





import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   ["""input_values""", """attention_mask"""]


      def __init__(		self   ,				a__ = 1   ,				a__ = 1_6000   ,				a__ = 0.0   ,				a__ = False   ,				a__ = 80   ,				a__ = 16   ,				a__ = 64   ,				a__ = "hann_window"   ,				a__ = 1.0   ,				a__ = 80   ,				a__ = 7600   ,				a__ = 1e-10   ,				a__ = 2   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(feature_size=a__   ,				sampling_rate=a__   ,				padding_value=a__   ,				**a__)
             _lowerCamelCase				:  Union[str, Any]						=		do_normalize
             _lowerCamelCase				:  List[Any]						=		return_attention_mask

             _lowerCamelCase				:  Union[str, Any]						=		num_mel_bins
             _lowerCamelCase				:  Optional[int]						=		hop_length
             _lowerCamelCase				:  Dict						=		win_length
             _lowerCamelCase				:  int						=		win_function
             _lowerCamelCase				:  str						=		frame_signal_scale
             _lowerCamelCase				:  Any						=		fmin
             _lowerCamelCase				:  Dict						=		fmax
             _lowerCamelCase				:  Union[str, Any]						=		mel_floor
             _lowerCamelCase				:  Optional[int]						=		reduction_factor

             _lowerCamelCase				:  Any						=		win_length * sampling_rate // 1000
             _lowerCamelCase				:  int						=		hop_length * sampling_rate // 1000
             _lowerCamelCase				:  Optional[Any]						=		optimal_fft_length(self.sample_size)
             _lowerCamelCase				:  str						=		(self.n_fft // 2) + 1

             _lowerCamelCase				:  Optional[Any]						=		window_function(window_length=self.sample_size   ,				name=self.win_function   ,				periodic=a__)

             _lowerCamelCase				:  Any						=		mel_filter_bank(
                 num_frequency_bins=self.n_freqs   ,				num_mel_filters=self.num_mel_bins   ,				min_frequency=self.fmin   ,				max_frequency=self.fmax   ,				sampling_rate=self.sampling_rate   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)

             if frame_signal_scale != 1.0:
                    warnings.warn(
                        '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''   ,				a__   ,				)
             if reduction_factor != 2.0:
                    warnings.warn(
                        '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''   ,				a__   ,				)


      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def 					__snake_case      (		a__   ,				a__   ,				a__ = 0.0):



             """simple docstring"""





             if attention_mask is not None:
                    _lowerCamelCase				:  Union[str, Any]						=		np.array(a__   ,				np.intaa)
                    _lowerCamelCase				:  Optional[int]						=		[]

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

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

             return normed_input_values


      def 					__snake_case      (		self   ,				a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  int						=		spectrogram(
                 a__   ,				window=self.window   ,				frame_length=self.sample_size   ,				hop_length=self.sample_stride   ,				fft_length=self.n_fft   ,				mel_filters=self.mel_filters   ,				mel_floor=self.mel_floor   ,				log_mel='''log10'''   ,				)
             return log_mel_spec.T


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



             """simple docstring"""





             if audio is None and audio_target is None:
                    raise ValueError('''You must provide either `audio` or `audio_target` values.''')

             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 audio 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.''')

             if audio is not None:
                    _lowerCamelCase				:  str						=		self._process_audio(
                        a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				**a__   ,				)
             else:
                    _lowerCamelCase				:  str						=		None

             if audio_target is not None:
                    _lowerCamelCase				:  Dict						=		self._process_audio(
                        a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				**a__   ,				)

                    if inputs is None:
                           return inputs_target
                    else:
                           _lowerCamelCase				:  List[str]						=		inputs_target['''input_values''']
                           _lowerCamelCase				:  Any						=		inputs_target.get('''attention_mask''')
                           if decoder_attention_mask is not None:
                                  _lowerCamelCase				:  Optional[int]						=		decoder_attention_mask

             return inputs


      def 					__snake_case      (		self   ,				a__   ,				a__ = False   ,				a__ = False   ,				a__ = None   ,				a__ = False   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		isinstance(a__   ,				np.ndarray) and len(speech.shape) > 1
             if is_batched_numpy and len(speech.shape) > 2:
                    raise ValueError(F"""Only mono-channel audio is supported for input to {self}""")
             _lowerCamelCase				:  Tuple						=		is_batched_numpy or (
                 isinstance(a__   ,				(list, tuple)) and (isinstance(speech[0]   ,				(np.ndarray, tuple, list)))
             )

             if is_batched:
                    _lowerCamelCase				:  int						=		[np.asarray(a__   ,				dtype=np.floataa) for speech in 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 speech.dtype is np.dtype(np.floataa):
                    _lowerCamelCase				:  Tuple						=		speech.astype(np.floataa)

