




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[int]:
							torch.manual_seed(0							)
							a    =			UNetaDModel(
							    block_out_channels=(32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=3					,      out_channels=3					,      down_block_types=("DownBlock2D", "AttnDownBlock2D")					,      up_block_types=("AttnUpBlock2D", "UpBlock2D")					,      )
							return model





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    =			self.dummy_uncond_unet
							a    =			KarrasVeScheduler()

							a    =			KarrasVePipeline(unet=__lowerCamelCase					,      scheduler=__lowerCamelCase							)
							pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			torch.manual_seed(0							)
							a    =			pipe(num_inference_steps=2					,      generator=__lowerCamelCase					,      output_type="numpy"							).images

							a    =			torch.manual_seed(0							)
							a    =			pipe(num_inference_steps=2					,      generator=__lowerCamelCase					,      output_type="numpy"					,      return_dict=__lowerCamelCase							)[0]

							a    =			image[0, -3:, -3:, -1]
							a    =			image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							a    =			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2




@slow
@require_torch
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							a    =			"google/ncsnpp-celebahq-256"
							a    =			UNetaDModel.from_pretrained(__lowerCamelCase							)
							a    =			KarrasVeScheduler()

							a    =			KarrasVePipeline(unet=__lowerCamelCase					,      scheduler=__lowerCamelCase							)
							pipe.to(__lowerCamelCase							)
							pipe.set_progress_bar_config(disable=__lowerCamelCase							)

							a    =			torch.manual_seed(0							)
							a    =			pipe(num_inference_steps=20					,      generator=__lowerCamelCase					,      output_type="numpy"							).images

							a    =			image[0, -3:, -3:, -1]
							assert image.shape == (1, 2_56, 2_56, 3)
							a    =			np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2












from __future__ import annotations

from scipy.special import comb  # type: ignore




class     snake_case__							:
				"""simple docstring"""



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





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     float							)					->     list[float]:
							assert 0 <= t <= 1, "Time t must be between 0 and 1."
							a    =			[]
							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				:     Optional[Any]					,      __lowerCamelCase				:     float							)					->     tuple[float, float]:
							assert 0 <= t <= 1, "Time t must be between 0 and 1."

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

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

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

							a    =			[i[0] for i in self.list_of_points]
							a    =			[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






