"""simple docstring"""






import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__lowerCAmelCase					: List[str]        =logging.getLogger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__=2      ,	lowerCAmelCase__=3      ,	lowerCAmelCase__=1_6      ,	lowerCAmelCase__ = 1_0      ,	lowerCAmelCase__ = 2	)				->					List[Any]:
      '''simple docstring'''


      def get_dataset(lowerCAmelCase__	):
            lowercase														=			torch.randn(batch_size * n_batches      ,	1	)
            return TensorDataset(lowerCAmelCase__      ,	a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,	1	)	)

      lowercase														=			get_dataset(lowerCAmelCase__	)
      lowercase														=			get_dataset(lowerCAmelCase__	)
      lowercase														=			DataLoader(lowerCAmelCase__      ,	shuffle=lowerCAmelCase__      ,	batch_size=lowerCAmelCase__      ,	num_workers=4	)
      lowercase														=			DataLoader(lowerCAmelCase__      ,	shuffle=lowerCAmelCase__      ,	batch_size=lowerCAmelCase__      ,	num_workers=4	)
      return (train_dataloader, valid_dataloader)


def     UpperCAmelCase__ (       lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__=None	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			[]
      for epoch in range(lowerCAmelCase__	):
            # Train quickly
            model.train()
            for batch in dataloader:
                  lowercase							,							lowercase														=			batch
                  lowercase														=			model(lowerCAmelCase__	)
                  lowercase														=			torch.nn.functional.mse_loss(lowerCAmelCase__      ,	lowerCAmelCase__	)
                  accelerator.backward(lowerCAmelCase__	)
                  optimizer.step()
                  optimizer.zero_grad()
            rands.append(random.random()	)  # Introduce some randomness
            if scheduler is not None:
                  scheduler.step()
      return rands





class 				_A		( nn.Module			):

      def __init__(		self		):







            """simple docstring"""



            super().__init__()
            lowercase														=			nn.Parameter(torch.randn(1		)		)
            lowercase														=			nn.Parameter(torch.randn(1		)		)







      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            return x * self.a + self.b






class 				_A		( unittest.TestCase			):

      def        A__ (		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			torch.optim.Adam(params=model.parameters()	, lr=1E-3		)
                  lowercase							,							lowercase														=			dummy_dataloaders()
                  lowercase														=			ProjectConfiguration(total_limit=1	, project_dir=__lowerCAmelCase	, automatic_checkpoint_naming=__lowerCAmelCase		)
                  # Train baseline
                  lowercase														=			Accelerator(project_config=__lowerCAmelCase		)
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  # Save initial
                  accelerator.save_state()

                  # Save second state
                  accelerator.save_state()
                  self.assertEqual(len(os.listdir(accelerator.project_dir		)		)	, 1		)

      def        A__ (		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			torch.optim.Adam(params=model.parameters()	, lr=1E-3		)
                  lowercase							,							lowercase														=			dummy_dataloaders()
                  # Train baseline
                  lowercase														=			Accelerator()
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  # Save initial
                  lowercase														=			os.path.join(__lowerCAmelCase	, """initial"""		)
                  accelerator.save_state(__lowerCAmelCase		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()
                  lowercase														=			train(3	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()

                  # Train partially
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			torch.optim.Adam(params=model.parameters()	, lr=1E-3		)
                  lowercase							,							lowercase														=			dummy_dataloaders()
                  lowercase														=			Accelerator()
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  accelerator.load_state(__lowerCAmelCase		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)

                  lowercase														=			train(2	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  # Save everything
                  lowercase														=			os.path.join(__lowerCAmelCase	, """checkpoint"""		)
                  accelerator.save_state(__lowerCAmelCase		)

                  # Load everything back in and make sure all states work
                  accelerator.load_state(__lowerCAmelCase		)
                  test_rands += train(1	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			torch.optim.Adam(params=model.parameters()	, lr=1E-3		)
                  lowercase							,							lowercase														=			dummy_dataloaders()
                  lowercase														=			ProjectConfiguration(automatic_checkpoint_naming=__lowerCAmelCase		)

                  # Train baseline
                  lowercase														=			Accelerator(project_dir=__lowerCAmelCase	, project_config=__lowerCAmelCase		)
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  # Save initial
                  accelerator.save_state()
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()
                  lowercase														=			train(3	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()

                  # Train partially
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			torch.optim.Adam(params=model.parameters()	, lr=1E-3		)
                  lowercase							,							lowercase														=			dummy_dataloaders()
                  lowercase														=			ProjectConfiguration(iteration=1	, automatic_checkpoint_naming=__lowerCAmelCase		)
                  lowercase														=			Accelerator(project_dir=__lowerCAmelCase	, project_config=__lowerCAmelCase		)
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  accelerator.load_state(os.path.join(__lowerCAmelCase	, """checkpoints"""	, """checkpoint_0"""		)		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)

                  lowercase														=			train(2	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  # Save everything
                  accelerator.save_state()

