'''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


_lowercase		:				List[Any]	       =		logging.getLogger(__name__)




def 							lowerCamelCase			(   UpperCAmelCase__ :      int=2   ,   UpperCAmelCase__ :      Optional[Any]=3   ,   UpperCAmelCase__ :      Optional[Any]=16   ,   UpperCAmelCase__ :      int = 10   ,   UpperCAmelCase__ :      int = 2				)    ->   Optional[Any]:

							def get_dataset(UpperCAmelCase__ :      Tuple				):
														lowercase_      :    Optional[Any]       		= torch.randn(batch_size * n_batches   ,   1				)
														return TensorDataset(UpperCAmelCase__   ,   a * x + b + 0.1 * torch.randn(batch_size * n_batches   ,   1				)				)

							lowercase_      :    Any       		= get_dataset(UpperCAmelCase__				)
							lowercase_      :    str       		= get_dataset(UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= DataLoader(UpperCAmelCase__   ,   shuffle=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__   ,   num_workers=4				)
							lowercase_      :    Optional[Any]       		= DataLoader(UpperCAmelCase__   ,   shuffle=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__   ,   num_workers=4				)
							return (train_dataloader, valid_dataloader)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      List[str]=None				)    ->   Dict:
							lowercase_      :    Optional[int]       		= []
							for epoch in range(UpperCAmelCase__				):
														# Train quickly
														model.train()
														for batch in dataloader:
																					lowercase_		,		lowercase_      :    int       		= batch
																					lowercase_      :    Tuple       		= model(UpperCAmelCase__				)
																					lowercase_      :    Optional[Any]       		= torch.nn.functional.mse_loss(UpperCAmelCase__   ,   UpperCAmelCase__				)
																					accelerator.backward(UpperCAmelCase__				)
																					optimizer.step()
																					optimizer.zero_grad()
														rands.append(random.random()				)  # Introduce some randomness
														if scheduler is not None:
																					scheduler.step()
							return rands





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[Any]					):
										super().__init__()
										lowercase_      :    List[Any]       		= nn.Parameter(torch.randn(1					)					)
										lowercase_      :    Tuple       		= nn.Parameter(torch.randn(1					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Dict					):
										return x * self.a + self.b





class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										with tempfile.TemporaryDirectory() as tmpdir:
																	set_seed(42					)
																	lowercase_      :    int       		= DummyModel()
																	lowercase_      :    Optional[Any]       		= torch.optim.Adam(params=model.parameters()       ,    lr=1E-3					)
																	lowercase_		,		lowercase_      :    Any       		= dummy_dataloaders()
																	lowercase_      :    int       		= ProjectConfiguration(total_limit=1       ,    project_dir=lowercase_       ,    automatic_checkpoint_naming=lowercase_					)
																	# Train baseline
																	lowercase_      :    List[Any]       		= Accelerator(project_config=lowercase_					)
																	lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    List[str]       		= accelerator.prepare(
																	    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	# Save initial
																	accelerator.save_state()

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										with tempfile.TemporaryDirectory() as tmpdir:
																	set_seed(42					)
																	lowercase_      :    List[str]       		= DummyModel()
																	lowercase_      :    Any       		= torch.optim.Adam(params=model.parameters()       ,    lr=1E-3					)
																	lowercase_		,		lowercase_      :    Dict       		= dummy_dataloaders()
																	# Train baseline
																	lowercase_      :    str       		= Accelerator()
																	lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    Any       		= accelerator.prepare(
																	    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	# Save initial
																	lowercase_      :    Any       		= os.path.join(lowercase_       ,    """initial"""					)
																	accelerator.save_state(lowercase_					)
																	((lowercase_)		,		(lowercase_))      :    Tuple       		= model.a.item(), model.b.item()
																	lowercase_      :    Tuple       		= optimizer.state_dict()
																	lowercase_      :    int       		= train(3       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	((lowercase_)		,		(lowercase_))      :    List[Any]       		= model.a.item(), model.b.item()
																	lowercase_      :    Dict       		= optimizer.state_dict()

