






"""simple docstring"""







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		["""image_processor""", """tokenizer"""]
						_SCREAMING_SNAKE_CASE  : int												=		"""ViltImageProcessor"""
						_SCREAMING_SNAKE_CASE  : List[str]												=		("""BertTokenizer""", """BertTokenizerFast""")






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

										lowercase    :      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__(__magic_name__			, __magic_name__				)
										lowercase    :      Optional[int]       = self.image_processor






						def __call__(		self  :List[Any]			, __magic_name__  :Optional[Any]			, __magic_name__  :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[bool, str, PaddingStrategy] = False			, __magic_name__  :Union[bool, str, TruncationStrategy] = None			, __magic_name__  :Optional[int] = None			, __magic_name__  :int = 0			, __magic_name__  :Optional[int] = None			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[bool] = None			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = False			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, TensorType]] = None			, **__magic_name__  :Optional[int]			, )      ->BatchEncoding:
										lowercase    :      Union[str, Any]       = self.tokenizer(
										    text=__magic_name__			, add_special_tokens=__magic_name__			, padding=__magic_name__			, truncation=__magic_name__			, max_length=__magic_name__			, stride=__magic_name__			, pad_to_multiple_of=__magic_name__			, return_token_type_ids=__magic_name__			, return_attention_mask=__magic_name__			, return_overflowing_tokens=__magic_name__			, return_special_tokens_mask=__magic_name__			, return_offsets_mapping=__magic_name__			, return_length=__magic_name__			, verbose=__magic_name__			, return_tensors=__magic_name__			, **__magic_name__			, )
										# add pixel_values + pixel_mask
										lowercase    :      Union[str, Any]       = self.image_processor(__magic_name__			, return_tensors=__magic_name__				)
										encoding.update(__magic_name__				)

										return encoding






						def      __snake_case      (		self  :List[Any]			, *__magic_name__  :List[str]			, **__magic_name__  :Tuple				)      ->Tuple:
										return self.tokenizer.batch_decode(*__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :List[str]			, *__magic_name__  :str			, **__magic_name__  :Any				)      ->Dict:
										return self.tokenizer.decode(*__magic_name__			, **__magic_name__				)






						@property
						def      __snake_case      (		self  :Optional[Any]				)      ->Optional[int]:
										lowercase    :      List[Any]       = self.tokenizer.model_input_names
										lowercase    :      List[Any]       = self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Optional[int]:
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			, __magic_name__			, )
										return self.image_processor_class






						@property
						def      __snake_case      (		self  :Any				)      ->Optional[int]:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			, __magic_name__			, )
										return self.image_processor








"""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
