

from __future__ import annotations

from scipy.special import comb  # type: ignore







class        a__       :







		"""simple docstring"""




		def __init__(			self    : List[str]				,				UpperCAmelCase__    : str		)					->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      List[str]       =   list_of_points
								# Degree determines the flexibility of the curve.
								# Degree = 1 will produce a straight line.
								SCREAMING_SNAKE_CASE :      Dict       =   len(A__		) - 1


		def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : Tuple		)					->list[float]:
								"""simple docstring"""







								assert 0 <= t <= 1, "Time t must be between 0 and 1."
								SCREAMING_SNAKE_CASE :      List[str]       =   []
								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  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : List[Any]		)					->tuple[float, float]:
								"""simple docstring"""







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

								SCREAMING_SNAKE_CASE :      int       =   self.basis_function(A__		)
								SCREAMING_SNAKE_CASE :      Optional[int]       =   0.0
								SCREAMING_SNAKE_CASE :      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  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : Any = 0.01		)					->str:
								"""simple docstring"""







								from matplotlib import pyplot as plt  # type: ignore

								SCREAMING_SNAKE_CASE :      Tuple       =   []  # x coordinates of points to plot
								SCREAMING_SNAKE_CASE :      List[str]       =   []  # y coordinates of points to plot

								SCREAMING_SNAKE_CASE :      List[str]       =   0.0
								while t <= 1:
														SCREAMING_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

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :     List[Any]				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       ="""lxmert"""
  UpperCAmelCase__      : str			       ={}


  def __init__(			self    : int				,				UpperCAmelCase__    : Dict=3_0_5_2_2				,				UpperCAmelCase__    : int=7_6_8				,				UpperCAmelCase__    : Union[str, Any]=1_2				,				UpperCAmelCase__    : int=9_5_0_0				,				UpperCAmelCase__    : Union[str, Any]=1_6_0_0				,				UpperCAmelCase__    : Optional[Any]=4_0_0				,				UpperCAmelCase__    : List[str]=3_0_7_2				,				UpperCAmelCase__    : List[Any]="gelu"				,				UpperCAmelCase__    : int=0.1				,				UpperCAmelCase__    : Optional[Any]=0.1				,				UpperCAmelCase__    : Union[str, Any]=5_1_2				,				UpperCAmelCase__    : int=2				,				UpperCAmelCase__    : List[str]=0.02				,				UpperCAmelCase__    : str=1e-12				,				UpperCAmelCase__    : int=9				,				UpperCAmelCase__    : Optional[int]=5				,				UpperCAmelCase__    : int=5				,				UpperCAmelCase__    : List[str]=2_0_4_8				,				UpperCAmelCase__    : Tuple=4				,				UpperCAmelCase__    : List[str]=6.67				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[Any]=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[Any]=True				,				**UpperCAmelCase__    : Tuple				,				)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_size
        SCREAMING_SNAKE_CASE :      Any       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      str       =   hidden_act
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   intermediate_size
        SCREAMING_SNAKE_CASE :      List[str]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      str       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Dict       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Any       =   type_vocab_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[int]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      Dict       =   num_qa_labels
        SCREAMING_SNAKE_CASE :      str       =   num_object_labels
        SCREAMING_SNAKE_CASE :      str       =   num_attr_labels
        SCREAMING_SNAKE_CASE :      List[str]       =   l_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   x_layers
        SCREAMING_SNAKE_CASE :      List[str]       =   r_layers
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   visual_feat_dim
        SCREAMING_SNAKE_CASE :      Dict       =   visual_pos_dim
        SCREAMING_SNAKE_CASE :      List[Any]       =   visual_loss_normalizer
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   task_matched
        SCREAMING_SNAKE_CASE :      int       =   task_mask_lm
        SCREAMING_SNAKE_CASE :      int       =   task_obj_predict
        SCREAMING_SNAKE_CASE :      Optional[int]       =   task_qa
        SCREAMING_SNAKE_CASE :      Any       =   visual_obj_loss
        SCREAMING_SNAKE_CASE :      str       =   visual_attr_loss
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   visual_feat_loss
        SCREAMING_SNAKE_CASE :      List[Any]       =   {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
        super().__init__(**UpperCAmelCase__		)




