






from typing import List, Optional, Union

import numpy as np

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


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


class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				Dict            =    ['''input_values''', '''attention_mask''']






						def __init__(		self   ,  _UpperCamelCase=1   ,  _UpperCamelCase=1_60_00   ,  _UpperCamelCase=0.0   ,  _UpperCamelCase=False   ,  _UpperCamelCase=True   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												super().__init__(feature_size=_UpperCamelCase   ,  sampling_rate=_UpperCamelCase   ,  padding_value=_UpperCamelCase   ,  **_UpperCamelCase      )
												lowerCAmelCase__			  =  return_attention_mask
												lowerCAmelCase__			  =  do_normalize






						@staticmethod
						def 					UpperCamelCase__					(		_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = 0.0      ):






												"""simple docstring"""
												if attention_mask is not None:
																		lowerCAmelCase__			  =  np.array(_UpperCamelCase   ,  np.intaa      )
																		lowerCAmelCase__			  =  []

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

																								normed_input_values.append(_UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  [(x - x.mean()) / np.sqrt(x.var() + 1E-7      ) for x in input_values]

												return normed_input_values







						def __call__(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  _UpperCamelCase = False   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  **_UpperCamelCase   ,  ):






												"""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__			  =  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__			  =  is_batched_numpy or (
												    isinstance(_UpperCamelCase   ,  (list, tuple)      ) and (isinstance(raw_speech[0]   ,  (np.ndarray, tuple, list)      ))
												)

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

												# convert into correct format for padding
												lowerCAmelCase__			  =  BatchFeature({'input_values': raw_speech}      )

												lowerCAmelCase__			  =  self.pad(
												    _UpperCamelCase   ,  padding=_UpperCamelCase   ,  max_length=_UpperCamelCase   ,  truncation=_UpperCamelCase   ,  pad_to_multiple_of=_UpperCamelCase   ,  return_attention_mask=_UpperCamelCase   ,  )

												# convert input values to correct format
												lowerCAmelCase__			  =  padded_inputs['input_values']
												if not isinstance(input_values[0]   ,  np.ndarray      ):
																		lowerCAmelCase__			  =  [np.asarray(_UpperCamelCase   ,  dtype=np.floataa      ) for array in input_values]
												elif (
												    not isinstance(_UpperCamelCase   ,  np.ndarray      )
												    and isinstance(input_values[0]   ,  np.ndarray      )
												    and input_values[0].dtype is np.dtype(np.floataa      )
												):
																		lowerCAmelCase__			  =  [array.astype(np.floataa      ) for array in input_values]
												elif isinstance(_UpperCamelCase   ,  np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
																		lowerCAmelCase__			  =  input_values.astype(np.floataa      )

												# convert attention_mask to correct format
												lowerCAmelCase__			  =  padded_inputs.get('attention_mask'      )
												if attention_mask is not None:
																		lowerCAmelCase__			  =  [np.asarray(_UpperCamelCase   ,  dtype=np.intaa      ) for array in attention_mask]

												# zero-mean and unit-variance normalization
												if self.do_normalize:
																		lowerCAmelCase__			  =  (
																		    attention_mask
																		    if self._get_padding_strategies(_UpperCamelCase   ,  max_length=_UpperCamelCase      ) is not PaddingStrategy.DO_NOT_PAD
																		    else None
																		)
																		lowerCAmelCase__			  =  self.zero_mean_unit_var_norm(
																		    padded_inputs['input_values']   ,  attention_mask=_UpperCamelCase   ,  padding_value=self.padding_value      )

												if return_tensors is not None:
																		lowerCAmelCase__			  =  padded_inputs.convert_to_tensors(_UpperCamelCase      )

												return padded_inputs













def 		_UpperCamelCase      (			UpperCamelCase_				:    float			,				UpperCamelCase_				:    float			,				UpperCamelCase_				:    float						)     ->      list[int]:

						"""simple docstring"""



						if hue < 0 or hue > 360:
												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'						)

						lowerCAmelCase__			  =  value * saturation
						lowerCAmelCase__			  =  hue / 60
						lowerCAmelCase__			  =  chroma * (1 - abs(hue_section % 2 - 1						))
						lowerCAmelCase__			  =  value - chroma

						if hue_section >= 0 and hue_section <= 1:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 1 and hue_section <= 2:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 2 and hue_section <= 3:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
						elif hue_section > 3 and hue_section <= 4:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						elif hue_section > 4 and hue_section <= 5:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						else:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)

						return [red, green, blue]







def 		_UpperCamelCase      (			UpperCamelCase_				:    int			,				UpperCamelCase_				:    int			,				UpperCamelCase_				:    int						)     ->      list[float]:

						"""simple docstring"""



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

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

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

						lowerCAmelCase__			  =  red / 255
						lowerCAmelCase__			  =  green / 255
						lowerCAmelCase__			  =  blue / 255
						lowerCAmelCase__			  =  max(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  value - min(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  0 if value == 0 else chroma / value

						if chroma == 0:
												lowerCAmelCase__			  =  0.0
						elif value == float_red:
												lowerCAmelCase__			  =  60 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												lowerCAmelCase__			  =  60 * (2 + (float_blue - float_red) / chroma)
						else:
												lowerCAmelCase__			  =  60 * (4 + (float_red - float_green) / chroma)

						lowerCAmelCase__			  =  (hue + 360) % 360

						return [hue, saturation, value]







def 		_UpperCamelCase      (			UpperCamelCase_				:    list[float]			,				UpperCamelCase_				:    list[float]						)     ->      bool:

						"""simple docstring"""



						lowerCAmelCase__			  =  abs(hsv_a[0] - hsv_a[0]						) < 0.2
						lowerCAmelCase__			  =  abs(hsv_a[1] - hsv_a[1]						) < 0.002
						lowerCAmelCase__			  =  abs(hsv_a[2] - hsv_a[2]						) < 0.002

						return check_hue and check_saturation and check_value





