
from manim import *





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					Rectangle(height=0.5				,width=0.5					)
					__A       =					Rectangle(height=0.46				,width=0.46					).set_stroke(width=0					)

					__A       =					[mem.copy() for i in range(6					)]
					__A       =					[mem.copy() for i in range(6					)]
					__A       =					VGroup(*A					).arrange(A				,buff=0					)
					__A       =					VGroup(*A					).arrange(A				,buff=0					)
					__A       =					VGroup(A				,A					).arrange(A				,buff=0					)
					__A       =					Text("CPU"				,font_size=24					)
					__A       =					Group(A				,A					).arrange(A				,buff=0.5				,aligned_edge=A					)
					cpu.move_to([-2.5, -0.5, 0]					)
					self.add(A					)

					__A       =					[mem.copy() for i in range(1					)]
					__A       =					VGroup(*A					).arrange(A				,buff=0					)
					__A       =					Text("GPU"				,font_size=24					)
					__A       =					Group(A				,A					).arrange(A				,buff=0.5				,aligned_edge=A					)
					gpu.align_to(A				,A					)
					gpu.set_x(gpu.get_x() - 1					)

					self.add(A					)

					__A       =					[mem.copy() for i in range(6					)]
					__A       =					VGroup(*A					).arrange(A				,buff=0					)

					__A       =					Text("Model"				,font_size=24					)
					__A       =					Group(A				,A					).arrange(A				,buff=0.5				,aligned_edge=A					)
					model.move_to([3, -1.0, 0]					)

					self.play(
					    Create(A				,run_time=1					)				,Create(A				,run_time=1					)				,Create(A				,run_time=1					)				,)

					__A       =					MarkupText(
					    f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''				,font_size=24				,)

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

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

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

					step_a.move_to([2, 2, 0]					)
					self.play(Write(A				,run_time=2.5					)				,Write(A					)				,Write(A					)					)

					self.add(A					)

					__A       =					[]
					__A       =					[]
					__A       =					[]
					for i, rect in enumerate(A					):
									__A       =					Rectangle(height=0.46				,width=0.46					).set_stroke(width=0.0					).set_fill(A				,opacity=0.7					)
									cpu_target.move_to(A					)
									cpu_target.generate_target()
									__A       =					0.46 / 4
									__A       =					0.46 / 3

									if i == 0:
													cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)				,buff=0.02				,direction=A					)
													cpu_target.target.set_x(cpu_target.target.get_x() + 0.1					)
									elif i == 3:
													cpu_target.target.next_to(cpu_targs[0].target				,direction=A				,buff=0.0					)
									else:
													cpu_target.target.next_to(cpu_targs[i - 1].target				,direction=A				,buff=0.0					)
									cpu_targs.append(A					)

									first_animations.append(rect.animate(run_time=0.5					).set_stroke(A					)					)
									second_animations.append(MoveToTarget(A				,run_time=1.5					)					)

					self.play(*A					)
					self.play(*A					)

					self.wait()




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 192.7614   ) < 1e-2
       assert abs(result_mean.item() - 0.2510   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 347.6357   ) < 1e-2
       assert abs(result_mean.item() - 0.4527   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

       with self.assertRaises(_a     ,		msg='''`timesteps` must be in descending order.'''   ):
         scheduler.set_timesteps(timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

       with self.assertRaises(_a     ,		msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''   ):
         scheduler.set_timesteps(num_inference_steps=_a     ,		timesteps=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _a     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
         scheduler.set_timesteps(timesteps=_a   )

