"""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


lowercase__					       =	logging.get_logger(__name__)




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		["""input_features"""]



      def __init__(						self    ,			lowercase=80    ,			lowercase=1_60_00    ,			lowercase=1_60    ,			lowercase=30    ,			lowercase=4_00    ,			lowercase=0.0    ,			lowercase=False    ,			**lowercase    ,			)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase)	->			np.ndarray:





        '''simple docstring'''





        a__:     Tuple										=	spectrogram(
            lowercase    ,			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'    ,			)
        a__:     Optional[int]										=	log_spec[:, :-1]
        a__:     str										=	np.maximum(lowercase    ,			log_spec.max() - 8.0)
        a__:     List[str]										=	(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   lowerCamelCase_   (						lowercase    ,			lowercase    ,			lowercase = 0.0)	->			List[np.ndarray]:





        '''simple docstring'''





        if attention_mask is not None:
          a__:     List[str]										=	np.array(lowercase    ,			np.intaa)
          a__:     List[Any]										=	[]

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

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

        return normed_input_values



      def __call__(						self    ,			lowercase    ,			lowercase = True    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = "max_length"    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			**lowercase    ,			)	->			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.__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.')

        a__:     Optional[int]										=	isinstance(lowercase    ,			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}')
        a__:     Any										=	is_batched_numpy or (
            isinstance(lowercase    ,			(list, tuple)) and (isinstance(raw_speech[0]    ,			(np.ndarray, tuple, list)))
        )

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

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

        a__:     Optional[Any]										=	BatchFeature({'input_features': raw_speech})

        # convert into correct format for padding

        a__:     Union[str, Any]										=	self.pad(
            lowercase    ,			padding=lowercase    ,			max_length=max_length if max_length else self.n_samples    ,			truncation=lowercase    ,			pad_to_multiple_of=lowercase    ,			return_attention_mask=return_attention_mask or do_normalize    ,			)

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

        # make sure list is in array format
        a__:     Union[str, Any]										=	padded_inputs.get('input_features').transpose(2    ,			0    ,			1)

        a__:     Dict										=	[self._np_extract_fbank_features(lowercase) for waveform in input_features[0]]

        if isinstance(input_features[0]    ,			lowercase):
          a__:     Optional[Any]										=	[np.asarray(lowercase    ,			dtype=np.floataa) for feature in input_features]
        else:
          a__:     Any										=	input_features

        if return_attention_mask:
          # rescale from sample (48000) to feature (3000)
          a__:     Union[str, Any]										=	padded_inputs['attention_mask'][:, :: self.hop_length]

        if return_tensors is not None:
          a__:     Dict										=	padded_inputs.convert_to_tensors(lowercase)

        return padded_inputs



      def   lowerCamelCase_   (						self)	->			Dict[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	copy.deepcopy(self.__dict__)
        a__:     Dict										=	self.__class__.__name__
        if "mel_filters" in output:
          del output["mel_filters"]
        return output



"""simple docstring"""
from math import pi, sqrt, tan







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('surface_area_cube() only accepts non-negative values'   )
  return 6 * side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or breadth < 0 or height < 0:
    raise ValueError('surface_area_cuboid() only accepts non-negative values'   )
  return 2 * ((length * breadth) + (breadth * height) + (length * height))







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_sphere() only accepts non-negative values'   )
  return 4 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_hemisphere() only accepts non-negative values'   )
  return 3 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cone() only accepts non-negative values'   )
  return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_a < 0 or radius_a < 0 or height < 0:
    raise ValueError(
        'surface_area_conical_frustum() only accepts non-negative values'   )
  a__:     List[Any]										=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
  return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cylinder() only accepts non-negative values'   )
  return 2 * pi * radius * (height + radius)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if torus_radius < 0 or tube_radius < 0:
    raise ValueError('surface_area_torus() only accepts non-negative values'   )
  if torus_radius < tube_radius:
    raise ValueError(
        'surface_area_torus() does not support spindle or self intersecting tori'   )
  return 4 * pow(_SCREAMING_SNAKE_CASE		,     2   ) * torus_radius * tube_radius







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or width < 0:
    raise ValueError('area_rectangle() only accepts non-negative values'   )
  return length * width







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('area_square() only accepts non-negative values'   )
  return side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_triangle() only accepts non-negative values'   )
  return (base * height) / 2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if sidea < 0 or sidea < 0 or sidea < 0:
    raise ValueError('area_triangle_three_sides() only accepts non-negative values'   )
  elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
    raise ValueError('Given three sides do not form a triangle'   )
  a__:     int										=	(sidea + sidea + sidea) / 2
  a__:     Tuple										=	sqrt(
      semi_perimeter
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)   )
  return area







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_parallelogram() only accepts non-negative values'   )
  return base * height







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if basea < 0 or basea < 0 or height < 0:
    raise ValueError('area_trapezium() only accepts non-negative values'   )
  return 1 / 2 * (basea + basea) * height







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('area_circle() only accepts non-negative values'   )
  return pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_x < 0 or radius_y < 0:
    raise ValueError('area_ellipse() only accepts non-negative values'   )
  return pi * radius_x * radius_y







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if diagonal_a < 0 or diagonal_a < 0:
    raise ValueError('area_rhombus() only accepts non-negative values'   )
  return 1 / 2 * diagonal_a * diagonal_a







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) or sides < 3:
    raise ValueError(
        'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'   )
  elif length < 0:
    raise ValueError(
        'area_reg_polygon() only accepts non-negative values as \
length of a side'   )
  return (sides * length**2) / (4 * tan(pi / sides   ))
  return (sides * length**2) / (4 * tan(pi / sides   ))


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

 print('[DEMO] Areas of various geometric shapes: \n')
 print(f"Rectangle: {area_rectangle(10, 20) = }")
 print(f"Square: {area_square(10) = }")
 print(f"Triangle: {area_triangle(10, 10) = }")
 print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
 print(f"Parallelogram: {area_parallelogram(10, 20) = }")
 print(f"Rhombus: {area_rhombus(10, 20) = }")
 print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
 print(f"Circle: {area_circle(20) = }")
 print(f"Ellipse: {area_ellipse(10, 20) = }")
 print('\nSurface Areas of various geometric shapes: \n')
 print(f"Cube: {surface_area_cube(20) = }")
 print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
 print(f"Sphere: {surface_area_sphere(20) = }")
 print(f"Hemisphere: {surface_area_hemisphere(20) = }")
 print(f"Cone: {surface_area_cone(10, 20) = }")
 print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
 print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
 print(f"Torus: {surface_area_torus(20, 10) = }")
 print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
 print(f"Square: {area_reg_polygon(4, 10) = }")
 print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")


