






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Optional[int]											=   (DDIMParallelScheduler,)
	lowercase__						:					Optional[Any]											=   (('''eta''', 0.0), ('''num_inference_steps''', 50))
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       str       						= {
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_0_0_1,
							    """beta_end""": 0.0_2,
							    """beta_schedule""": """linear""",
							    """clip_sample""": True,
							}

							config.update(**lowerCAmelCase__			)
							return config
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       List[Any]       						= self.scheduler_classes[0]
							__magic_name__      :       Optional[Any]       						= self.get_scheduler_config(**lowerCAmelCase__			)
							__magic_name__      :       Optional[int]       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__						,__magic_name__      :       int       						= 10, 0.0

							__magic_name__      :       Tuple       						= self.dummy_model()
							__magic_name__      :       Any       						= self.dummy_sample_deter

							scheduler.set_timesteps(lowerCAmelCase__			)

							for t in scheduler.timesteps:
													__magic_name__      :       Union[str, Any]       						= model(lowerCAmelCase__      ,       lowerCAmelCase__			)
													__magic_name__      :       List[str]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			).prev_sample

							return sample
	def       __magic_name__    (    self			)							->				Tuple:
							for timesteps in [1_00, 5_00, 10_00]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= self.scheduler_classes[0]
							__magic_name__      :       Tuple       						= self.get_scheduler_config(steps_offset=1			)
							__magic_name__      :       Dict       						= scheduler_class(**lowerCAmelCase__			)
							scheduler.set_timesteps(5			)
							assert torch.equal(scheduler.timesteps      ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]			)			)
	def       __magic_name__    (    self			)							->				Any:
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]			):
													self.check_over_configs(beta_start=lowerCAmelCase__      ,       beta_end=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[str]:
							for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							self.check_over_configs(thresholding=lowerCAmelCase__			)
							for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=lowerCAmelCase__      ,       prediction_type=lowerCAmelCase__      ,       sample_max_value=lowerCAmelCase__      ,       )
	def       __magic_name__    (    self			)							->				int:
							for t in [1, 10, 49]:
													self.check_over_forward(time_step=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Dict:
							for t, num_inference_steps in zip([1, 10, 50]      ,       [10, 50, 5_00]			):
													self.check_over_forward(time_step=lowerCAmelCase__      ,       num_inference_steps=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							for t, eta in zip([1, 10, 49]      ,       [0.0, 0.5, 1.0]			):
													self.check_over_forward(time_step=lowerCAmelCase__      ,       eta=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= self.scheduler_classes[0]
							__magic_name__      :       List[Any]       						= self.get_scheduler_config()
							__magic_name__      :       List[str]       						= scheduler_class(**lowerCAmelCase__			)

							assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0			) - 0.0			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_20      ,       4_00			) - 0.1_4_7_7_1			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_80      ,       9_60			) - 0.3_2_4_6_0			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0			) - 0.0			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87      ,       4_86			) - 0.0_0_9_7_9			)			) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99      ,       9_98			) - 0.0_2			)			) < 1e-5
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Union[str, Any]       						= self.scheduler_classes[0]
							__magic_name__      :       Optional[Any]       						= self.get_scheduler_config()
							__magic_name__      :       Dict       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__						,__magic_name__      :       Dict       						= 10, 0.0
							scheduler.set_timesteps(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= self.dummy_model()
							__magic_name__      :       Optional[int]       						= self.dummy_sample_deter
							__magic_name__      :       Any       						= self.dummy_sample_deter + 0.1
							__magic_name__      :       Union[str, Any]       						= self.dummy_sample_deter - 0.1

