




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


__UpperCamelCase      :      Optional[Any]     						=						logging.getLogger(__name__)





def 				snake_case  (     lowerCamelCase=2				,  lowerCamelCase=3				,  lowerCamelCase=16				,  lowerCamelCase = 10				,  lowerCamelCase = 2					):




	'''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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=None					):




	'''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        __UpperCamelCase    (     nn.Module   ):







	def __init__(  self      :							int			)		-> Union[str, Any]:
		"""simple docstring"""







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







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]			)		-> List[str]:
		"""simple docstring"""







		return x * self.a + self.b


class        __UpperCamelCase    (     unittest.TestCase   ):







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""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      :							str			)		-> Optional[int]:
		"""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      :							Union[str, Any]			)		-> int:
		"""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      :							Optional[int]			)		-> Any:
		"""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      :							str			)		-> Union[str, Any]:
		"""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=0.99			)
			__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      :							Tuple			)		-> Optional[Any]:
		"""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      :							Union[str, Any]			)		-> Optional[Any]:
		"""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__":
						__UpperCamelCase      :      Dict     						=						"""/tmp/accelerate/state_checkpointing"""
						__UpperCamelCase      :      str     						=						DummyModel()
						__UpperCamelCase      :      int     						=						torch.optim.Adam(params=model.parameters(), lr=1e-3)
						__UpperCamelCase      :      List[str]     						=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
						__UpperCamelCase      :      Optional[int]     						=						dummy_dataloaders()
						__UpperCamelCase      :      str     						=						ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						__UpperCamelCase      :      List[Any]     						=						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)
						__UpperCamelCase      :      str     						=						accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						__UpperCamelCase      :      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:
												__UpperCamelCase      :      Optional[int]     						=						group["""params"""][0].device
												break
						assert param_device.type == accelerator.device.type
						__UpperCamelCase      :      int     						=						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:
												__UpperCamelCase      :      Optional[Any]     						=						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:
												__UpperCamelCase      :      str     						=						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()









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







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







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		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=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

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

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

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

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

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

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

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

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

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass

