				   =			self._np_extract_fbank_features(_A						,   self.mel_filters    )
																					UpperCamelCase				:		Any				   =			np.stack([input_mel, input_mel, input_mel, input_mel]						,   axis=0    )
															else:
																					UpperCamelCase				:		Union[str, Any]				   =			self._np_extract_fbank_features(_A						,   self.mel_filters_slaney    )[None, :]

									return input_mel, longer







			def __call__(      self						,   _A						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			truncation if truncation is not None else self.truncation
									UpperCamelCase				:		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."""    )

									UpperCamelCase				:		int				   =			isinstance(_A						,   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}"""    )
									UpperCamelCase				:		int				   =			is_batched_numpy or (
									    isinstance(_A						,   (list, tuple)    ) and (isinstance(raw_speech[0]						,   (np.ndarray, tuple, list)    ))
									)

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

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

									# convert to mel spectrogram, truncate and pad if needed.
									UpperCamelCase				:		List[str]				   =			[
									    self._get_input_mel(_A						,   max_length if max_length else self.nb_max_samples						,   _A						,   _A    )
									    for waveform in raw_speech
									]

									UpperCamelCase				:		Union[str, Any]				   =			[]
									UpperCamelCase				:		Optional[int]				   =			[]
									for mel, longer in padded_inputs:
															input_mel.append(_A    )
															is_longer.append(_A    )

									if truncation == "fusion" and sum(_A    ) == 0:
															# if no audio is longer than 10s, then randomly select one audio to be longer
															UpperCamelCase				:		int				   =			np.random.randint(0						,   len(_A    )    )
															UpperCamelCase				:		List[str]				   =			True

									if isinstance(input_mel[0]						,   _A    ):
															UpperCamelCase				:		Dict				   =			[np.asarray(_A						,   dtype=np.floataa    ) for feature in input_mel]

									# is_longer is a list of bool
									UpperCamelCase				:		Optional[int]				   =			[[longer] for longer in is_longer]

									UpperCamelCase				:		Union[str, Any]				   =			{"""input_features""": input_mel, """is_longer""": is_longer}
									UpperCamelCase				:		List[Any]				   =			BatchFeature(_A    )

									if return_tensors is not None:
															UpperCamelCase				:		str				   =			input_features.convert_to_tensors(_A    )

									return input_features









def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			list:





				"""simple docstring"""


				if n_term == "":
								return []
				__lowerCamelCase									=       []
				for temp in range(int(UpperCamelCase__ ) ):
								series.append(F"""1/{temp + 1}""" if series else '1' )
				return series


if __name__ == "__main__":
					__A								=       input("Enter the last number (nth term) of the Harmonic Series")
					print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
					print(harmonic_series(nth_term))