							__magic_name__      :       Tuple       						= samplea.shape[0]
							__magic_name__      :       List[str]       						= torch.stack([samplea, samplea, samplea]      ,       dim=0			)
							__magic_name__      :       Optional[Any]       						= torch.arange(lowerCAmelCase__			)[0:3, None].repeat(1      ,       lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= model(samples.flatten(0      ,       1			)      ,       timesteps.flatten(0      ,       1			)			)
							__magic_name__      :       Tuple       						= scheduler.batch_step_no_noise(lowerCAmelCase__      ,       timesteps.flatten(0      ,       1			)      ,       samples.flatten(0      ,       1			)      ,       lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       Tuple       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 1_1_4_7.7_9_0_4			) < 1e-2
							assert abs(result_mean.item() - 0.4_9_8_2			) < 1e-3
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[int]       						= self.full_loop()

							__magic_name__      :       int       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       List[Any]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
							assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       List[str]       						= self.full_loop(prediction_type="""v_prediction"""			)

							__magic_name__      :       Tuple       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       str       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
							assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3
	def       __magic_name__    (    self			)							->				Optional[Any]:
							# We specify different beta, so that the first alpha is 0.99
							__magic_name__      :       Tuple       						= self.full_loop(set_alpha_to_one=lowerCAmelCase__      ,       beta_start=0.0_1			)
							__magic_name__      :       int       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       Optional[Any]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
							assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3







	def       __magic_name__    (    self			)							->				Optional[Any]:
							# We specify different beta, so that the first alpha is 0.99
							__magic_name__      :       Optional[Any]       						= self.full_loop(set_alpha_to_one=lowerCAmelCase__      ,       beta_start=0.0_1			)
							__magic_name__      :       Optional[Any]       						= torch.sum(torch.abs(lowerCAmelCase__			)			)
							__magic_name__      :       Optional[Any]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
							assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3








from manim import *





class 	snake_case__ (							_lowerCAmelCase				):
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= Rectangle(height=0.5      ,       width=0.5			)
							__magic_name__      :       Optional[int]       						= Rectangle(height=0.2_5      ,       width=0.2_5			)
							__magic_name__      :       str       						= Rectangle(height=0.4_6      ,       width=0.4_6			).set_stroke(width=0			)

							__magic_name__      :       List[Any]       						= [mem.copy() for i in range(6			)]
							__magic_name__      :       int       						= [mem.copy() for i in range(6			)]
							__magic_name__      :       Tuple       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       List[str]       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       str       						= VGroup(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       Union[str, Any]       						= Text("""CPU"""      ,       font_size=24			)
							__magic_name__      :       Tuple       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							cpu.move_to([-2.5, -0.5, 0]			)
							self.add(lowerCAmelCase__			)

							__magic_name__      :       Any       						= [mem.copy() for i in range(4			)]
							__magic_name__      :       List[Any]       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       Tuple       						= Text("""GPU"""      ,       font_size=24			)
							__magic_name__      :       Tuple       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							gpu.move_to([-1, -1, 0]			)
							self.add(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= [mem.copy() for i in range(6			)]
							__magic_name__      :       Union[str, Any]       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)

							__magic_name__      :       str       						= Text("""Model"""      ,       font_size=24			)
							__magic_name__      :       Optional[int]       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							model.move_to([3, -1.0, 0]			)
							self.add(lowerCAmelCase__			)

							__magic_name__      :       str       						= []
							__magic_name__      :       Tuple       						= []
							__magic_name__      :       Union[str, Any]       						= []

							for i, rect in enumerate(lowerCAmelCase__			):
													rect.set_stroke(lowerCAmelCase__			)

