
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


lowercase  : Any						   =							logging.get_logger(__name__)

class __snake_case		(				__lowercase					):
    _a		:						Any=    ['''input_features''']
    def __init__(  self		,snake_case=80		,snake_case=16000		,snake_case=160		,snake_case=30		,snake_case=400		,snake_case=0.0		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               feature_size=UpperCAmelCase__		,sampling_rate=UpperCAmelCase__		,padding_value=UpperCAmelCase__		,return_attention_mask=UpperCAmelCase__		,**UpperCAmelCase__		,)
           lowercase							:					str	     =				n_fft
           lowercase							:					Dict	     =				hop_length
           lowercase							:					Tuple	     =				chunk_length
           lowercase							:					Optional[Any]	     =				chunk_length * sampling_rate
           lowercase							:					Dict	     =				self.n_samples // hop_length
           lowercase							:					Any	     =				sampling_rate
           lowercase							:					List[Any]	     =				mel_filter_bank(
               num_frequency_bins=1 + n_fft // 2		,num_mel_filters=UpperCAmelCase__		,min_frequency=0.0		,max_frequency=8000.0		,sampling_rate=UpperCAmelCase__		,norm="""slaney"""		,mel_scale="""slaney"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				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"""		,)
           lowercase							:					List[Any]	     =				log_spec[:, :-1]
           lowercase							:					Any	     =				np.maximum(UpperCAmelCase__		,log_spec.max() - 8.0				)
           lowercase							:					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 				_SCREAMING_SNAKE_CASE	(  snake_case		,snake_case		,snake_case = 0.0				):



           '''simple docstring'''







           if attention_mask is not None:
                  lowercase							:					int	     =				np.array(UpperCAmelCase__		,np.intaa				)
                  lowercase							:					Union[str, Any]	     =				[]

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

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

           return normed_input_values
    def __call__(  self		,snake_case		,snake_case = True		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = "max_length"		,snake_case = None		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''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.__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."""				)

           lowercase							:					str	     =				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}"				)
           lowercase							:					Optional[Any]	     =				is_batched_numpy or (
               isinstance(UpperCAmelCase__		,(list, tuple)				) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)				))
           )

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

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

           lowercase							:					int	     =				BatchFeature({"""input_features""": raw_speech}				)

           # convert into correct format for padding

           lowercase							:					int	     =				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:
                  lowercase							:					Union[str, Any]	     =				self.zero_mean_unit_var_norm(
                      padded_inputs["""input_features"""]		,attention_mask=padded_inputs["""attention_mask"""]		,padding_value=self.padding_value		,)
                  lowercase							:					Optional[Any]	     =				np.stack(padded_inputs["""input_features"""]		,axis=0				)

           # make sure list is in array format
           lowercase							:					List[Any]	     =				padded_inputs.get("""input_features"""				).transpose(2		,0		,1				)

           lowercase							:					str	     =				[self._np_extract_fbank_features(UpperCAmelCase__				) for waveform in input_features[0]]

           if isinstance(input_features[0]		,UpperCAmelCase__				):
                  lowercase							:					Dict	     =				[np.asarray(UpperCAmelCase__		,dtype=np.floataa				) for feature in input_features]
           else:
                  lowercase							:					str	     =				input_features

           if return_attention_mask:
                  # rescale from sample (48000) to feature (3000)
                  lowercase							:					List[Any]	     =				padded_inputs["""attention_mask"""][:, :: self.hop_length]

           if return_tensors is not None:
                  lowercase							:					str	     =				padded_inputs.convert_to_tensors(UpperCAmelCase__				)

           return padded_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				copy.deepcopy(self.__dict__				)
           lowercase							:					str	     =				self.__class__.__name__
           if "mel_filters" in output:
                  del output["mel_filters"]
           return output


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     lowercase							:					str	     =				mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     lowercase							:					Optional[int]	     =				max(
                         mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     j - wt[i - 1]    ) + val[i - 1] ,     )
              lowercase							:					List[Any]	     =				val
       return f[i][j]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Optional[int]	     =				[[0] * (w + 1) for _ in range(n + 1    )]

       for i in range(1 ,     n + 1    ):
              for w_ in range(1 ,     w + 1    ):
                     if wt[i - 1] <= w_:
                            lowercase							:					Union[str, Any]	     =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,     dp[i - 1][w_]    )
                     else:
                            lowercase							:					Any	     =				dp[i - 1][w_]

       return dp[n][w_], dp







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       if not (isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    ) and isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    )):
              raise ValueError(
                  """Both the weights and values vectors must be either lists or tuples"""    )

       lowercase							:					Optional[Any]	     =				len(SCREAMING_SNAKE_CASE__    )
       if num_items != len(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Optional[Any]	     =				(
                  """The number of weights must be the same as the number of values.\n"""
                  f"But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__    )} values"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              if not isinstance(wt[i] ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Tuple	     =				(
                         """All weights must be integers but got weight of """
                         f"type {type(wt[i]    )} at index {i}"
                     )
                     raise TypeError(SCREAMING_SNAKE_CASE__    )

       lowercase     ,      lowercase							:					Any	     =				knapsack(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					set	     =				set()
       _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       return optimal_val, example_optional_set







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     optimal_set.add(SCREAMING_SNAKE_CASE__    )
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     j - wt[i - 1] ,     SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Dict						   =							[3, 2, 4, 4]
  lowercase  : List[Any]						   =							[4, 3, 2, 3]
  lowercase  : Tuple						   =							4
  lowercase  : Tuple						   =							6
  lowercase  : Tuple						   =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
  lowercase   ,  lowercase  : List[str]						   =							knapsack(w, wt, val, n)
  print(optimal_solution)
  print(mf_knapsack(n, wt, val, w))  # switched the n and w

  # testing the dynamic programming problem with example
  # the optimal subset for the above example are items 3 and 4
  lowercase   ,  lowercase  : Optional[int]						   =							knapsack_with_example_solution(w, wt, val)
  assert optimal_solution == 8
  assert optimal_subset == {3, 4}
  print("""optimal_value = """, optimal_solution)
  print("""An optimal subset corresponding to the optimal value""", optimal_subset)
