
'''simple docstring'''
from __future__ import annotations

from scipy.special import comb  # type: ignore






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_):
     snake_case_ :    Dict     			=						list_of_points
     # Degree determines the flexibility of the curve.
     # Degree = 1 will produce a straight line.
     snake_case_ :    Union[str, Any]     			=						len(lowercase_) - 1





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





 def        snake_case__		(    self ,       lowercase_):
     assert 0 <= t <= 1, "Time t must be between 0 and 1."

     snake_case_ :    Optional[Any]     			=						self.basis_function(lowercase_)
     snake_case_ :    str     			=						0.0
     snake_case_ :    int     			=						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 ,       lowercase_ = 0.01):
     from matplotlib import pyplot as plt  # type: ignore

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

     snake_case_ :    Optional[Any]     			=						0.0
     while t <= 1:
         snake_case_ :    Dict     			=						self.bezier_curve_function(lowercase_)
         to_plot_x.append(value[0])
         to_plot_y.append(value[1])
         t += step_size

     snake_case_ :    str     			=						[i[0] for i in self.list_of_points]
     snake_case_ :    Any     			=						[i[1] for i in self.list_of_points]

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




'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""ClapFeatureExtractor"""
 UpperCAmelCase_		    =						("""RobertaTokenizer""", """RobertaTokenizerFast""")





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__(lowercase_ ,       lowercase_)





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    Any     			=						kwargs.pop("sampling_rate" ,       lowercase_)

     if text is None and audios is None:
         raise ValueError("You have to specify either text or audios. Both cannot be none.")

     if text is not None:
         snake_case_ :    Optional[int]     			=						self.tokenizer(lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if audios is not None:
         snake_case_ :    Any     			=						self.feature_extractor(
             lowercase_ ,       sampling_rate=lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if text is not None and audios is not None:
         snake_case_ :    Dict     			=						audio_features.input_features
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_) ,       tensor_type=lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.batch_decode(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.decode(*lowercase_ ,       **lowercase_)





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer.model_input_names
     snake_case_ :    int     			=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))




