



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

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

														_lowerCAmelCase     :							Tuple    =    optimal_fft_length(self.sample_size)
														_lowerCAmelCase     :							Tuple    =    (self.n_fft // 2) + 1







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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

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

														if input_length < x.shape[0]:
																					_lowerCAmelCase     :							Union[str, Any]    =    padding_value

														# make sure array is in float32
														_lowerCAmelCase     :							Optional[int]    =    x.astype(np.floataa)

														return x







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

														'''simple docstring'''

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

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

														# always return batch
														if not is_batched:
																					_lowerCAmelCase     :							Optional[Any]    =    [raw_speech]

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

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

														_lowerCAmelCase     :							str    =    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     :							Optional[Any]    =    padded_inputs.get("input_features")
														if isinstance(input_features[0],			__a):
																					_lowerCAmelCase     :							int    =    [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     :							Optional[Any]    =    [np.asarray(__a,			dtype=np.intaa) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					_lowerCAmelCase     :							int    =    (
																					    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     :							List[Any]    =    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




"""simple docstring"""







from __future__ import annotations

import math


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							size
         # approximate the overall size of segment tree with given value
         lowerCAmelCase     :     Optional[int]								=							[0 for i in range(0		,     4 * size						)]
         # create array to store lazy update
         lowerCAmelCase     :     List[str]								=							[0 for i in range(0		,     4 * size						)]
         lowerCAmelCase     :     Dict								=							[0 for i in range(0		,     4 * size						)]  # flag for lazy update

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         return idx * 2 + 1

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if left_element == right_element:
           lowerCAmelCase     :     List[str]								=							a[left_element - 1]
         else:
           lowerCAmelCase     :     Tuple								=							(left_element + right_element) // 2
           self.build(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__						)
           self.build(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__						)
           lowerCAmelCase     :     Tuple								=							max(
               self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     Optional[int]								=							self.lazy[idx]
           lowerCAmelCase     :     List[str]								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							True
             lowerCAmelCase     :     Optional[Any]								=							True

         if right_element < a or left_element > b:
           return True
         if left_element >= a and right_element <= b:
           lowerCAmelCase     :     str								=							val
           if left_element != right_element:
             lowerCAmelCase     :     Optional[Any]								=							val
             lowerCAmelCase     :     Union[str, Any]								=							val
             lowerCAmelCase     :     int								=							True
             lowerCAmelCase     :     int								=							True
           return True
         lowerCAmelCase     :     List[str]								=							(left_element + right_element) // 2
         self.update(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         self.update(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							max(
             self.segment_tree[self.left(snake_case__						)]		,     self.segment_tree[self.right(snake_case__						)]						)
         return True

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.flag[idx] is True:
           lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
           lowerCAmelCase     :     str								=							False
           if left_element != right_element:
             lowerCAmelCase     :     Tuple								=							self.lazy[idx]
             lowerCAmelCase     :     List[Any]								=							self.lazy[idx]
             lowerCAmelCase     :     Optional[int]								=							True
             lowerCAmelCase     :     str								=							True
         if right_element < a or left_element > b:
           return -math.inf
         if left_element >= a and right_element <= b:
           return self.segment_tree[idx]
         lowerCAmelCase     :     Any								=							(left_element + right_element) // 2
         lowerCAmelCase     :     Optional[int]								=							self.query(self.left(snake_case__						)		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)
         lowerCAmelCase     :     Dict								=							self.query(self.right(snake_case__						)		,     mid + 1		,     snake_case__		,     snake_case__		,     snake_case__						)
         return max(snake_case__		,     snake_case__						)







       def __str__(						self						):






         """simple docstring"""





         return str([self.query(1		,     1		,     self.size		,     snake_case__		,     snake_case__						) for i in range(1		,     self.size + 1						)]						)


if __name__ == "__main__":
     lowerCAmelCase__      				=   [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
     lowerCAmelCase__      				=   15
     lowerCAmelCase__      				=   SegmentTree(size)
     segt.build(1, 1, size, A)
     print(segt.query(1, 1, size, 4, 6))
     print(segt.query(1, 1, size, 7, 11))
     print(segt.query(1, 1, size, 7, 12))
     segt.update(1, 1, size, 1, 3, 111)
     print(segt.query(1, 1, size, 1, 15))
     segt.update(1, 1, size, 7, 8, 235)
     print(segt)





