
"""simple docstring"""





import warnings

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


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		"""ViTImageProcessor"""
  SCREAMING_SNAKE_CASE__			:						int       =		("""CLIPTokenizer""", """CLIPTokenizerFast""")







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    None
     if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."				,   lowercase_				,   )
        UpperCAmelCase_					:					Dict			     =    kwargs.pop("feature_extractor"  )

     UpperCAmelCase_					:					Dict			     =    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__(lowercase_				,   lowercase_  )







  def __call__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   lowercase_=None				,   **lowercase_  ):

     """simple docstring"""
     if text is None and visual_prompt is None and images is None:
        raise ValueError("You have to specify either text, visual prompt or images."  )

     if text is not None and visual_prompt is not None:
        raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."  )

     if text is not None:
        UpperCAmelCase_					:					Any			     =    self.tokenizer(lowercase_				,   return_tensors=lowercase_				,   **lowercase_  )

     if visual_prompt is not None:
        UpperCAmelCase_					:					Optional[int]			     =    self.image_processor(lowercase_				,   return_tensors=lowercase_				,   **lowercase_  )

     if images is not None:
        UpperCAmelCase_					:					Dict			     =    self.image_processor(lowercase_				,   return_tensors=lowercase_				,   **lowercase_  )

     if visual_prompt is not None and images is not None:
        UpperCAmelCase_					:					List[Any]			     =    {
            "pixel_values": image_features.pixel_values,
            "conditional_pixel_values": prompt_features.pixel_values,
        }
        return encoding
     elif text is not None and images is not None:
        UpperCAmelCase_					:					Tuple			     =    image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     elif visual_prompt is not None:
        UpperCAmelCase_					:					Optional[Any]			     =    {
            "conditional_pixel_values": prompt_features.pixel_values,
        }
        return encoding
     else:
        return BatchEncoding(data=dict(**lowercase_  )				,   tensor_type=lowercase_  )







  def        UpperCamelCase__					(		self				,   *lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return self.tokenizer.batch_decode(*lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   *lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return self.tokenizer.decode(*lowercase_				,   **lowercase_  )







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,   lowercase_				,   )
     return self.image_processor_class







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,   lowercase_				,   )
     return self.image_processor






"""simple docstring"""
from __future__ import annotations

from scipy.special import comb  # type: ignore



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			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				,       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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

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

			__snake_case	:						List[str]						=				self.basis_function(a_					)
			__snake_case	:						str						=				0.0
			__snake_case	:						Union[str, 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 				SCREAMING_SNAKE_CASE  (self				,       a_ = 0.01					):





			'''simple docstring'''

			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	:						int						=				0.0
			while t <= 1:
				__snake_case	:						Union[str, Any]						=				self.bezier_curve_function(a_					)
				to_plot_x.append(value[0]					)
				to_plot_y.append(value[1]					)
				t += step_size

			__snake_case	:						List[Any]						=				[i[0] for i in self.list_of_points]
			__snake_case	:						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





