




"""simple docstring"""






from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)




@flax.struct.dataclass
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = 42




class   a__			(       nn.Module  ):
						snake_case__             = 42
						snake_case__             = (1_6, 3_2, 9_6, 2_5_6)
						snake_case__             = jnp.floataa





						def       __UpperCamelCase     (							self      :	Tuple)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  nn.Conv(
											    self.block_out_channels[0]     ,kernel_size=(3, 3)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)

											_lowerCAmelCase:Tuple									=  []
											for i in range(len(self.block_out_channels) - 1):
																_lowerCAmelCase:Optional[int]									=  self.block_out_channels[i]
																_lowerCAmelCase:List[str]									=  self.block_out_channels[i + 1]
																_lowerCAmelCase:List[str]									=  nn.Conv(
																    a__     ,kernel_size=(3, 3)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)
																blocks.append(a__)
																_lowerCAmelCase:List[str]									=  nn.Conv(
																    a__     ,kernel_size=(3, 3)     ,strides=(2, 2)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)
																blocks.append(a__)
											_lowerCAmelCase:int									=  blocks

											_lowerCAmelCase:Dict									=  nn.Conv(
											    self.conditioning_embedding_channels     ,kernel_size=(3, 3)     ,padding=((1, 1), (1, 1))     ,kernel_init=nn.initializers.zeros_init()     ,bias_init=nn.initializers.zeros_init()     ,dtype=self.dtype     ,)







						def __call__(							self      :	Optional[int]     ,a__      :	Tuple)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.conv_in(a__)
											_lowerCAmelCase:Tuple									=  nn.silu(a__)

											for block in self.blocks:
																_lowerCAmelCase:str									=  block(a__)
																_lowerCAmelCase:List[Any]									=  nn.silu(a__)

											_lowerCAmelCase:List[str]									=  self.conv_out(a__)

											return embedding




@flax_register_to_config
class   a__			(       nn.Module					,	UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 3_2
						snake_case__             = 4
						snake_case__             = (
						    "CrossAttnDownBlock2D",
						    "CrossAttnDownBlock2D",
						    "CrossAttnDownBlock2D",
						    "DownBlock2D",
						)
						snake_case__             = False
						snake_case__             = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
						snake_case__             = 2
						snake_case__             = 8
						snake_case__             = None
						snake_case__             = 1_2_8_0
						snake_case__             = 0.0
						snake_case__             = False
						snake_case__             = jnp.floataa
						snake_case__             = True
						snake_case__             = 0
						snake_case__             = "rgb"
						snake_case__             = (1_6, 3_2, 9_6, 2_5_6)





						def       __UpperCamelCase     (							self      :	str     ,a__      :	jax.random.KeyArray)						->       FrozenDict:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  (1, self.in_channels, self.sample_size, self.sample_size)
											_lowerCAmelCase:Optional[int]									=  jnp.zeros(a__     ,dtype=jnp.floataa)
											_lowerCAmelCase:Optional[Any]									=  jnp.ones((1,)     ,dtype=jnp.intaa)
											_lowerCAmelCase:List[str]									=  jnp.zeros((1, 1, self.cross_attention_dim)     ,dtype=jnp.floataa)
											_lowerCAmelCase:List[str]									=  (1, 3, self.sample_size * 8, self.sample_size * 8)
											_lowerCAmelCase:Dict									=  jnp.zeros(a__     ,dtype=jnp.floataa)

											_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  jax.random.split(a__)
											_lowerCAmelCase:Optional[int]									=  {'''params''': params_rng, '''dropout''': dropout_rng}

											return self.init(a__     ,a__     ,a__     ,a__     ,a__)["params"]





						def       __UpperCamelCase     (							self      :	Any)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  self.block_out_channels
											_lowerCAmelCase:List[str]									=  block_out_channels[0] * 4

											# If `num_attention_heads` is not defined (which is the case for most models)
											# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
											# The reason for this behavior is to correct for incorrectly named variables that were introduced
											# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
											# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
											# which is why we correct for the naming here.
											_lowerCAmelCase:int									=  self.num_attention_heads or self.attention_head_dim

