


from __future__ import annotations

from scipy.special import comb  # type: ignore







class  snake_case__:







						'''simple docstring'''




						def __init__( self      ,  __lowercase							)    ->			List[Any]:
										lowerCAmelCase_       :					Any			 = list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										lowerCAmelCase_       :					List[Any]			 = len(lowerCAmelCase__							) - 1




						def   lowercase_       ( self      ,  __lowercase							)    ->			list[float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										lowerCAmelCase_       :					list[float]			 = []
										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   lowercase_       ( self      ,  __lowercase							)    ->			tuple[float, float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										lowerCAmelCase_       :					Optional[Any]			 = self.basis_function(lowerCAmelCase__							)
										lowerCAmelCase_       :					Dict			 = 0.0
										lowerCAmelCase_       :					Dict			 = 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      ,  __lowercase = 0.01							)    ->			Any:
										from matplotlib import pyplot as plt  # type: ignore

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

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase	:	Any		     =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[Any]		     =       ["""ConvBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[str]		     =       [
         """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ConvBertForMaskedLM""",
         """ConvBertForMultipleChoice""",
         """ConvBertForQuestionAnswering""",
         """ConvBertForSequenceClassification""",
         """ConvBertForTokenClassification""",
         """ConvBertLayer""",
         """ConvBertModel""",
         """ConvBertPreTrainedModel""",
         """load_tf_weights_in_convbert""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Union[str, Any]		     =       [
         """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFConvBertForMaskedLM""",
         """TFConvBertForMultipleChoice""",
         """TFConvBertForQuestionAnswering""",
         """TFConvBertForSequenceClassification""",
         """TFConvBertForTokenClassification""",
         """TFConvBertLayer""",
         """TFConvBertModel""",
         """TFConvBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
     from .tokenization_convbert import ConvBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_convbert_fast import ConvBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convbert import (
              CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvBertForMaskedLM,
              ConvBertForMultipleChoice,
              ConvBertForQuestionAnswering,
              ConvBertForSequenceClassification,
              ConvBertForTokenClassification,
              ConvBertLayer,
              ConvBertModel,
              ConvBertPreTrainedModel,
              load_tf_weights_in_convbert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convbert import (
              TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFConvBertForMaskedLM,
              TFConvBertForMultipleChoice,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertLayer,
              TFConvBertModel,
              TFConvBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase	:	Union[str, Any]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






