




'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		(UnCLIPScheduler,)


				def   A_ (		self    :					Union[str, Any]       ,				**lowercase_    :					Union[str, Any]					):
									snake_case_									=				{
									    '''num_train_timesteps''': 1000,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**lowercase_					)
									return config


				def   A_ (		self    :					str					):
									for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=lowercase_					)


				def   A_ (		self    :					str					):
									for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=lowercase_					)


				def   A_ (		self    :					Any					):
									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=lowercase_       ,				prev_timestep=lowercase_					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(variance_type='''fixed_small_log'''					)
									snake_case_									=				scheduler_class(**lowercase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000e-10					)					) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.054_9625					)					) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.999_4987					)					) < 1e-5


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(variance_type='''learned_range'''					)
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				0.5

									assert scheduler._get_variance(1       ,				predicted_variance=lowercase_					) - -10.171_2790 < 1e-5
									assert scheduler._get_variance(487       ,				predicted_variance=lowercase_					) - -5.799_8052 < 1e-5
									assert scheduler._get_variance(999       ,				predicted_variance=lowercase_					) - -0.001_0011 < 1e-5


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config()
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				scheduler.timesteps

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

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

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

														snake_case_									=				pred_prev_sample

									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 252.268_2495					) < 1e-2
									assert abs(result_mean.item() - 0.328_4743					) < 1e-3


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config()
									snake_case_									=				scheduler_class(**lowercase_					)

									scheduler.set_timesteps(25					)

									snake_case_									=				scheduler.timesteps

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

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

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

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

														snake_case_									=				pred_prev_sample

									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 258.204_4983					) < 1e-2
									assert abs(result_mean.item() - 0.336_2038					) < 1e-3


				def   A_ (		self    :					str					):
									pass


				def   A_ (		self    :					int					):
									pass




from manim import *






class 	__magic_name__    (			lowerCAmelCase_      ):





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =Rectangle(height=0.5					,  width=0.5					)
						__a        =Rectangle(height=0.25					,  width=0.25					)
						__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(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('CPU'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						cpu.move_to([-2.5, -0.5, 0]					)
						self.add(__snake_case					)

						__a        =[mem.copy() for i in range(4					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('GPU'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						gpu.move_to([-1, -1, 0]					)
						self.add(__snake_case					)

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

						__a        =Text('Model'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						model.move_to([3, -1.0, 0]					)
						self.add(__snake_case					)

						__a        =[]
						__a        =[]
						__a        =[]

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

									__a        =Rectangle(height=0.46 / 4					,  width=0.46 / 3					).set_stroke(width=0.0					).set_fill(__snake_case					,  opacity=0.7					)

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

						self.add(*__snake_case					,  *__snake_case					,  *__snake_case					)

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

						__a        =Text('Loaded Checkpoint'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						checkpoint.move_to([3, 0.5, 0]					)

						self.add(__snake_case					)

						__a        =[]
						__a        =[]

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

									__a        =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(__snake_case					)
						self.add(*__snake_case					,  *__snake_case					)

						__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]					)

						self.add(__snake_case					,  __snake_case					)

						__a        =MarkupText(
						    f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'					,  font_size=18					,  )

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

						__a        =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]					)

						__a        =[meta_mem.copy() for i in range(6					)]
						__a        =[meta_mem.copy() for i in range(6					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('Disk'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						disk.move_to([-4.0, -1.25, 0]					)
						self.play(Write(__snake_case					,  run_time=3					)					,  Write(__snake_case					,  run_time=1					)					,  Create(__snake_case					,  run_time=1					)					)

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

						self.play(FadeOut(__snake_case					)					)

						__a        =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(__snake_case					,  run_time=3					)					)

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

						self.wait()