											# input
											_lowerCAmelCase:str									=  nn.Conv(
											    block_out_channels[0]     ,kernel_size=(3, 3)     ,strides=(1, 1)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)

											# time
											_lowerCAmelCase:Any									=  FlaxTimesteps(
											    block_out_channels[0]     ,flip_sin_to_cos=self.flip_sin_to_cos     ,freq_shift=self.config.freq_shift)
											_lowerCAmelCase:Any									=  FlaxTimestepEmbedding(a__     ,dtype=self.dtype)

											_lowerCAmelCase:Tuple									=  FlaxControlNetConditioningEmbedding(
											    conditioning_embedding_channels=block_out_channels[0]     ,block_out_channels=self.conditioning_embedding_out_channels     ,)

											_lowerCAmelCase:Dict									=  self.only_cross_attention
											if isinstance(a__     ,a__):
																_lowerCAmelCase:List[Any]									=  (only_cross_attention,) * len(self.down_block_types)

											if isinstance(a__     ,a__):
																_lowerCAmelCase:Optional[Any]									=  (num_attention_heads,) * len(self.down_block_types)

											# down
											_lowerCAmelCase:Tuple									=  []
											_lowerCAmelCase:Optional[Any]									=  []

											_lowerCAmelCase:str									=  block_out_channels[0]

											_lowerCAmelCase:str									=  nn.Conv(
											    a__     ,kernel_size=(1, 1)     ,padding='''VALID'''     ,kernel_init=nn.initializers.zeros_init()     ,bias_init=nn.initializers.zeros_init()     ,dtype=self.dtype     ,)
											controlnet_down_blocks.append(a__)

											for i, down_block_type in enumerate(self.down_block_types):
																_lowerCAmelCase:str									=  output_channel
																_lowerCAmelCase:Any									=  block_out_channels[i]
																_lowerCAmelCase:Optional[Any]									=  i == len(a__) - 1

																if down_block_type == "CrossAttnDownBlock2D":
																					_lowerCAmelCase:Dict									=  FlaxCrossAttnDownBlockaD(
																					    in_channels=a__     ,out_channels=a__     ,dropout=self.dropout     ,num_layers=self.layers_per_block     ,num_attention_heads=num_attention_heads[i]     ,add_downsample=not is_final_block     ,use_linear_projection=self.use_linear_projection     ,only_cross_attention=only_cross_attention[i]     ,dtype=self.dtype     ,)
																else:
																					_lowerCAmelCase:Dict									=  FlaxDownBlockaD(
																					    in_channels=a__     ,out_channels=a__     ,dropout=self.dropout     ,num_layers=self.layers_per_block     ,add_downsample=not is_final_block     ,dtype=self.dtype     ,)

																down_blocks.append(a__)

																for _ in range(self.layers_per_block):
																					_lowerCAmelCase:Dict									=  nn.Conv(
																					    a__     ,kernel_size=(1, 1)     ,padding='''VALID'''     ,kernel_init=nn.initializers.zeros_init()     ,bias_init=nn.initializers.zeros_init()     ,dtype=self.dtype     ,)
																					controlnet_down_blocks.append(a__)

																if not is_final_block:
																					_lowerCAmelCase:str									=  nn.Conv(
																					    a__     ,kernel_size=(1, 1)     ,padding='''VALID'''     ,kernel_init=nn.initializers.zeros_init()     ,bias_init=nn.initializers.zeros_init()     ,dtype=self.dtype     ,)
																					controlnet_down_blocks.append(a__)

											_lowerCAmelCase:Optional[int]									=  down_blocks
											_lowerCAmelCase:int									=  controlnet_down_blocks

											# mid
											_lowerCAmelCase:Optional[Any]									=  block_out_channels[-1]
											_lowerCAmelCase:Union[str, Any]									=  FlaxUNetMidBlockaDCrossAttn(
											    in_channels=a__     ,dropout=self.dropout     ,num_attention_heads=num_attention_heads[-1]     ,use_linear_projection=self.use_linear_projection     ,dtype=self.dtype     ,)

