





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   (DDIMScheduler,)
      UpperCamelCase__       						=   (("""eta""", 0.0), ("""num_inference_steps""", 50))






      def 	UpperCAmelCase_   (						self:	Union[str, Any]					,  **__lowerCamelCase:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[int]												=      {
              "num_train_timesteps": 1000,
              "beta_start": 0.0_001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**__lowerCamelCase			)
          return config






      def 	UpperCAmelCase_   (						self:	Optional[Any]					,  **__lowerCamelCase:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      self.scheduler_classes[0]
          UpperCamelCase__:					Dict												=      self.get_scheduler_config(**__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      scheduler_class(**__lowerCamelCase			)

          UpperCamelCase__							,				UpperCamelCase__:					str												=      10, 0.0

          UpperCamelCase__:					Optional[int]												=      self.dummy_model()
          UpperCamelCase__:					Tuple												=      self.dummy_sample_deter

          scheduler.set_timesteps(__lowerCamelCase			)

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

          return sample






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=__lowerCamelCase			)

          UpperCamelCase__:					Optional[int]												=      self.scheduler_classes[0]
          UpperCamelCase__:					Dict												=      self.get_scheduler_config(steps_offset=1			)
          UpperCamelCase__:					Dict												=      scheduler_class(**__lowerCamelCase			)
          scheduler.set_timesteps(5			)
          assert torch.equal(scheduler.timesteps					,  torch.LongTensor([801, 601, 401, 201, 1]			)			)






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,  [0.002, 0.02, 0.2, 2]			):
              self.check_over_configs(beta_start=__lowerCamelCase					,  beta_end=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Optional[Any]			):
          '''simple docstring'''


          for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          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 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          for t in [1, 10, 49]:
              self.check_over_forward(time_step=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          for t, num_inference_steps in zip([1, 10, 50]					,  [10, 50, 500]			):
              self.check_over_forward(time_step=__lowerCamelCase					,  num_inference_steps=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          for t, eta in zip([1, 10, 49]					,  [0.0, 0.5, 1.0]			):
              self.check_over_forward(time_step=__lowerCamelCase					,  eta=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[int]												=      self.scheduler_classes[0]
          UpperCamelCase__:					Tuple												=      self.get_scheduler_config()
          UpperCamelCase__:					Optional[Any]												=      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(420					,  400			) - 0.14_771			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980					,  960			) - 0.32_460			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0					,  0			) - 0.0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487					,  486			) - 0.00_979			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999					,  998			) - 0.02			)			) < 1e-5






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      self.full_loop()

          UpperCamelCase__:					Dict												=      torch.sum(torch.abs(__lowerCamelCase			)			)
          UpperCamelCase__:					Optional[Any]												=      torch.mean(torch.abs(__lowerCamelCase			)			)

          assert abs(result_sum.item() - 172.0_067			) < 1e-2
          assert abs(result_mean.item() - 0.223_967			) < 1e-3






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      self.full_loop(prediction_type="v_prediction"			)

          UpperCamelCase__:					Union[str, Any]												=      torch.sum(torch.abs(__lowerCamelCase			)			)
          UpperCamelCase__:					Tuple												=      torch.mean(torch.abs(__lowerCamelCase			)			)

          assert abs(result_sum.item() - 52.5_302			) < 1e-2
          assert abs(result_mean.item() - 0.0_684			) < 1e-3






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					int												=      self.full_loop(set_alpha_to_one=__lowerCamelCase					,  beta_start=0.01			)
          UpperCamelCase__:					Dict												=      torch.sum(torch.abs(__lowerCamelCase			)			)
          UpperCamelCase__:					Union[str, Any]												=      torch.mean(torch.abs(__lowerCamelCase			)			)

          assert abs(result_sum.item() - 149.8_295			) < 1e-2
          assert abs(result_mean.item() - 0.1_951			) < 1e-3






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      self.full_loop(set_alpha_to_one=__lowerCamelCase					,  beta_start=0.01			)
          UpperCamelCase__:					Optional[Any]												=      torch.sum(torch.abs(__lowerCamelCase			)			)
          UpperCamelCase__:					List[str]												=      torch.mean(torch.abs(__lowerCamelCase			)			)

          assert abs(result_sum.item() - 149.0_784			) < 1e-2
          assert abs(result_mean.item() - 0.1_941			) < 1e-3








from manim import *




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      Rectangle(height=0.5					,  width=0.5			)
          UpperCamelCase__:					Optional[int]												=      Rectangle(height=0.46					,  width=0.46			).set_stroke(width=0			)
          UpperCamelCase__:					List[Any]												=      Rectangle(height=0.25					,  width=0.25			)

