






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			if i + 1 == timesteps.shape[0]:
				__lowercase             =  None
			else:
				__lowercase             =  timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(
			    _lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass









from __future__ import annotations

from scipy.special import comb  # type: ignore


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							list[tuple[float, float]]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  list_of_points
		# Degree determines the flexibility of the curve.
		# Degree = 1 will produce a straight line.
		__lowercase             =  len(_lowerCAmelCase			) - 1







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							float			)		-> list[float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."
		__lowercase             =  []
		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  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							float			)		-> tuple[float, float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."

		__lowercase             =  self.basis_function(_lowerCAmelCase			)
		__lowercase             =  0.0
		__lowercase             =  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  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							float = 0.01			)		-> Union[str, Any]:
		"""simple docstring"""







		from matplotlib import pyplot as plt  # type: ignore

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

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

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