											_lowerCAmelCase:Union[str, Any]									=  nn.Conv(
											    a__     ,kernel_size=(1, 1)     ,padding='''VALID'''     ,kernel_init=nn.initializers.zeros_init()     ,bias_init=nn.initializers.zeros_init()     ,dtype=self.dtype     ,)







						def __call__(							self      :	Optional[Any]     ,a__      :	List[str]     ,a__      :	Dict     ,a__      :	Optional[int]     ,a__      :	Tuple     ,a__      :	float = 1.0     ,a__      :	bool = True     ,a__      :	bool = False     ,)						->       Union[FlaxControlNetOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.controlnet_conditioning_channel_order
											if channel_order == "bgr":
																_lowerCAmelCase:List[str]									=  jnp.flip(a__     ,axis=1)

											# 1. time
											if not isinstance(a__     ,jnp.ndarray):
																_lowerCAmelCase:Optional[Any]									=  jnp.array([timesteps]     ,dtype=jnp.intaa)
											elif isinstance(a__     ,jnp.ndarray) and len(timesteps.shape) == 0:
																_lowerCAmelCase:Optional[int]									=  timesteps.astype(dtype=jnp.floataa)
																_lowerCAmelCase:Any									=  jnp.expand_dims(a__     ,0)

											_lowerCAmelCase:List[str]									=  self.time_proj(a__)
											_lowerCAmelCase:Union[str, Any]									=  self.time_embedding(a__)

											# 2. pre-process
											_lowerCAmelCase:Any									=  jnp.transpose(a__     ,(0, 2, 3, 1))
											_lowerCAmelCase:List[Any]									=  self.conv_in(a__)

											_lowerCAmelCase:Optional[int]									=  jnp.transpose(a__     ,(0, 2, 3, 1))
											_lowerCAmelCase:Any									=  self.controlnet_cond_embedding(a__)
											sample += controlnet_cond

											# 3. down
											_lowerCAmelCase:Tuple									=  (sample,)
											for down_block in self.down_blocks:
																if isinstance(a__     ,a__):
																					_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  down_block(a__     ,a__     ,a__     ,deterministic=not train)
																else:
																					_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  down_block(a__     ,a__     ,deterministic=not train)
																down_block_res_samples += res_samples

											# 4. mid
											_lowerCAmelCase:Union[str, Any]									=  self.mid_block(a__     ,a__     ,a__     ,deterministic=not train)

											# 5. contronet blocks
											_lowerCAmelCase:Union[str, Any]									=  ()
											for down_block_res_sample, controlnet_block in zip(a__     ,self.controlnet_down_blocks):
																_lowerCAmelCase:Optional[int]									=  controlnet_block(a__)
																controlnet_down_block_res_samples += (down_block_res_sample,)

											_lowerCAmelCase:List[Any]									=  controlnet_down_block_res_samples

											_lowerCAmelCase:str									=  self.controlnet_mid_block(a__)

											# 6. scaling
											_lowerCAmelCase:Tuple									=  [sample * conditioning_scale for sample in down_block_res_samples]
											mid_block_res_sample *= conditioning_scale

											if not return_dict:
																return (down_block_res_samples, mid_block_res_sample)

											return FlaxControlNetOutput(
											    down_block_res_samples=a__     ,mid_block_res_sample=a__)












"""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


UpperCamelCase__						=			logging.getLogger(__name__)





def 							UpperCAmelCase		(		snake_case			:					List[Any]=2      ,       snake_case			:					int=3      ,       snake_case			:					Optional[int]=16      ,       snake_case			:					int = 10      ,       snake_case			:					int = 2    ):

					def get_dataset(snake_case			:					Any    ):
										_lowerCAmelCase:Union[str, Any]									=  torch.randn(batch_size * n_batches      ,       1    )
										return TensorDataset(snake_case      ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,       1    )    )

					_lowerCAmelCase:Tuple									=  get_dataset(snake_case    )
					_lowerCAmelCase:List[Any]									=  get_dataset(snake_case    )
					_lowerCAmelCase:Dict									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					_lowerCAmelCase:Any									=  DataLoader(snake_case      ,       shuffle=snake_case      ,       batch_size=snake_case      ,       num_workers=4    )
					return (train_dataloader, valid_dataloader)