																	# Train partially
																	set_seed(42					)
																	lowercase_      :    List[Any]       		= DummyModel()
																	lowercase_      :    int       		= torch.optim.Adam(params=model.parameters()       ,    lr=1E-3					)
																	lowercase_		,		lowercase_      :    List[str]       		= dummy_dataloaders()
																	lowercase_      :    Optional[int]       		= Accelerator()
																	lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    Optional[Any]       		= accelerator.prepare(
																	    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	accelerator.load_state(lowercase_					)
																	((lowercase_)		,		(lowercase_))      :    List[Any]       		= model.a.item(), model.b.item()
																	lowercase_      :    Optional[int]       		= optimizer.state_dict()
																	self.assertEqual(lowercase_       ,    lowercase_					)
																	self.assertEqual(lowercase_       ,    lowercase_					)
																	self.assertEqual(lowercase_       ,    lowercase_					)

																	lowercase_      :    Optional[int]       		= train(2       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	# Save everything
																	lowercase_      :    str       		= os.path.join(lowercase_       ,    """checkpoint"""					)
																	accelerator.save_state(lowercase_					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										with tempfile.TemporaryDirectory() as tmpdir:
																	set_seed(42					)
																	lowercase_      :    Any       		= DummyModel()
																	lowercase_      :    int       		= torch.optim.Adam(params=model.parameters()       ,    lr=1E-3					)
																	lowercase_		,		lowercase_      :    Dict       		= dummy_dataloaders()
																	lowercase_      :    Tuple       		= ProjectConfiguration(automatic_checkpoint_naming=lowercase_					)

																	# Train baseline
																	lowercase_      :    List[Any]       		= Accelerator(project_dir=lowercase_       ,    project_config=lowercase_					)
																	lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    str       		= accelerator.prepare(
																	    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	# Save initial
																	accelerator.save_state()
																	((lowercase_)		,		(lowercase_))      :    str       		= model.a.item(), model.b.item()
																	lowercase_      :    str       		= optimizer.state_dict()
																	lowercase_      :    Optional[Any]       		= train(3       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	((lowercase_)		,		(lowercase_))      :    str       		= model.a.item(), model.b.item()
																	lowercase_      :    List[Any]       		= optimizer.state_dict()

																	# Train partially
																	set_seed(42					)
																	lowercase_      :    Union[str, Any]       		= DummyModel()
																	lowercase_      :    Union[str, Any]       		= torch.optim.Adam(params=model.parameters()       ,    lr=1E-3					)
																	lowercase_		,		lowercase_      :    Union[str, Any]       		= dummy_dataloaders()
																	lowercase_      :    Dict       		= ProjectConfiguration(iteration=1       ,    automatic_checkpoint_naming=lowercase_					)
																	lowercase_      :    int       		= Accelerator(project_dir=lowercase_       ,    project_config=lowercase_					)
																	lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    Optional[int]       		= accelerator.prepare(
																	    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	accelerator.load_state(os.path.join(lowercase_       ,    """checkpoints"""       ,    """checkpoint_0"""					)					)
																	((lowercase_)		,		(lowercase_))      :    List[Any]       		= model.a.item(), model.b.item()
																	lowercase_      :    List[str]       		= optimizer.state_dict()
																	self.assertEqual(lowercase_       ,    lowercase_					)
																	self.assertEqual(lowercase_       ,    lowercase_					)
																	self.assertEqual(lowercase_       ,    lowercase_					)

																	lowercase_      :    Optional[int]       		= train(2       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	# Save everything
																	accelerator.save_state()

