





'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case_  :							Optional[int]            =	logging.get_logger(__name__)


class        __a   (lowerCamelCase  ):
							__a       : List[str]          =							["input_values", "attention_mask"]

							def __init__(				self				:  List[str]							,  __magic_name__				:  str=1							,  __magic_name__				:  List[str]=1_60_00							,  __magic_name__				:  Any=1_28							,  __magic_name__				:  List[Any]=10_24							,  __magic_name__				:  Optional[Any]=0.0							,  __magic_name__				:  Optional[Any]=True							,  __magic_name__				:  Optional[int]=-4.2_6_7_7_3_9_3							,  __magic_name__				:  List[Any]=4.5_6_8_9_9_7_4							,  __magic_name__				:  Dict=False							,  **__magic_name__				:  Tuple							,  )       ->							Optional[Any]:







													"""simple docstring"""
													super().__init__(feature_size=__magic_name__							,  sampling_rate=__magic_name__							,  padding_value=__magic_name__							,  **__magic_name__ )
													UpperCAmelCase_      :   Tuple      =	num_mel_bins
													UpperCAmelCase_      :   Union[str, Any]      =	max_length
													UpperCAmelCase_      :   Dict      =	do_normalize
													UpperCAmelCase_      :   int      =	mean
													UpperCAmelCase_      :   int      =	std
													UpperCAmelCase_      :   int      =	return_attention_mask

							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  int							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	torch.from_numpy(__magic_name__ ).unsqueeze(0 )
													UpperCAmelCase_      :   Dict      =	ta_kaldi.fbank(
													    __magic_name__							,  htk_compat=__magic_name__							,  sample_frequency=self.sampling_rate							,  use_energy=__magic_name__							,  window_type='''hanning'''							,  num_mel_bins=self.num_mel_bins							,  dither=0.0							,  frame_shift=10							,  )

													UpperCAmelCase_      :   int      =	fbank.shape[0]
													UpperCAmelCase_      :   int      =	max_length - n_frames

													# pad or truncate, depending on difference
													if difference > 0:
																			UpperCAmelCase_      :   Any      =	torch.nn.ZeroPadad((0, 0, 0, difference) )
																			UpperCAmelCase_      :   Optional[Any]      =	pad_module(__magic_name__ )
													elif difference < 0:
																			UpperCAmelCase_      :   List[str]      =	fbank[0:max_length, :]

													UpperCAmelCase_      :   Dict      =	fbank.numpy()

													return fbank

							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  np.ndarray )       ->							np.ndarray:







													"""simple docstring"""
													return (input_values - (self.mean)) / (self.std * 2)



							def __call__(				self				:  Dict							,  __magic_name__				:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,  __magic_name__				:  Optional[int] = None							,  __magic_name__				:  Optional[Union[str, TensorType]] = None							,  **__magic_name__				:  Union[str, Any]							,  )       ->							BatchFeature:







													"""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.''' )

													UpperCAmelCase_      :   Tuple      =	isinstance(__magic_name__							,  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_      :   Dict      =	is_batched_numpy or (
													    isinstance(__magic_name__							,  (list, tuple) ) and (isinstance(raw_speech[0]							,  (np.ndarray, tuple, list) ))
													)

													if is_batched:
																			UpperCAmelCase_      :   int      =	[np.asarray(__magic_name__							,  dtype=np.floataa ) for speech in raw_speech]
													elif not is_batched and not isinstance(__magic_name__							,  np.ndarray ):
																			UpperCAmelCase_      :   Optional[int]      =	np.asarray(__magic_name__							,  dtype=np.floataa )
													elif isinstance(__magic_name__							,  np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
																			UpperCAmelCase_      :   int      =	raw_speech.astype(np.floataa )

													# always return batch
													if not is_batched:
																			UpperCAmelCase_      :   Dict      =	[raw_speech]

													# extract fbank features and pad/truncate to max_length
													UpperCAmelCase_      :   int      =	[self._extract_fbank_features(__magic_name__							,  max_length=self.max_length ) for waveform in raw_speech]

													# convert into BatchFeature
													UpperCAmelCase_      :   Tuple      =	BatchFeature({'''input_values''': features} )

													# make sure list is in array format
													UpperCAmelCase_      :   int      =	padded_inputs.get('''input_values''' )
													if isinstance(input_values[0]							,  __magic_name__ ):
																			UpperCAmelCase_      :   Any      =	[np.asarray(__magic_name__							,  dtype=np.floataa ) for feature in input_values]

													# normalization
													if self.do_normalize:
																			UpperCAmelCase_      :   Tuple      =	[self.normalize(__magic_name__ ) for feature in input_values]

													if return_tensors is not None:
																			UpperCAmelCase_      :   Any      =	padded_inputs.convert_to_tensors(__magic_name__ )

													return padded_inputs










def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[int]:






      '''simple docstring'''







      if hue < 0 or hue > 3_6_0:
            raise Exception("hue should be between 0 and 360"     )

      if saturation < 0 or saturation > 1:
            raise Exception("saturation should be between 0 and 1"     )

      if value < 0 or value > 1:
            raise Exception("value should be between 0 and 1"     )

      UpperCAmelCase_	:						Tuple					   =		value * saturation
      UpperCAmelCase_	:						Union[str, Any]					   =		hue / 6_0
      UpperCAmelCase_	:						Tuple					   =		chroma * (1 - abs(hue_section % 2 - 1     ))
      UpperCAmelCase_	:						Tuple					   =		value - chroma

      if hue_section >= 0 and hue_section <= 1:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 1 and hue_section <= 2:
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 2 and hue_section <= 3:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )
      elif hue_section > 3 and hue_section <= 4:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Optional[Any]					   =		round(2_5_5 * (chroma + match_value)     )
      elif hue_section > 4 and hue_section <= 5:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (chroma + match_value)     )
      else:
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )

      return [red, green, blue]







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[float]:






      '''simple docstring'''







      if red < 0 or red > 2_5_5:
            raise Exception("red should be between 0 and 255"     )

      if green < 0 or green > 2_5_5:
            raise Exception("green should be between 0 and 255"     )

      if blue < 0 or blue > 2_5_5:
            raise Exception("blue should be between 0 and 255"     )

      UpperCAmelCase_	:						str					   =		red / 2_5_5
      UpperCAmelCase_	:						Dict					   =		green / 2_5_5
      UpperCAmelCase_	:						int					   =		blue / 2_5_5
      UpperCAmelCase_	:						Any					   =		max(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Union[str, Any]					   =		value - min(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Optional[Any]					   =		0 if value == 0 else chroma / value

      if chroma == 0:
            UpperCAmelCase_	:						Tuple					   =		0.0
      elif value == float_red:
            UpperCAmelCase_	:						List[str]					   =		6_0 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
            UpperCAmelCase_	:						Union[str, Any]					   =		6_0 * (2 + (float_blue - float_red) / chroma)
      else:
            UpperCAmelCase_	:						Dict					   =		6_0 * (4 + (float_red - float_green) / chroma)

      UpperCAmelCase_	:						int					   =		(hue + 3_6_0) % 3_6_0

      return [hue, saturation, value]







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  bool:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		abs(hsv_a[0] - hsv_a[0]     ) < 0.2
      UpperCAmelCase_	:						Optional[Any]					   =		abs(hsv_a[1] - hsv_a[1]     ) < 0.002
      UpperCAmelCase_	:						Tuple					   =		abs(hsv_a[2] - hsv_a[2]     ) < 0.002

      return check_hue and check_saturation and check_value
