





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class    _a  (  unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__       						=   (ScoreSdeVeScheduler,)
      UpperCamelCase__       						=   ()






      @property
      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      4
          UpperCamelCase__:					Optional[int]												=      3
          UpperCamelCase__:					Optional[Any]												=      8
          UpperCamelCase__:					Any												=      8

          UpperCamelCase__:					List[Any]												=      torch.rand((batch_size, num_channels, height, width)			)

          return sample






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


          UpperCamelCase__:					List[str]												=      4
          UpperCamelCase__:					Dict												=      3
          UpperCamelCase__:					str												=      8
          UpperCamelCase__:					Tuple												=      8

          UpperCamelCase__:					Dict												=      batch_size * num_channels * height * width
          UpperCamelCase__:					Tuple												=      torch.arange(__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      sample.reshape(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          UpperCamelCase__:					Tuple												=      sample / num_elems
          UpperCamelCase__:					int												=      sample.permute(3					,  0					,  1					,  2			)

          return sample






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


          def model(__lowerCamelCase:	str					,  __lowerCamelCase:	int					,  *__lowerCamelCase:	Dict			):
              return sample * t / (t + 1)

          return model






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


          UpperCamelCase__:					List[str]												=      {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1e-5,
          }

          config.update(**__lowerCamelCase			)
          return config






      def 	UpperCAmelCase_   (						self:	str					,  __lowerCamelCase:	Optional[int]=0					,  **__lowerCamelCase:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      dict(self.forward_default_kwargs			)

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase__:					List[Any]												=      self.dummy_sample
              UpperCamelCase__:					Tuple												=      0.1 * sample

              UpperCamelCase__:					List[Any]												=      self.get_scheduler_config(**__lowerCamelCase			)
              UpperCamelCase__:					Any												=      scheduler_class(**__lowerCamelCase			)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCamelCase			)
                  UpperCamelCase__:					int												=      scheduler_class.from_pretrained(__lowerCamelCase			)

              UpperCamelCase__:					List[str]												=      scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					int												=      new_scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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

              UpperCamelCase__:					List[str]												=      scheduler.step_correct(__lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					List[str]												=      new_scheduler.step_correct(
                  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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






      def 	UpperCAmelCase_   (						self:	Any					,  __lowerCamelCase:	Optional[int]=0					,  **__lowerCamelCase:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      dict(self.forward_default_kwargs			)
          kwargs.update(__lowerCamelCase			)

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase__:					Any												=      self.dummy_sample
              UpperCamelCase__:					Tuple												=      0.1 * sample

              UpperCamelCase__:					Dict												=      self.get_scheduler_config()
              UpperCamelCase__:					Any												=      scheduler_class(**__lowerCamelCase			)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCamelCase			)
                  UpperCamelCase__:					Optional[Any]												=      scheduler_class.from_pretrained(__lowerCamelCase			)

              UpperCamelCase__:					List[str]												=      scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					int												=      new_scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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

              UpperCamelCase__:					Dict												=      scheduler.step_correct(__lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					Any												=      new_scheduler.step_correct(
                  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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






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


          for timesteps in [10, 100, 1000]:
              self.check_over_configs(num_train_timesteps=__lowerCamelCase			)






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


          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]					,  [1, 100, 1000]			):
              self.check_over_configs(sigma_min=__lowerCamelCase					,  sigma_max=__lowerCamelCase			)






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


          for t in [0.1, 0.5, 0.75]:
              self.check_over_forward(time_step=__lowerCamelCase			)






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


          UpperCamelCase__:					List[str]												=      dict(self.forward_default_kwargs			)

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

          UpperCamelCase__:					int												=      3

          UpperCamelCase__:					Tuple												=      self.dummy_model()
          UpperCamelCase__:					List[Any]												=      self.dummy_sample_deter

          scheduler.set_sigmas(__lowerCamelCase			)
          scheduler.set_timesteps(__lowerCamelCase			)
          UpperCamelCase__:					Optional[Any]												=      torch.manual_seed(0			)

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase__:					Dict												=      scheduler.sigmas[i]

              for _ in range(scheduler.config.correct_steps			):
                  with torch.no_grad():
                      UpperCamelCase__:					int												=      model(__lowerCamelCase					,  __lowerCamelCase			)
                  UpperCamelCase__:					Union[str, Any]												=      scheduler.step_correct(__lowerCamelCase					,  __lowerCamelCase					,  generator=__lowerCamelCase					,  **__lowerCamelCase			).prev_sample

              with torch.no_grad():
                  UpperCamelCase__:					str												=      model(__lowerCamelCase					,  __lowerCamelCase			)

              UpperCamelCase__:					Tuple												=      scheduler.step_pred(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=__lowerCamelCase					,  **__lowerCamelCase			)
              UpperCamelCase__:					Union[str, Any]												=      output.prev_sample, output.prev_sample_mean

          UpperCamelCase__:					List[str]												=      torch.sum(torch.abs(__lowerCamelCase			)			)
          UpperCamelCase__:					Any												=      torch.mean(torch.abs(__lowerCamelCase			)			)

          assert np.isclose(result_sum.item()					,  143_7275_8528.0			)
          assert np.isclose(result_mean.item()					,  1871_4530.0			)






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


          UpperCamelCase__:					Dict												=      dict(self.forward_default_kwargs			)

          UpperCamelCase__:					List[Any]												=      kwargs.pop("num_inference_steps"					,  __lowerCamelCase			)

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase__:					Any												=      self.get_scheduler_config()
              UpperCamelCase__:					List[Any]												=      scheduler_class(**__lowerCamelCase			)

              UpperCamelCase__:					str												=      self.dummy_sample
              UpperCamelCase__:					Optional[int]												=      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"			):
                  UpperCamelCase__:					Union[str, Any]												=      num_inference_steps

              UpperCamelCase__:					Optional[Any]												=      scheduler.step_pred(__lowerCamelCase					,  0					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					Union[str, Any]												=      scheduler.step_pred(__lowerCamelCase					,  1					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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








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()