                  # Load everything back in and make sure all states work
                  accelerator.load_state(os.path.join(__lowerCAmelCase	, """checkpoints"""	, """checkpoint_1"""		)		)
                  test_rands += train(1	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  ((lowercase)							,							(lowercase))														=			model.a.item(), model.b.item()
                  lowercase														=			optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			torch.tensor([1, 2, 3]		)
            lowercase														=			torch.tensor([2, 3, 4]		)
            lowercase														=			DummyModel()
            lowercase														=			torch.optim.Adam(net.parameters()		)
            lowercase														=			Accelerator()
            with self.assertRaises(__lowerCAmelCase		) as ve:
                  accelerator.register_for_checkpointing(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            lowercase														=			str(ve.exception		)
            self.assertTrue("""Item at index 0""" in message		)
            self.assertTrue("""Item at index 1""" in message		)
            self.assertFalse("""Item at index 2""" in message		)
            self.assertFalse("""Item at index 3""" in message		)

      def        A__ (		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			torch.optim.Adam(params=model.parameters()	, lr=1E-3		)
                  lowercase														=			torch.optim.lr_scheduler.StepLR(__lowerCAmelCase	, step_size=1	, gamma=0.9_9		)
                  lowercase							,							lowercase														=			dummy_dataloaders()
                  lowercase														=			ProjectConfiguration(automatic_checkpoint_naming=__lowerCAmelCase		)
                  # Train baseline
                  lowercase														=			Accelerator(project_dir=__lowerCAmelCase	, project_config=__lowerCAmelCase		)
                  lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  # Save initial
                  accelerator.save_state()
                  lowercase														=			scheduler.state_dict()
                  train(3	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  self.assertNotEqual(__lowerCAmelCase	, scheduler.state_dict()		)

                  # Load everything back in and make sure all states work
                  accelerator.load_state(os.path.join(__lowerCAmelCase	, """checkpoints"""	, """checkpoint_0"""		)		)
                  self.assertEqual(__lowerCAmelCase	, scheduler.state_dict()		)

      def        A__ (		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase														=			DummyModel()
                  lowercase														=			ProjectConfiguration(automatic_checkpoint_naming=__lowerCAmelCase	, total_limit=2		)
                  # Train baseline
                  lowercase														=			Accelerator(project_dir=__lowerCAmelCase	, project_config=__lowerCAmelCase		)
                  lowercase														=			accelerator.prepare(__lowerCAmelCase		)
                  # Save 3 states:
                  for _ in range(11		):
                        accelerator.save_state()
                  self.assertTrue(not os.path.exists(os.path.join(__lowerCAmelCase	, """checkpoints"""	, """checkpoint_0"""		)		)		)
                  self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase	, """checkpoints"""	, """checkpoint_9"""		)		)		)
                  self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase	, """checkpoints"""	, """checkpoint_10"""		)		)		)







      @require_cuda
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
            execute_subprocess_async(__lowerCAmelCase	, env=os.environ.copy()		)


if __name__ == "__main__":
       __lowerCAmelCase					: int        ="""/tmp/accelerate/state_checkpointing"""
       __lowerCAmelCase					: Tuple        =DummyModel()
       __lowerCAmelCase					: Tuple        =torch.optim.Adam(params=model.parameters(), lr=1E-3)
       __lowerCAmelCase					: Optional[Any]        =torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
       __lowerCAmelCase					: List[str]        =dummy_dataloaders()
       __lowerCAmelCase					: Union[str, Any]        =ProjectConfiguration(automatic_checkpoint_naming=True)
       # Train baseline
       __lowerCAmelCase					: Dict        =Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
       if accelerator.process_index == 0:
              if os.path.exists(savedir):
                     shutil.rmtree(savedir)
              os.makedirs(savedir)
       __lowerCAmelCase					: str        =accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       __lowerCAmelCase					: Any        =accelerator.prepare(model, optimizer)
       train(3, model, train_dataloader, optimizer, accelerator, scheduler)
       # Check that the intial optimizer is loaded on the GPU
       for group in optimizer.param_groups:
              __lowerCAmelCase					: List[Any]        =group["""params"""][0].device
              break
       assert param_device.type == accelerator.device.type
       __lowerCAmelCase					: Dict        =model.cpu()
       accelerator.wait_for_everyone()
       accelerator.save_state()
       accelerator.wait_for_everyone()

       # Check CPU state
       accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
       for group in optimizer.param_groups:
              __lowerCAmelCase					: Tuple        =group["""params"""][0].device
              break
       assert (
           param_device.type == torch.device("""cpu""").type
       ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

       # Check device state
       model.to(accelerator.device)
       accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
       for group in optimizer.param_groups:
              __lowerCAmelCase					: Optional[int]        =group["""params"""][0].device
              break
       assert (
           param_device.type == accelerator.device.type
       ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

       # Check error
       with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
              accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
       accelerator.wait_for_everyone()
       if accelerator.process_index == 0:
              shutil.rmtree(savedir)
       accelerator.wait_for_everyone()




"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowercase														=			dummy_past_residuals[:]

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			scheduler_class.from_pretrained(__lowerCAmelCase		)
                        new_scheduler.set_timesteps(__lowerCAmelCase		)
                        # copy over dummy past residuals
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			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        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

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

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			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)
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			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        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			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"""		):
                        lowercase														=			num_inference_steps

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

                  lowercase														=			scheduler.timesteps[5]
                  lowercase														=			scheduler.timesteps[6]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



            for t, num_inference_steps in zip([1, 5, 10]	, [10, 50, 100]		):
                  self.check_over_forward(num_inference_steps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10



