






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Optional[int]											=   (IPNDMScheduler,)
	lowercase__						:					List[str]											=   (('''num_inference_steps''', 50),)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       List[Any]       						= {"""num_train_timesteps""": 10_00}
							config.update(**lowerCAmelCase__			)
							return config
	def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				Optional[int]:
							__magic_name__      :       Optional[int]       						= dict(self.forward_default_kwargs			)
							__magic_name__      :       Optional[Any]       						= kwargs.pop("""num_inference_steps"""      ,       lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= self.dummy_sample
							__magic_name__      :       List[Any]       						= 0.1 * sample
							__magic_name__      :       int       						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
													__magic_name__      :       Any       						= self.get_scheduler_config(**lowerCAmelCase__			)
													__magic_name__      :       Any       						= scheduler_class(**lowerCAmelCase__			)
													scheduler.set_timesteps(lowerCAmelCase__			)
													# copy over dummy past residuals
													__magic_name__      :       Optional[int]       						= dummy_past_residuals[:]

													if time_step is None:
																			__magic_name__      :       Optional[Any]       						= scheduler.timesteps[len(scheduler.timesteps			) // 2]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowerCAmelCase__			)
																			__magic_name__      :       Optional[int]       						= scheduler_class.from_pretrained(lowerCAmelCase__			)
																			new_scheduler.set_timesteps(lowerCAmelCase__			)
																			# copy over dummy past residuals
																			__magic_name__      :       Optional[Any]       						= dummy_past_residuals[:]

													__magic_name__      :       Tuple       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
													__magic_name__      :       Optional[int]       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

													__magic_name__      :       List[Any]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
													__magic_name__      :       List[Any]       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
	def       __magic_name__    (    self			)							->				List[Any]:
							pass
	def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       str       						= dict(self.forward_default_kwargs			)
							__magic_name__      :       int       						= kwargs.pop("""num_inference_steps"""      ,       lowerCAmelCase__			)
							__magic_name__      :       Optional[Any]       						= self.dummy_sample
							__magic_name__      :       Dict       						= 0.1 * sample
							__magic_name__      :       Any       						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

							for scheduler_class in self.scheduler_classes:
													__magic_name__      :       List[Any]       						= self.get_scheduler_config()
													__magic_name__      :       Union[str, Any]       						= scheduler_class(**lowerCAmelCase__			)
													scheduler.set_timesteps(lowerCAmelCase__			)

													# copy over dummy past residuals (must be after setting timesteps)
													__magic_name__      :       str       						= dummy_past_residuals[:]

													if time_step is None:
																			__magic_name__      :       Optional[Any]       						= scheduler.timesteps[len(scheduler.timesteps			) // 2]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowerCAmelCase__			)
																			__magic_name__      :       Union[str, Any]       						= scheduler_class.from_pretrained(lowerCAmelCase__			)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(lowerCAmelCase__			)

																			# copy over dummy past residual (must be after setting timesteps)
																			__magic_name__      :       List[Any]       						= dummy_past_residuals[:]

													__magic_name__      :       Optional[int]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
													__magic_name__      :       Union[str, Any]       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

													__magic_name__      :       Tuple       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
													__magic_name__      :       Tuple       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

													assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       Optional[int]       						= self.scheduler_classes[0]
							__magic_name__      :       Union[str, Any]       						= self.get_scheduler_config(**lowerCAmelCase__			)
							__magic_name__      :       str       						= scheduler_class(**lowerCAmelCase__			)

							__magic_name__      :       str       						= 10
							__magic_name__      :       List[str]       						= self.dummy_model()
							__magic_name__      :       int       						= self.dummy_sample_deter
							scheduler.set_timesteps(lowerCAmelCase__			)

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

							for i, t in enumerate(scheduler.timesteps			):
													__magic_name__      :       int       						= model(lowerCAmelCase__      ,       lowerCAmelCase__			)
													__magic_name__      :       Tuple       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			).prev_sample

							return sample
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       Tuple       						= dict(self.forward_default_kwargs			)

							__magic_name__      :       Tuple       						= kwargs.pop("""num_inference_steps"""      ,       lowerCAmelCase__			)

							for scheduler_class in self.scheduler_classes:
													__magic_name__      :       Optional[Any]       						= self.get_scheduler_config()
													__magic_name__      :       Dict       						= scheduler_class(**lowerCAmelCase__			)

													__magic_name__      :       int       						= self.dummy_sample
													__magic_name__      :       Dict       						= 0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCAmelCase__      ,       """set_timesteps"""			):
																			scheduler.set_timesteps(lowerCAmelCase__			)
													elif num_inference_steps is not None and not hasattr(lowerCAmelCase__      ,       """set_timesteps"""			):
																			__magic_name__      :       List[str]       						= num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__magic_name__      :       str       						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
													__magic_name__      :       List[str]       						= dummy_past_residuals[:]

													__magic_name__      :       int       						= scheduler.timesteps[5]
													__magic_name__      :       str       						= scheduler.timesteps[6]

													__magic_name__      :       str       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
													__magic_name__      :       str       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

													self.assertEqual(output_a.shape      ,       sample.shape			)
													self.assertEqual(output_a.shape      ,       output_a.shape			)

													__magic_name__      :       Any       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
													__magic_name__      :       str       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

													self.assertEqual(output_a.shape      ,       sample.shape			)
													self.assertEqual(output_a.shape      ,       output_a.shape			)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							for timesteps in [1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__      ,       time_step=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							for t, num_inference_steps in zip([1, 5, 10]      ,       [10, 50, 1_00]			):
													self.check_over_forward(num_inference_steps=lowerCAmelCase__      ,       time_step=lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       int       						= self.full_loop()
							__magic_name__      :       Optional[int]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

							assert abs(result_mean.item() - 2_54_05_29			) < 10








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()
