"""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


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

class 	UpperCamelCase    (					__SCREAMING_SNAKE_CASE		):
				UpperCAmelCase       :   Any					=	['input_features', 'is_longer']



				def __init__(self       :   List[str]			,						_A       :   List[str]=64			,						_A       :   Any=4_80_00			,						_A       :   List[str]=4_80			,						_A       :   Tuple=10			,						_A       :   Optional[int]=10_24			,						_A       :   Optional[int]=0.0			,						_A       :   Union[str, Any]=False			,						_A       :   Tuple = 0			,						_A       :   Union[str, Any] = 1_40_00			,						_A       :   List[str] = None			,						_A       :   List[Any] = "fusion"			,						_A       :   Optional[int] = "repeatpad"			,						**_A       :   Tuple			,						)					->			List[Any]:
					super().__init__(
					    feature_size=_a			,						sampling_rate=_a			,						padding_value=_a			,						return_attention_mask=_a			,						**_a			,						)
					__snake_case    :							Optional[int]   	=				top_db
					__snake_case    :							Any   	=				truncation
					__snake_case    :							str   	=				padding
					__snake_case    :							Union[str, Any]   	=				fft_window_size
					__snake_case    :							Tuple   	=				(fft_window_size >> 1) + 1
					__snake_case    :							Optional[int]   	=				hop_length
					__snake_case    :							Optional[int]   	=				max_length_s
					__snake_case    :							Dict   	=				max_length_s * sampling_rate
					__snake_case    :							List[str]   	=				sampling_rate
					__snake_case    :							Optional[Any]   	=				frequency_min
					__snake_case    :							Any   	=				frequency_max
					__snake_case    :							Any   	=				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'			,						)
					__snake_case    :							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='slaney'			,						mel_scale='slaney'			,						)



				def       _lowercase  (self       :   Tuple)					->			Dict:
					__snake_case    :							Tuple   	=				copy.deepcopy(self.__dict__)
					__snake_case    :							Tuple   	=				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       _lowercase  (self       :   Dict			,						_A       :   str			,						_A       :   int = None)					->			Optional[Any]:
					__snake_case    :							str   	=				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       _lowercase  (self       :   List[str]			,						_A       :   Optional[int]			,						_A       :   List[Any]			,						_A       :   int)					->			Union[str, Any]:
					__snake_case    :							Optional[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
						__snake_case    :							str   	=				[0]
					if len(ranges[2]) == 0:
						# if the audio is too short, we just use the first chunk
						__snake_case    :							Tuple   	=				[0]
					# randomly choose index for each part
					__snake_case    :							Optional[Any]   	=				np.random.choice(ranges[0])
					__snake_case    :							List[str]   	=				np.random.choice(ranges[1])
					__snake_case    :							List[Any]   	=				np.random.choice(ranges[2])

					__snake_case    :							Any   	=				mel[idx_front : idx_front + chunk_frames, :]
					__snake_case    :							Any   	=				mel[idx_middle : idx_middle + chunk_frames, :]
					__snake_case    :							int   	=				mel[idx_back : idx_back + chunk_frames, :]

					__snake_case    :							List[str]   	=				torch.tensor(mel[None, None, :])
					__snake_case    :							int   	=				torch.nn.functional.interpolate(
					    _a			,						size=[chunk_frames, 64]			,						mode='bilinear'			,						align_corners=_a)
					__snake_case    :							Optional[int]   	=				mel_shrink[0][0].numpy()
					__snake_case    :							Optional[int]   	=				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,						axis=0)
					return mel_fusion



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

					else:
						__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":
								__snake_case    :							Dict   	=				int(max_length / len(_a))
								__snake_case    :							Any   	=				np.stack(np.tile(_a			,						n_repeat + 1))[:max_length]
							if padding == "repeatpad":
								__snake_case    :							Optional[Any]   	=				int(max_length / len(_a))
								__snake_case    :							List[str]   	=				np.stack(np.tile(_a			,						_a))
							__snake_case    :							Optional[Any]   	=				np.pad(_a			,						(0, max_length - waveform.shape[0])			,						mode='constant'			,						constant_values=0)

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

					return input_mel, longer






				def __call__(self       :   Optional[int]			,						_A       :   List[str]			,						_A       :   List[Any] = None			,						_A       :   Optional[int] = None			,						_A       :   Optional[int] = None			,						_A       :   Dict = None			,						_A       :   Dict = None			,						**_A       :   List[str]			,						)					->			List[str]:
					__snake_case    :							Tuple   	=				truncation if truncation is not None else self.truncation
					__snake_case    :							Dict   	=				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.')

					__snake_case    :							List[Any]   	=				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}")
					__snake_case    :							Tuple   	=				is_batched_numpy or (
					    isinstance(_a			,						(list, tuple)) and (isinstance(raw_speech[0]			,						(np.ndarray, tuple, list)))
					)

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

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

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

					__snake_case    :							List[Any]   	=				[]
					__snake_case    :							Tuple   	=				[]
					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
						__snake_case    :							str   	=				np.random.randint(0			,						len(_a))
						__snake_case    :							str   	=				True

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

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

					__snake_case    :							int   	=				{'input_features': input_mel, 'is_longer': is_longer}
					__snake_case    :							Any   	=				BatchFeature(_a)

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

					return input_features






"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list[int]    ,							UpperCAmelCase_					:       str						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				int(UpperCAmelCase_						)

	# Initialize Result
	__snake_case    :							int   	=				[]

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

	return answer


# Driver Code
if __name__ == "__main__":
	_a  :  Optional[int]=				[]
	_a  :  Optional[int]=				"0"

	if (
	    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
	    == "y"
	):
		_a  :  int=				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()))
		_a  :  Optional[int]=				input("Enter the change you want to make in Indian Currency: ").strip()
	else:
		# All denominations of Indian Currency if user does not enter
		_a  :  Tuple=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
		_a  :  List[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}: ''')
		_a  :  List[Any]=				find_minimum_change(denominations, value)
		# Print result
		for i in range(len(answer)):
			print(answer[i], end=" ")