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					int      ,       snake_case			:					Dict      ,       snake_case			:					Dict      ,       snake_case			:					int      ,       snake_case			:					Tuple=None    ):
					_lowerCAmelCase:Optional[Any]									=  []
					for epoch in range(snake_case    ):
										# Train quickly
										model.train()
										for batch in dataloader:
															_lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  batch
															_lowerCAmelCase:int									=  model(snake_case    )
															_lowerCAmelCase:Optional[int]									=  torch.nn.functional.mse_loss(snake_case      ,       snake_case    )
															accelerator.backward(snake_case    )
															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      :	List[Any])						->       Any:




											"""simple docstring"""







											super().__init__()
											_lowerCAmelCase:str									=  nn.Parameter(torch.randn(1))
											_lowerCAmelCase:Dict									=  nn.Parameter(torch.randn(1))







						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	List[Any])						->       Tuple:




											"""simple docstring"""







											return x * self.a + self.b




class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	Any)						->       Optional[Any]:




											"""simple docstring"""







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

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





						def       __UpperCamelCase     (							self      :	Any)						->       Union[str, Any]:




											"""simple docstring"""







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

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

																_lowerCAmelCase:str									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																_lowerCAmelCase:Optional[Any]									=  os.path.join(a__     ,'''checkpoint''')
																accelerator.save_state(a__)

																# Load everything back in and make sure all states work
																accelerator.load_state(a__)
																test_rands += train(1     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):List[Any]									=  model.a.item(), model.b.item()
																_lowerCAmelCase:List[Any]									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42)
																_lowerCAmelCase:List[str]									=  DummyModel()
																_lowerCAmelCase:int									=  torch.optim.Adam(params=model.parameters()     ,lr=1E-3)
																_lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  dummy_dataloaders()
																_lowerCAmelCase:Any									=  ProjectConfiguration(automatic_checkpoint_naming=a__)

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

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

																_lowerCAmelCase:Dict									=  train(2     ,a__     ,a__     ,a__     ,a__)
																# Save everything
																accelerator.save_state()

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(a__     ,'''checkpoints'''     ,'''checkpoint_1'''))
																test_rands += train(1     ,a__     ,a__     ,a__     ,a__)
																((_lowerCAmelCase)							,   (_lowerCAmelCase)):Dict									=  model.a.item(), model.b.item()
																_lowerCAmelCase:Union[str, Any]									=  optimizer.state_dict()
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)
																self.assertEqual(a__     ,a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.tensor([1, 2, 3])
											_lowerCAmelCase:Tuple									=  torch.tensor([2, 3, 4])
											_lowerCAmelCase:Tuple									=  DummyModel()
											_lowerCAmelCase:Union[str, Any]									=  torch.optim.Adam(net.parameters())
											_lowerCAmelCase:Union[str, Any]									=  Accelerator()
											with self.assertRaises(a__) as ve:
																accelerator.register_for_checkpointing(a__     ,a__     ,a__     ,a__)
											_lowerCAmelCase:List[Any]									=  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       __UpperCamelCase     (							self      :	str)						->       Optional[Any]:




											"""simple docstring"""







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

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





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







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







						@require_cuda
						def       __UpperCamelCase     (							self      :	int)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
											execute_subprocess_async(a__     ,env=os.environ.copy())


if __name__ == "__main__":
				UpperCamelCase__						=			'''/tmp/accelerate/state_checkpointing'''
				UpperCamelCase__						=			DummyModel()
				UpperCamelCase__						=			torch.optim.Adam(params=model.parameters(), lr=1e-3)
				UpperCamelCase__						=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
				UpperCamelCase__						,							UpperCamelCase__						=			dummy_dataloaders()
				UpperCamelCase__						=			ProjectConfiguration(automatic_checkpoint_naming=True)
				# Train baseline
				UpperCamelCase__						=			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__						,							UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						,							UpperCamelCase__						=			accelerator.prepare(
				    model, optimizer, train_dataloader, valid_dataloader, scheduler
				)
				UpperCamelCase__						,							UpperCamelCase__						=			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__						=			group['''params'''][0].device
								break
				assert param_device.type == accelerator.device.type
				UpperCamelCase__						=			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__						=			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__						=			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()






