





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







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							["""input_features"""]




 def __init__(					self       :    Union[str, Any]	,			snake_case_       :    List[Any]=8_0	,			snake_case_       :    Any=1_6_0_0_0	,			snake_case_       :    Union[str, Any]=1_6_0	,			snake_case_       :    Optional[int]=3_0	,			snake_case_       :    str=4_0_0	,			snake_case_       :    Optional[Any]=0.0	,			snake_case_       :    int=False	,			**snake_case_       :    Optional[int]	,			):
      super().__init__(
          feature_size=snake_case_	,			sampling_rate=snake_case_	,			padding_value=snake_case_	,			return_attention_mask=snake_case_	,			**snake_case_	,			)
      _UpperCAmelCase     							=   n_fft
      _UpperCAmelCase     							=   hop_length
      _UpperCAmelCase     							=   chunk_length
      _UpperCAmelCase     							=   chunk_length * sampling_rate
      _UpperCAmelCase     							=   self.n_samples // hop_length
      _UpperCAmelCase     							=   sampling_rate
      _UpperCAmelCase     							=   mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2	,			num_mel_filters=snake_case_	,			min_frequency=0.0	,			max_frequency=8_0_0_0.0	,			sampling_rate=snake_case_	,			norm="slaney"	,			mel_scale="slaney"	,			)




 def      lowercase	(					self       :    Dict	,			snake_case_       :    np.array    ):
      _UpperCAmelCase     							=   spectrogram(
          snake_case_	,			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"	,			)
      _UpperCAmelCase     							=   log_spec[:, :-1]
      _UpperCAmelCase     							=   np.maximum(snake_case_	,			log_spec.max() - 8.0    )
      _UpperCAmelCase     							=   (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      lowercase	(					snake_case_       :    List[np.ndarray]	,			snake_case_       :    List[np.ndarray]	,			snake_case_       :    float = 0.0    ):
      if attention_mask is not None:
           _UpperCAmelCase     							=   np.array(snake_case_	,			np.intaa    )
           _UpperCAmelCase     							=   []

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

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

      return normed_input_values




 def __call__(					self       :    Optional[int]	,			snake_case_       :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			snake_case_       :    bool = True	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[Union[str, TensorType]] = None	,			snake_case_       :    Optional[bool] = None	,			snake_case_       :    Optional[str] = "max_length"	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[bool] = None	,			**snake_case_       :    Union[str, Any]	,			):

      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."    )

      _UpperCAmelCase     							=   isinstance(snake_case_	,			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}'    )
      _UpperCAmelCase     							=   is_batched_numpy or (
          isinstance(snake_case_	,			(list, tuple)    ) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)    ))
      )

      if is_batched:
           _UpperCAmelCase     							=   [np.asarray([speech]	,			dtype=np.floataa    ).T for speech in raw_speech]
      elif not is_batched and not isinstance(snake_case_	,			np.ndarray    ):
           _UpperCAmelCase     							=   np.asarray(snake_case_	,			dtype=np.floataa    )
      elif isinstance(snake_case_	,			np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
           _UpperCAmelCase     							=   raw_speech.astype(np.floataa    )

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

      _UpperCAmelCase     							=   BatchFeature({"input_features": raw_speech}    )

      # convert into correct format for padding

      _UpperCAmelCase     							=   self.pad(
          snake_case_	,			padding=snake_case_	,			max_length=max_length if max_length else self.n_samples	,			truncation=snake_case_	,			pad_to_multiple_of=snake_case_	,			return_attention_mask=return_attention_mask or do_normalize	,			)

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

      # make sure list is in array format
      _UpperCAmelCase     							=   padded_inputs.get("input_features"    ).transpose(2	,			0	,			1    )

      _UpperCAmelCase     							=   [self._np_extract_fbank_features(snake_case_    ) for waveform in input_features[0]]

      if isinstance(input_features[0]	,			snake_case_    ):
           _UpperCAmelCase     							=   [np.asarray(snake_case_	,			dtype=np.floataa    ) for feature in input_features]
      else:
           _UpperCAmelCase     							=   input_features

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

      if return_tensors is not None:
           _UpperCAmelCase     							=   padded_inputs.convert_to_tensors(snake_case_    )

      return padded_inputs




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











'''simple docstring'''




from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




 def      lowercase	(					self       :    List[Any]    ):
      for p in range(self.N - 1	,			0	,			-1    ):
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


if __name__ == "__main__":
  from functools import reduce

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

  for index, value in test_updates.items():
    __SCREAMING_SNAKE_CASE     :str       =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()




