





'''simple docstring'''





import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory






def     lowerCamelCase__			():
     raise RuntimeError("CUDA out of memory."	)





class 			lowerCAmelCase__( nn.Module    ):






       '''simple docstring'''


       def __init__(      self							)			->     Tuple:
            super().__init__()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							nn.Linear(3    ,						4							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							nn.BatchNormad(4							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							nn.Linear(4    ,						5							)







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     str:
            return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase							)							)							)





class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[]

            @find_executable_batch_size(starting_batch_size=1_2_8							)
            def mock_training_loop_function(__lowerCamelCase							):
                 nonlocal batch_sizes
                 batch_sizes.append(__lowerCamelCase							)
                 if batch_size != 8:
                      raise_fake_out_of_memory()

            mock_training_loop_function()
            self.assertListEqual(__lowerCamelCase    ,						[1_2_8, 6_4, 3_2, 1_6, 8]							)




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							[]

            @find_executable_batch_size(starting_batch_size=1_2_8							)
            def mock_training_loop_function(__lowerCamelCase    ,						__lowerCamelCase							):
                 nonlocal batch_sizes
                 batch_sizes.append(__lowerCamelCase							)
                 if batch_size != 8:
                      raise_fake_out_of_memory()
                 return batch_size, arga

            _SCREAMING_SNAKE_CASE       :	str						    =							mock_training_loop_function("hello"							)
            self.assertListEqual(__lowerCamelCase    ,						[1_2_8, 6_4, 3_2, 1_6, 8]							)
            self.assertListEqual([bs, arga]    ,						[8, "hello"]							)




       def     UpperCamelCase_			(      self							)			->     str:
            @find_executable_batch_size(starting_batch_size=0							)
            def mock_training_loop_function(__lowerCamelCase							):
                 pass

            with self.assertRaises(__lowerCamelCase							) as cm:
                 mock_training_loop_function()
                 self.assertIn("No executable batch size found, reached zero."    ,						cm.exception.args[0]							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            @find_executable_batch_size(starting_batch_size=1_6							)
            def mock_training_loop_function(__lowerCamelCase							):
                 if batch_size > 0:
                      raise_fake_out_of_memory()
                 pass

            with self.assertRaises(__lowerCamelCase							) as cm:
                 mock_training_loop_function()
                 self.assertIn("No executable batch size found, reached zero."    ,						cm.exception.args[0]							)




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            @find_executable_batch_size(starting_batch_size=1_2_8							)
            def mock_training_loop_function(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							):
                 if batch_size != 8:
                      raise raise_fake_out_of_memory()

            with self.assertRaises(__lowerCamelCase							) as cm:
                 mock_training_loop_function(1_2_8    ,						"hello"    ,						"world"							)
                 self.assertIn("Batch size was passed into `f`"    ,						cm.exception.args[0]							)
                 self.assertIn("`f(arg1='hello', arg2='world')"    ,						cm.exception.args[0]							)




       def     UpperCamelCase_			(      self							)			->     Any:
            @find_executable_batch_size(starting_batch_size=1_6							)
            def mock_training_loop_function(__lowerCamelCase							):
                 raise ValueError("Oops, we had an error!"							)

            with self.assertRaises(__lowerCamelCase							) as cm:
                 mock_training_loop_function()
                 self.assertIn("Oops, we had an error!"    ,						cm.exception.args[0]							)







       @require_cuda
       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.cuda.memory_allocated()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							ModelForTest()
            model.cuda()
            self.assertGreater(torch.cuda.memory_allocated()    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							release_memory(__lowerCamelCase							)
            self.assertEqual(torch.cuda.memory_allocated()    ,						__lowerCamelCase							)






from __future__ import annotations

from scipy.special import comb  # type: ignore





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase							)			->     Tuple:
            _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       :	Optional[int]						    =							len(__lowerCamelCase							) - 1




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

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.basis_function(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							0.0
            _SCREAMING_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     UpperCamelCase_			(      self    ,						__lowerCamelCase = 0.01							)			->     int:
            from matplotlib import pyplot as plt  # type: ignore

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

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

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