



'''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


UpperCamelCase__       :	List[Any]    = logging.get_logger(__name__)






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		str			    =		['''input_features''', '''is_longer''']

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





					"""simple docstring"""






					super().__init__(
					    feature_size=lowerCAmelCase__							,     sampling_rate=lowerCAmelCase__							,     padding_value=lowerCAmelCase__							,     return_attention_mask=lowerCAmelCase__							,     **lowerCAmelCase__							,     )
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		top_db
					__SCREAMING_SNAKE_CASE       :		Dict           =		truncation
					__SCREAMING_SNAKE_CASE       :		Tuple           =		padding
					__SCREAMING_SNAKE_CASE       :		Dict           =		fft_window_size
					__SCREAMING_SNAKE_CASE       :		Any           =		(fft_window_size >> 1) + 1
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		hop_length
					__SCREAMING_SNAKE_CASE       :		Any           =		max_length_s
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		max_length_s * sampling_rate
					__SCREAMING_SNAKE_CASE       :		Dict           =		sampling_rate
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		frequency_min
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		frequency_max
					__SCREAMING_SNAKE_CASE       :		List[str]           =		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"""							,     )
					__SCREAMING_SNAKE_CASE       :		List[str]           =		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 UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		copy.deepcopy(self.__dict__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		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 UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : np.array							,     lowerCAmelCase__     : Optional[np.array] = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		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 UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE       :		str           =		mel[idx_front : idx_front + chunk_frames, :]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		mel[idx_middle : idx_middle + chunk_frames, :]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		mel[idx_back : idx_back + chunk_frames, :]

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

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : np.array							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






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

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

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

					return input_mel, longer



			def __call__(							self     : Union[str, 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__     : Union[str, Any]							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		truncation if truncation is not None else self.truncation
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		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."""						)

					__SCREAMING_SNAKE_CASE       :		Dict           =		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}"						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		is_batched_numpy or (
					    isinstance(lowerCAmelCase__							,     (list, tuple)						) and (isinstance(raw_speech[0]							,     (np.ndarray, tuple, list)						))
					)

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

					# always return batch
					if not is_batched:
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[np.asarray(lowerCAmelCase__						)]

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

					__SCREAMING_SNAKE_CASE       :		int           =		[]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[]
					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
							__SCREAMING_SNAKE_CASE       :		List[str]           =		np.random.randint(0							,     len(lowerCAmelCase__						)						)
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		True

					if isinstance(input_mel[0]							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		[np.asarray(lowerCAmelCase__							,     dtype=np.floataa						) for feature in input_mel]

					# is_longer is a list of bool
					__SCREAMING_SNAKE_CASE       :		Tuple           =		[[longer] for longer in is_longer]

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		{"""input_features""": input_mel, """is_longer""": is_longer}
					__SCREAMING_SNAKE_CASE       :		int           =		BatchFeature(lowerCAmelCase__						)

					if return_tensors is not None:
							__SCREAMING_SNAKE_CASE       :		Dict           =		input_features.convert_to_tensors(lowerCAmelCase__						)

					return input_features




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')