



from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	['input_features', 'attention_mask']







							def __init__(     self,    __a=80,    __a=1_6000,    __a=0.0,    __a=10,    __a=25,    __a="hamming_window",    __a=32_768.0,    __a=0.97,    __a=1.0,    __a=True,    __a=True,    __a=False,    **__a,    ):

														'''simple docstring'''

														super().__init__(feature_size=__a,    sampling_rate=__a,    padding_value=__a,    **__a)

														_lowerCAmelCase  :							List[Any]				     =					feature_size
														_lowerCAmelCase  :							Tuple				     =					sampling_rate
														_lowerCAmelCase  :							List[Any]				     =					padding_value
														_lowerCAmelCase  :							int				     =					hop_length
														_lowerCAmelCase  :							str				     =					win_length
														_lowerCAmelCase  :							Any				     =					frame_signal_scale
														_lowerCAmelCase  :							str				     =					preemphasis_coeff
														_lowerCAmelCase  :							Optional[int]				     =					mel_floor
														_lowerCAmelCase  :							Optional[Any]				     =					normalize_means
														_lowerCAmelCase  :							List[Any]				     =					normalize_vars
														_lowerCAmelCase  :							Union[str, Any]				     =					win_function
														_lowerCAmelCase  :							Dict				     =					return_attention_mask

														_lowerCAmelCase  :							Union[str, Any]				     =					win_length * sampling_rate // 1000
														_lowerCAmelCase  :							int				     =					hop_length * sampling_rate // 1000

														_lowerCAmelCase  :							Optional[int]				     =					optimal_fft_length(self.sample_size)
														_lowerCAmelCase  :							List[str]				     =					(self.n_fft // 2) + 1







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														if self.win_function == "hamming_window":
																					_lowerCAmelCase  :							Optional[int]				     =					window_function(window_length=self.sample_size,    name=self.win_function,    periodic=__a)
														else:
																					_lowerCAmelCase  :							List[Any]				     =					window_function(window_length=self.sample_size,    name=self.win_function)

														_lowerCAmelCase  :							Union[str, Any]				     =					mel_filter_bank(
														    num_frequency_bins=self.n_freqs,    num_mel_filters=self.feature_size,    min_frequency=0.0,    max_frequency=self.sampling_rate / 2.0,    sampling_rate=self.sampling_rate,    )

														_lowerCAmelCase  :							Union[str, Any]				     =					spectrogram(
														    one_waveform * self.frame_signal_scale,    window=__a,    frame_length=self.sample_size,    hop_length=self.sample_stride,    fft_length=self.n_fft,    center=__a,    preemphasis=self.preemphasis_coeff,    mel_filters=__a,    mel_floor=self.mel_floor,    log_mel="log",    )
														return msfc_features.T







							def      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														if self.normalize_means:
																					_lowerCAmelCase  :							Optional[Any]				     =					x[:input_length].mean(axis=0)
																					_lowerCAmelCase  :							List[Any]				     =					np.subtract(__a,    __a)
														if self.normalize_vars:
																					_lowerCAmelCase  :							Optional[int]				     =					x[:input_length].std(axis=0)
																					_lowerCAmelCase  :							int				     =					np.divide(__a,    __a)

														if input_length < x.shape[0]:
																					_lowerCAmelCase  :							List[Any]				     =					padding_value

														# make sure array is in float32
														_lowerCAmelCase  :							Dict				     =					x.astype(np.floataa)

														return x







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(__a,    __a,    self.padding_value) for x, n in zip(__a,    __a)]







							def __call__(     self,    __a,    __a = False,    __a = None,    __a = False,    __a = None,    __a = None,    __a = None,    __a = None,    **__a,    ):

														'''simple docstring'''

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																												    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
																												    f" {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.")

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

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

														# always return batch
														if not is_batched:
																					_lowerCAmelCase  :							Union[str, Any]				     =					[raw_speech]

														# extract fbank features
														_lowerCAmelCase  :							Optional[int]				     =					[self._extract_mfsc_features(__a) for one_waveform in raw_speech]

														# convert into correct format for padding
														_lowerCAmelCase  :							Any				     =					BatchFeature({"input_features": features})

														_lowerCAmelCase  :							Dict				     =					self.pad(
														    __a,    padding=__a,    max_length=__a,    truncation=__a,    pad_to_multiple_of=__a,    return_attention_mask=__a,    **__a,    )
														# make sure list is in array format
														_lowerCAmelCase  :							Any				     =					padded_inputs.get("input_features")
														if isinstance(input_features[0],    __a):
																					_lowerCAmelCase  :							List[Any]				     =					[np.asarray(__a,    dtype=np.floataa) for feature in input_features]

														_lowerCAmelCase  :							str				     =					padded_inputs.get("attention_mask")
														if attention_mask is not None:
																					_lowerCAmelCase  :							Dict				     =					[np.asarray(__a,    dtype=np.intaa) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					_lowerCAmelCase  :							List[Any]				     =					(
																					    np.array(__a,    dtype=np.intaa)
																					    if self._get_padding_strategies(__a,    max_length=__a) is not PaddingStrategy.DO_NOT_PAD
																					    and padding
																					    else None
																					)
																					_lowerCAmelCase  :							str				     =					self.normalize(
																					    padded_inputs["input_features"],    attention_mask=__a)

														if return_tensors is not None:
																					_lowerCAmelCase  :							Tuple				     =					padded_inputs.convert_to_tensors(__a)

														return padded_inputs





def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations(_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item						) for item in array						)

							return count_of_possible_combinations(_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations_with_dp_array(
							    _lowerCamelCase       ,				_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														_lowerCAmelCase  :							Optional[int]				     =					sum(
														    count_of_possible_combinations_with_dp_array(target - item       ,				_lowerCamelCase						)
														    for item in array						)
														_lowerCAmelCase  :							Any				     =					answer
														return answer

							_lowerCAmelCase  :							List[Any]				     =					[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_lowerCamelCase       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					[0] * (target + 1)
							_lowerCAmelCase  :							List[str]				     =					1

							for i in range(1       ,				target + 1						):
														for j in range(_lowerCamelCase						):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						_snake_case	=						3
						_snake_case	=						5
						_snake_case	=						[1, 2, 5]
						print(combination_sum_iv(n, array, target))