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

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

          UpperCamelCase__:					str												=      [mem.copy() for i in range(6			)]
          UpperCamelCase__:					Tuple												=      VGroup(*__lowerCamelCase			).arrange(__lowerCamelCase					,  buff=0			)

          UpperCamelCase__:					Union[str, Any]												=      Text("Model"					,  font_size=24			)
          UpperCamelCase__:					Tuple												=      Group(__lowerCamelCase					,  __lowerCamelCase			).arrange(__lowerCamelCase					,  buff=0.5					,  aligned_edge=__lowerCamelCase			)
          model.move_to([3, -1.0, 0]			)
          self.add(__lowerCamelCase			)

          UpperCamelCase__:					int												=      []
          UpperCamelCase__:					Optional[Any]												=      []

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

              UpperCamelCase__:					Tuple												=      Rectangle(height=0.46 / 4					,  width=0.46 / 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=0.02					,  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			)

          UpperCamelCase__:					int												=      [meta_mem.copy() for i in range(6			)]
          UpperCamelCase__:					Dict												=      [meta_mem.copy() for i in range(6			)]
          UpperCamelCase__:					Union[str, Any]												=      VGroup(*__lowerCamelCase			).arrange(__lowerCamelCase					,  buff=0			)
          UpperCamelCase__:					Tuple												=      VGroup(*__lowerCamelCase			).arrange(__lowerCamelCase					,  buff=0			)
          UpperCamelCase__:					int												=      VGroup(__lowerCamelCase					,  __lowerCamelCase			).arrange(__lowerCamelCase					,  buff=0			)
          UpperCamelCase__:					List[Any]												=      Text("Disk"					,  font_size=24			)
          UpperCamelCase__:					Union[str, Any]												=      Group(__lowerCamelCase					,  __lowerCamelCase			).arrange(__lowerCamelCase					,  buff=0.5					,  aligned_edge=__lowerCamelCase			)
          disk.move_to([-4.0, -1.25, 0]			)
          self.add(__lowerCamelCase					,  __lowerCamelCase			)

          UpperCamelCase__:					Optional[int]												=      []

          for i in range(6			):
              UpperCamelCase__:					List[str]												=      fill.copy().set_fill(__lowerCamelCase					,  opacity=0.8			)
              target.move_to(disk_left_col_base[i]			).scale(0.5			)
              cpu_disk_arr.append(__lowerCamelCase			)

          self.add(*__lowerCamelCase			)

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

          UpperCamelCase__:					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			)

          UpperCamelCase__:					str												=      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			)

          UpperCamelCase__:					Dict												=      MarkupText(F"The offloaded weights are all sent to the CPU."					,  font_size=24			)
          step_a.move_to([2, 2, 0]			)

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

          for i in range(6			):
              UpperCamelCase__:					List[Any]												=      cpu_disk_arr[i]
              UpperCamelCase__:					Optional[int]												=      rect.copy().set_fill(__lowerCamelCase					,  opacity=0.8			).scale(2.0			)
              cpa.generate_target()
              cpa.target.move_to(model_base[i]			)

              if i == 0:
                  rect.set_fill(__lowerCamelCase					,  opacity=0.8			)
                  rect.generate_target()
                  rect.target.move_to(cpu_left_col_base[0]			).scale(2.0			)

                  self.remove(
                      *__lowerCamelCase					,  *__lowerCamelCase					,  )

              else:
                  rect.generate_target()
                  rect.target.move_to(cpu_left_col_base[i]			).scale(2.0			)
              self.play(MoveToTarget(__lowerCamelCase			)					,  MoveToTarget(__lowerCamelCase			)					,  model_base[i].animate.set_stroke(__lowerCamelCase			)			)
          self.play(FadeOut(__lowerCamelCase			)			)

          UpperCamelCase__:					Dict												=      MarkupText(
              F"Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."					,  font_size=24					,  )
          step_a.move_to([2, 2, 0]			)

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

          UpperCamelCase__:					List[str]												=      []
          UpperCamelCase__:					Optional[Any]												=      []
          for i in range(6			):
              UpperCamelCase__:					List[str]												=      Arrow(start=__lowerCamelCase					,  end=__lowerCamelCase					,  color=__lowerCamelCase					,  buff=0.5			)
              a.next_to(model_base[i].get_left()					,  __lowerCamelCase					,  buff=0.2			)
              arrows.append(__lowerCamelCase			)
              animations.append(Write(__lowerCamelCase			)			)
          self.play(*__lowerCamelCase			)
          self.wait()

