

from __future__ import annotations

from scipy.special import comb  # type: ignore





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




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





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			float			):
       assert 0 <= t <= 1, "Time t must be between 0 and 1."
       SCREAMING_SNAKE_CASE										=   []
       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      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			float			):
       assert 0 <= t <= 1, "Time t must be between 0 and 1."

       SCREAMING_SNAKE_CASE										=   self.basis_function(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   0.0
       SCREAMING_SNAKE_CASE										=   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					:			List[str]     , __lowerCamelCase					:			float = 0.01			):
       from matplotlib import pyplot as plt  # type: ignore

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

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

       SCREAMING_SNAKE_CASE										=   [i[0] for i in self.list_of_points]
       SCREAMING_SNAKE_CASE										=   [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 tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)