

"""simple docstring"""




import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class       lowerCamelCase  (unittest.TestCase	):

			@property
			def     SCREAMING_SNAKE_CASE (    self :  int				)      ->			List[Any]:
						torch.manual_seed(0				)
						SCREAMING_SNAKE_CASE__									=					UNetaDModel(
						    block_out_channels=(3_2, 6_4)			, layers_per_block=2			, sample_size=3_2			, in_channels=3			, out_channels=3			, down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			, up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			, )
						return model

			@property
			def     SCREAMING_SNAKE_CASE (    self :  Tuple				)      ->			List[Any]:
						torch.manual_seed(0				)
						SCREAMING_SNAKE_CASE__									=					VQModel(
						    block_out_channels=[3_2, 6_4]			, in_channels=3			, out_channels=3			, down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			, latent_channels=3			, )
						return model

			@property
			def     SCREAMING_SNAKE_CASE (    self :  Optional[int]				)      ->			Tuple:
						torch.manual_seed(0				)
						SCREAMING_SNAKE_CASE__									=					CLIPTextConfig(
						    bos_token_id=0			, eos_token_id=2			, hidden_size=3_2			, intermediate_size=3_7			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1_0_0_0			, )
						return CLIPTextModel(__lowerCamelCase				)





			def     SCREAMING_SNAKE_CASE (    self :  List[Any]				)      ->			Optional[int]:
						SCREAMING_SNAKE_CASE__									=					self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__									=					DDIMScheduler()
						SCREAMING_SNAKE_CASE__									=					self.dummy_vq_model

						SCREAMING_SNAKE_CASE__									=					LDMPipeline(unet=__lowerCamelCase			, vqvae=__lowerCamelCase			, scheduler=__lowerCamelCase				)
						ldm.to(__lowerCamelCase				)
						ldm.set_progress_bar_config(disable=__lowerCamelCase				)

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

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

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

						assert image.shape == (1, 6_4, 6_4, 3)
						SCREAMING_SNAKE_CASE__									=					np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]				)
						SCREAMING_SNAKE_CASE__									=					1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < tolerance





@slow
@require_torch
class       lowerCamelCase  (unittest.TestCase	):

			def     SCREAMING_SNAKE_CASE (    self :  Any				)      ->			List[str]:
						SCREAMING_SNAKE_CASE__									=					LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""				)
						ldm.to(__lowerCamelCase				)
						ldm.set_progress_bar_config(disable=__lowerCamelCase				)

						SCREAMING_SNAKE_CASE__									=					torch.manual_seed(0				)
						SCREAMING_SNAKE_CASE__									=					ldm(generator=__lowerCamelCase			, num_inference_steps=5			, output_type="""numpy"""				).images

						SCREAMING_SNAKE_CASE__									=					image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__									=					np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]				)
						SCREAMING_SNAKE_CASE__									=					1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance








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