													__magic_name__      :       Optional[Any]       						= Rectangle(height=0.4_6 / 4      ,       width=0.4_6 / 3			).set_stroke(width=0.0			).set_fill(lowerCAmelCase__      ,       opacity=0.7			)

													if i == 0:
																			cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT			)      ,       buff=0.0_2      ,       direction=lowerCAmelCase__			)
																			cpu_target.set_x(cpu_target.get_x() + 0.1			)
													elif i == 3:
																			cpu_target.next_to(model_cpu_arr[0]      ,       direction=lowerCAmelCase__      ,       buff=0.0			)
													else:
																			cpu_target.next_to(model_cpu_arr[i - 1]      ,       direction=lowerCAmelCase__      ,       buff=0.0			)
													self.add(lowerCAmelCase__			)
													model_cpu_arr.append(lowerCAmelCase__			)

							self.add(*lowerCAmelCase__      ,       *lowerCAmelCase__      ,       *lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= [mem.copy() for i in range(6			)]
							__magic_name__      :       Optional[Any]       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)

							__magic_name__      :       Any       						= Text("""Loaded Checkpoint"""      ,       font_size=24			)
							__magic_name__      :       Optional[int]       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							checkpoint.move_to([3, 0.5, 0]			)

							self.add(lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= []
							__magic_name__      :       Tuple       						= []

							for i, rect in enumerate(lowerCAmelCase__			):
													__magic_name__      :       Dict       						= fill.copy().set_fill(lowerCAmelCase__      ,       opacity=0.7			)
													target.move_to(lowerCAmelCase__			)
													ckpt_arr.append(lowerCAmelCase__			)

													__magic_name__      :       int       						= target.copy()
													if i < 5:
																			cpu_target.move_to(cpu_left_col_base[i + 1]			)
													else:
																			cpu_target.move_to(cpu_right_col_base[i - 5]			)
													ckpt_cpu_arr.append(lowerCAmelCase__			)
							self.add(*lowerCAmelCase__      ,       *lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= Square(side_length=2.2			)
							key.move_to([-5, 2, 0]			)

							__magic_name__      :       str       						= MarkupText(
							    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'      ,       font_size=18      ,       )

							key_text.move_to([-5, 2.4, 0]			)

							self.add(lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       Any       						= MarkupText(
							    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'      ,       font_size=18      ,       )

							blue_text.next_to(lowerCAmelCase__      ,       DOWN * 2.4      ,       aligned_edge=key_text.get_left()			)
							self.add(lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= MarkupText(
							    F'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'      ,       font_size=24      ,       )
							step_a.move_to([2, 2, 0]			)

							__magic_name__      :       int       						= [meta_mem.copy() for i in range(6			)]
							__magic_name__      :       Union[str, Any]       						= [meta_mem.copy() for i in range(6			)]
							__magic_name__      :       Any       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       str       						= VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       Tuple       						= VGroup(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0			)
							__magic_name__      :       int       						= Text("""Disk"""      ,       font_size=24			)
							__magic_name__      :       Union[str, Any]       						= Group(lowerCAmelCase__      ,       lowerCAmelCase__			).arrange(lowerCAmelCase__      ,       buff=0.5      ,       aligned_edge=lowerCAmelCase__			)
							disk.move_to([-4.0, -1.2_5, 0]			)
							self.play(Write(lowerCAmelCase__      ,       run_time=3			)      ,       Write(lowerCAmelCase__      ,       run_time=1			)      ,       Create(lowerCAmelCase__      ,       run_time=1			)			)

							__magic_name__      :       List[Any]       						= []
							for i, rect in enumerate(lowerCAmelCase__			):
													__magic_name__      :       Dict       						= rect.copy()
													target.generate_target()
													target.target.move_to(disk_left_col_base[i]			).scale(0.5			)
													animations.append(MoveToTarget(lowerCAmelCase__      ,       run_time=1.5			)			)
							self.play(*lowerCAmelCase__			)

							self.play(FadeOut(lowerCAmelCase__			)			)

							__magic_name__      :       str       						= MarkupText(F'Then, the checkpoint is removed from memory\nthrough garbage collection.'      ,       font_size=24			)
							step_a.move_to([2, 2, 0]			)

							self.play(Write(lowerCAmelCase__      ,       run_time=3			)			)

							self.play(
							    FadeOut(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       *lowerCAmelCase__			)      ,       )

							self.wait()