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    List[str]       		= torch.tensor([1, 2, 3]					)
										lowercase_      :    Any       		= torch.tensor([2, 3, 4]					)
										lowercase_      :    Tuple       		= DummyModel()
										lowercase_      :    str       		= torch.optim.Adam(net.parameters()					)
										lowercase_      :    Optional[Any]       		= Accelerator()
										with self.assertRaises(lowercase_					) as ve:
																	accelerator.register_for_checkpointing(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
										lowercase_      :    Optional[int]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										with tempfile.TemporaryDirectory() as tmpdir:
																	set_seed(42					)
																	lowercase_      :    Any       		= DummyModel()
																	lowercase_      :    Union[str, Any]       		= torch.optim.Adam(params=model.parameters()       ,    lr=1E-3					)
																	lowercase_      :    int       		= torch.optim.lr_scheduler.StepLR(lowercase_       ,    step_size=1       ,    gamma=0.99					)
																	lowercase_		,		lowercase_      :    List[Any]       		= dummy_dataloaders()
																	lowercase_      :    List[str]       		= ProjectConfiguration(automatic_checkpoint_naming=lowercase_					)
																	# Train baseline
																	lowercase_      :    Union[str, Any]       		= Accelerator(project_dir=lowercase_       ,    project_config=lowercase_					)
																	lowercase_		,		lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    Tuple       		= accelerator.prepare(
																	    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	# Save initial
																	accelerator.save_state()
																	lowercase_      :    Dict       		= scheduler.state_dict()
																	train(3       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_					)
																	self.assertNotEqual(lowercase_       ,    scheduler.state_dict()					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										with tempfile.TemporaryDirectory() as tmpdir:
																	set_seed(42					)
																	lowercase_      :    Dict       		= DummyModel()
																	lowercase_      :    Optional[int]       		= ProjectConfiguration(automatic_checkpoint_naming=lowercase_       ,    total_limit=2					)
																	# Train baseline
																	lowercase_      :    Optional[Any]       		= Accelerator(project_dir=lowercase_       ,    project_config=lowercase_					)
																	lowercase_      :    str       		= accelerator.prepare(lowercase_					)
																	# Save 3 states:
																	for _ in range(11					):
																								accelerator.save_state()
																	self.assertTrue(not os.path.exists(os.path.join(lowercase_       ,    """checkpoints"""       ,    """checkpoint_0"""					)					)					)
																	self.assertTrue(os.path.exists(os.path.join(lowercase_       ,    """checkpoints"""       ,    """checkpoint_9"""					)					)					)
																	self.assertTrue(os.path.exists(os.path.join(lowercase_       ,    """checkpoints"""       ,    """checkpoint_10"""					)					)					)







			@require_cuda
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    List[str]       		= ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__					)]
										execute_subprocess_async(lowercase_       ,    env=os.environ.copy()					)


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

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowercase		:				Optional[Any]	       =		argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
_lowercase		:				Dict	       =		parser.parse_args()


_lowercase		:				Dict	       =		"cpu"
_lowercase		:				str	       =		"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

_lowercase		:				Any	       =		"path-to-your-trained-model"
_lowercase		:				str	       =		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
						_lowercase		:				Optional[int]	       =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowercase		:				Any	       =		pipe.to(device)

# to channels last
_lowercase		:				Union[str, Any]	       =		pipe.unet.to(memory_format=torch.channels_last)
_lowercase		:				List[Any]	       =		pipe.vae.to(memory_format=torch.channels_last)
_lowercase		:				Union[str, Any]	       =		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
						_lowercase		:				Tuple	       =		pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowercase		:				int	       =		torch.randn(2, 4, 64, 64)
_lowercase		:				int	       =		torch.rand(1) * 999
_lowercase		:				Union[str, Any]	       =		torch.randn(2, 77, 768)
_lowercase		:				Optional[Any]	       =		(sample, timestep, encoder_hidden_status)
try:
						_lowercase		:				int	       =		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
						_lowercase		:				Optional[int]	       =		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase		:				List[Any]	       =		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase		:				str	       =		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
						_lowercase		:				int	       =		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowercase		:				int	       =		666
_lowercase		:				Any	       =		torch.Generator(device).manual_seed(seed)
_lowercase		:				int	       =		{"generator": generator}
if args.steps is not None:
						_lowercase		:				Optional[int]	       =		args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
						_lowercase		:				List[Any]	       =		pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")





