




from __future__ import annotations

from scipy.special import comb  # type: ignore




class     snake_case__							:
				"""simple docstring"""



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





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





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     float							)					->     tuple[float, float]:
							assert 0 <= t <= 1, "Time t must be between 0 and 1."

							a    =			self.basis_function(__lowerCamelCase							)
							a    =			0.0
							a    =			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 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     float = 0.01							)					->     List[str]:
							from matplotlib import pyplot as plt  # type: ignore

							a    =			[]  # x coordinates of points to plot
							a    =			[]  # y coordinates of points to plot

							a    =			0.0
							while t <= 1:
										a    =			self.bezier_curve_function(__lowerCamelCase							)
										to_plot_x.append(value[0]							)
										to_plot_y.append(value[1]							)
										t += step_size

							a    =			[i[0] for i in self.list_of_points]
							a    =			[i[1] for i in self.list_of_points]

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









import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

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

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



