






"""simple docstring"""







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


_lowerCAmelCase            =	logging.get_logger(__name__)


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_values""", """padding_mask"""]






						def __init__(		self  :str			, __magic_name__  :int = 1			, __magic_name__  :int = 24_000			, __magic_name__  :float = 0.0			, __magic_name__  :float = None			, __magic_name__  :float = None			, **__magic_name__  :List[Any]			, )      ->Dict:
										super().__init__(feature_size=__magic_name__			, sampling_rate=__magic_name__			, padding_value=__magic_name__			, **__magic_name__				)
										lowercase    :      Dict       = chunk_length_s
										lowercase    :      List[str]       = overlap






						@property
						def      __snake_case      (		self  :str				)      ->Optional[int]:
										if self.chunk_length_s is None:
														return None
										else:
														return int(self.chunk_length_s * self.sampling_rate				)






						@property
						def      __snake_case      (		self  :Dict				)      ->Optional[int]:
										if self.chunk_length_s is None or self.overlap is None:
														return None
										else:
														return max(1			, int((1.0 - self.overlap) * self.chunk_length				)				)






						def __call__(		self  :int			, __magic_name__  :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			, __magic_name__  :Optional[Union[bool, str, PaddingStrategy]] = None			, __magic_name__  :Optional[bool] = False			, __magic_name__  :Optional[int] = None			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :Optional[int] = None			, )      ->BatchFeature:
										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 padding and truncation:
														raise ValueError("""Both padding and truncation were set. Make sure you only set one."""				)
										elif padding is None:
														# by default let's pad the inputs
														lowercase    :      int       = True

										lowercase    :      int       = bool(
										    isinstance(__magic_name__			, (list, tuple)				) and (isinstance(raw_audio[0]			, (np.ndarray, tuple, list)				))				)

										if is_batched:
														lowercase    :      int       = [np.asarray(__magic_name__			, dtype=np.floataa				).T for audio in raw_audio]
										elif not is_batched and not isinstance(__magic_name__			, np.ndarray				):
														lowercase    :      Union[str, Any]       = np.asarray(__magic_name__			, dtype=np.floataa				)
										elif isinstance(__magic_name__			, np.ndarray				) and raw_audio.dtype is np.dtype(np.floataa				):
														lowercase    :      Optional[int]       = raw_audio.astype(np.floataa				)

										# always return batch
										if not is_batched:
														lowercase    :      Union[str, Any]       = [np.asarray(__magic_name__				).T]

										# verify inputs are valid
										for idx, example in enumerate(__magic_name__				):
														if example.ndim > 2:
																		raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""				)
														if self.feature_size == 1 and example.ndim != 1:
																		raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""				)
														if self.feature_size == 2 and example.shape[-1] != 2:
																		raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""				)

										lowercase    :      Union[str, Any]       = None
										lowercase    :      Optional[int]       = BatchFeature({"""input_values""": raw_audio}				)
										if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
														if truncation:
																		lowercase    :      Optional[int]       = min(array.shape[0] for array in raw_audio				)
																		lowercase    :      str       = int(np.floor(max_length / self.chunk_stride				)				)
																		lowercase    :      Tuple       = (nb_step - 1) * self.chunk_stride + self.chunk_length
														elif padding:
																		lowercase    :      Tuple       = max(array.shape[0] for array in raw_audio				)
																		lowercase    :      List[Any]       = int(np.ceil(max_length / self.chunk_stride				)				)
																		lowercase    :      Any       = (nb_step - 1) * self.chunk_stride + self.chunk_length
																		lowercase    :      Dict       = """max_length"""
														else:
																		lowercase    :      str       = input_values

        # normal padding on batch
										if padded_inputs is None:
														lowercase    :      Optional[int]       = self.pad(
														    __magic_name__			, max_length=__magic_name__			, truncation=__magic_name__			, padding=__magic_name__			, return_attention_mask=__magic_name__			, )
														if padding:
																		lowercase    :      List[Any]       = padded_inputs.pop("""attention_mask"""				)

										lowercase    :      Tuple       = []
										for example in padded_inputs.pop("""input_values"""				):
														if self.feature_size == 1:
																		lowercase    :      List[str]       = example[..., None]
														input_values.append(example.T				)

										lowercase    :      Dict       = input_values
										if return_tensors is not None:
														lowercase    :      int       = padded_inputs.convert_to_tensors(__magic_name__				)

										return padded_inputs








"""simple docstring"""







from __future__ import annotations

from scipy.special import comb  # type: ignore


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :list[tuple[float, float]]				)      ->str:
										lowercase    :      List[Any]       = list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										lowercase    :      Optional[int]       = len(__magic_name__				) - 1






						def      __snake_case      (		self  :List[str]			, __magic_name__  :float				)      ->list[float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										lowercase    :      list[float]       = []
										for i in range(len(self.list_of_points				)				):
														# basis function for each i
														output_values.append(
														    comb(self.degree			, __magic_name__				) * ((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(__magic_name__				)			, 5				) == 1
										return output_values






						def      __snake_case      (		self  :List[str]			, __magic_name__  :float				)      ->tuple[float, float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										lowercase    :      str       = self.basis_function(__magic_name__				)
										lowercase    :      Optional[int]       = 0.0
										lowercase    :      List[str]       = 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  :Tuple			, __magic_name__  :float = 0.01				)      ->List[str]:
										from matplotlib import pyplot as plt  # type: ignore

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

										lowercase    :      int       = 0.0
										while t <= 1:
														lowercase    :      List[Any]       = self.bezier_curve_function(__magic_name__				)
														to_plot_x.append(value[0]				)
														to_plot_y.append(value[1]				)
														t += step_size

										lowercase    :      Union[str, Any]       = [i[0] for i in self.list_of_points]
										lowercase    :      Optional[int]       = [i[1] for i in self.list_of_points]

										plt.plot(
										    __magic_name__			, __magic_name__			, color="""blue"""			, label="""Curve of Degree """ + str(self.degree				)			, )
										plt.scatter(__magic_name__			, __magic_name__			, 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
