






"""simple docstring"""


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




class 						__UpperCamelCase     (     _snake_case						):
					lowercase_    :				Union[str, Any]      =  ["""input_features"""]
					def __init__(							self							:	int     , UpperCAmelCase							:	Any=80     , UpperCAmelCase							:	Any=1_6000     , UpperCAmelCase							:	Any=160     , UpperCAmelCase							:	str=30     , UpperCAmelCase							:	List[str]=400     , UpperCAmelCase							:	Union[str, Any]=0.0     , UpperCAmelCase							:	Optional[Any]=False     , **UpperCAmelCase							:	Union[str, Any]     , )	->       List[str]:
											super().__init__(
											    feature_size=UpperCamelCase__     , sampling_rate=UpperCamelCase__     , padding_value=UpperCamelCase__     , return_attention_mask=UpperCamelCase__     , **UpperCamelCase__     , )
											lowerCAmelCase   :Tuple						=			n_fft
											lowerCAmelCase   :Dict						=			hop_length
											lowerCAmelCase   :str						=			chunk_length
											lowerCAmelCase   :List[Any]						=			chunk_length * sampling_rate
											lowerCAmelCase   :List[str]						=			self.n_samples // hop_length
											lowerCAmelCase   :Any						=			sampling_rate
											lowerCAmelCase   :Dict						=			mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2     , num_mel_filters=UpperCamelCase__     , min_frequency=0.0     , max_frequency=8_0_0_0.0     , sampling_rate=UpperCamelCase__     , norm='slaney'     , mel_scale='slaney'     , )
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	str   )	->       np.ndarray:
											lowerCAmelCase   :List[Any]						=			spectrogram(
											    UpperCamelCase__     , window_function(self.n_fft     , 'hann'   )     , frame_length=self.n_fft     , hop_length=self.hop_length     , power=2.0     , mel_filters=self.mel_filters     , log_mel='log10'     , )
											lowerCAmelCase   :Union[str, Any]						=			log_spec[:, :-1]
											lowerCAmelCase   :Dict						=			np.maximum(UpperCamelCase__     , log_spec.max() - 8.0   )
											lowerCAmelCase   :Tuple						=			(log_spec + 4.0) / 4.0
											return log_spec
					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def        UpperCAmelCase__ (							UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	str = 0.0   )	->       List[np.ndarray]:
											if attention_mask is not None:
																	lowerCAmelCase   :str						=			np.array(UpperCamelCase__     , np.intaa   )
																	lowerCAmelCase   :Optional[int]						=			[]

																	for vector, length in zip(UpperCamelCase__     , attention_mask.sum(-1   )   ):
																							lowerCAmelCase   :Any						=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
																							if length < normed_slice.shape[0]:
																													lowerCAmelCase   :List[Any]						=			padding_value

																							normed_input_values.append(UpperCamelCase__   )
											else:
																	lowerCAmelCase   :Any						=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

											return normed_input_values
					def __call__(							self							:	List[Any]     , UpperCAmelCase							:	List[Any]     , UpperCAmelCase							:	str = True     , UpperCAmelCase							:	Optional[Any] = None     , UpperCAmelCase							:	List[str] = None     , UpperCAmelCase							:	Any = None     , UpperCAmelCase							:	Dict = "max_length"     , UpperCAmelCase							:	List[Any] = None     , UpperCAmelCase							:	Union[str, Any] = None     , UpperCAmelCase							:	Union[str, Any] = None     , **UpperCAmelCase							:	int     , )	->       BatchFeature:

											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.'   )

											lowerCAmelCase   :Union[str, Any]						=			isinstance(UpperCamelCase__     , 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   :Dict						=			is_batched_numpy or (
											    isinstance(UpperCamelCase__     , (list, tuple)   ) and (isinstance(raw_speech[0]     , (np.ndarray, tuple, list)   ))
											)

											if is_batched:
																	lowerCAmelCase   :int						=			[np.asarray([speech]     , dtype=np.floataa   ).T for speech in raw_speech]
											elif not is_batched and not isinstance(UpperCamelCase__     , np.ndarray   ):
																	lowerCAmelCase   :Tuple						=			np.asarray(UpperCamelCase__     , dtype=np.floataa   )
											elif isinstance(UpperCamelCase__     , np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
																	lowerCAmelCase   :Optional[Any]						=			raw_speech.astype(np.floataa   )

											# always return batch
											if not is_batched:
																	lowerCAmelCase   :Any						=			[np.asarray([raw_speech]   ).T]

											lowerCAmelCase   :List[str]						=			BatchFeature({'input_features': raw_speech}   )

											# convert into correct format for padding

											lowerCAmelCase   :Dict						=			self.pad(
											    UpperCamelCase__     , padding=UpperCamelCase__     , max_length=max_length if max_length else self.n_samples     , truncation=UpperCamelCase__     , pad_to_multiple_of=UpperCamelCase__     , return_attention_mask=return_attention_mask or do_normalize     , )

											# zero-mean and unit-variance normalization
											if do_normalize:
																	lowerCAmelCase   :List[str]						=			self.zero_mean_unit_var_norm(
																	    padded_inputs['input_features']     , attention_mask=padded_inputs['attention_mask']     , padding_value=self.padding_value     , )
																	lowerCAmelCase   :Any						=			np.stack(padded_inputs['input_features']     , axis=0   )

											# make sure list is in array format
											lowerCAmelCase   :Dict						=			padded_inputs.get('input_features'   ).transpose(2     , 0     , 1   )

											lowerCAmelCase   :Dict						=			[self._np_extract_fbank_features(UpperCamelCase__   ) for waveform in input_features[0]]

											if isinstance(input_features[0]     , UpperCamelCase__   ):
																	lowerCAmelCase   :Optional[int]						=			[np.asarray(UpperCamelCase__     , dtype=np.floataa   ) for feature in input_features]
											else:
																	lowerCAmelCase   :Union[str, Any]						=			input_features

											if return_attention_mask:
																	# rescale from sample (48000) to feature (3000)
																	lowerCAmelCase   :Tuple						=			padded_inputs['attention_mask'][:, :: self.hop_length]

											if return_tensors is not None:
																	lowerCAmelCase   :int						=			padded_inputs.convert_to_tensors(UpperCamelCase__   )

											return padded_inputs
					def        UpperCAmelCase__ (							self							:	Any   )	->       Dict[str, Any]:
											lowerCAmelCase   :Tuple						=			copy.deepcopy(self.__dict__   )
											lowerCAmelCase   :Union[str, Any]						=			self.__class__.__name__
											if "mel_filters" in output:
																	del output["mel_filters"]
											return output


'''simple docstring'''







from math import pow
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							)      ->      tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       A_						       =   int(pow(UpperCAmelCase__,							UpperCAmelCase__					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              A_       ,      A_						       =   backtrack(
                  UpperCAmelCase__,							UpperCAmelCase__,							current_number + 1,							UpperCAmelCase__,							UpperCAmelCase__					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              A_       ,      A_						       =   backtrack(
                  UpperCAmelCase__,							UpperCAmelCase__,							current_number + 1,							UpperCAmelCase__,							UpperCAmelCase__					)
       return current_sum, solutions_count
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  """Invalid input\n"""
                  """needed_sum must be between 1 and 1000, power between 2 and 10."""					)

       return backtrack(UpperCAmelCase__,							UpperCAmelCase__,							1,							0,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
      import doctest

      doctest.testmod()