             # always return batch
             if not is_batched:
                    _lowerCamelCase				:  Optional[Any]						=		[speech]

             # needed to make pad() work on spectrogram inputs
             _lowerCamelCase				:  Tuple						=		self.feature_size

             # convert into correct format for padding
             if is_target:
                    _lowerCamelCase				:  Dict						=		[self._extract_mel_features(a__) for waveform in speech]
                    _lowerCamelCase				:  Any						=		BatchFeature({'''input_values''': features})
                    _lowerCamelCase				:  Tuple						=		self.num_mel_bins
             else:
                    _lowerCamelCase				:  Tuple						=		BatchFeature({'''input_values''': speech})

             _lowerCamelCase				:  Optional[Any]						=		self.pad(
                 a__   ,				padding=a__   ,				max_length=a__   ,				truncation=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=a__   ,				**a__   ,				)

             _lowerCamelCase				:  int						=		feature_size_hack

             # convert input values to correct format
             _lowerCamelCase				:  Tuple						=		padded_inputs['''input_values''']
             if not isinstance(input_values[0]   ,				np.ndarray):
                    _lowerCamelCase				:  List[Any]						=		[np.asarray(a__   ,				dtype=np.floataa) for array in input_values]
             elif (
                 not isinstance(a__   ,				np.ndarray)
                 and isinstance(input_values[0]   ,				np.ndarray)
                 and input_values[0].dtype is np.dtype(np.floataa)
             ):
                    _lowerCamelCase				:  List[Any]						=		[array.astype(np.floataa) for array in input_values]
             elif isinstance(a__   ,				np.ndarray) and input_values.dtype is np.dtype(np.floataa):
                    _lowerCamelCase				:  Union[str, Any]						=		input_values.astype(np.floataa)

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

             # zero-mean and unit-variance normalization
             if not is_target and self.do_normalize:
                    _lowerCamelCase				:  int						=		(
                        attention_mask
                        if self._get_padding_strategies(a__   ,				max_length=a__) is not PaddingStrategy.DO_NOT_PAD
                        else None
                    )
                    _lowerCamelCase				:  Union[str, Any]						=		self.zero_mean_unit_var_norm(
                        padded_inputs['''input_values''']   ,				attention_mask=a__   ,				padding_value=self.padding_value)

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

             return padded_inputs



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		super().to_dict()

             # Don't serialize these as they are derived from the other properties.
             _lowerCamelCase				:  Dict						=		['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
             for name in names:
                    if name in output:
                           del output[name]

             return output








from __future__ import annotations

from scipy.special import comb  # type: ignore







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		list_of_points
             # Degree determines the flexibility of the curve.
             # Degree = 1 will produce a straight line.
             _lowerCamelCase				:  List[Any]						=		len(a__) - 1


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."
             _lowerCamelCase				:  list[float]						=		[]
             for i in range(len(self.list_of_points)):
                    # basis function for each i
                    output_values.append(
                        comb(self.degree   ,				a__) * ((1 - t) ** (self.degree - i)) * (t**i))
             # the basis must sum up to 1 for it to produce a valid Bezier curve.
             assert round(sum(a__)   ,				5) == 1
             return output_values


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."

             _lowerCamelCase				:  Any						=		self.basis_function(a__)
             _lowerCamelCase				:  List[Any]						=		0.0
             _lowerCamelCase				:  List[Any]						=		0.0
             for i in range(len(self.list_of_points)):
                    # For all points, sum up the product of i-th basis function and i-th point.
                    x += basis_function[i] * self.list_of_points[i][0]
                    y += basis_function[i] * self.list_of_points[i][1]
             return (x, y)



      def 					__snake_case      (		self   ,				a__ = 0.01):



             """simple docstring"""





             from matplotlib import pyplot as plt  # type: ignore

             _lowerCamelCase				:  list[float]						=		[]  # x coordinates of points to plot
             _lowerCamelCase				:  list[float]						=		[]  # y coordinates of points to plot

             _lowerCamelCase				:  List[Any]						=		0.0
             while t <= 1:
                    _lowerCamelCase				:  Tuple						=		self.bezier_curve_function(a__)
                    to_plot_x.append(value[0])
                    to_plot_y.append(value[1])
                    t += step_size

             _lowerCamelCase				:  List[Any]						=		[i[0] for i in self.list_of_points]
             _lowerCamelCase				:  Union[str, Any]						=		[i[1] for i in self.list_of_points]

             plt.plot(
                 a__   ,				a__   ,				color='''blue'''   ,				label='''Curve of Degree ''' + str(self.degree)   ,				)
             plt.scatter(a__   ,				a__   ,				color='''red'''   ,				label='''Control Points''')
             plt.legend()
             plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

