






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder



@dataclass
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    "DiagonalGaussianDistribution"





class   lowerCamelCase      (_snake_case ,  _snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  True



		@register_to_config
		def __init__(							self				,		_UpperCamelCase = 3				,		_UpperCamelCase = 3				,		_UpperCamelCase = ("DownEncoderBlock2D",)				,		_UpperCamelCase = ("UpDecoderBlock2D",)				,		_UpperCamelCase = (6_4,)				,		_UpperCamelCase = 1				,		_UpperCamelCase = "silu"				,		_UpperCamelCase = 4				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 0.1_82_15				,		)	->							List[Any]:
							super().__init__()

							# pass init params to Encoder
							UpperCAmelCase_    :      List[str]            =						Encoder(
							    in_channels=_UpperCamelCase				,		out_channels=_UpperCamelCase				,		down_block_types=_UpperCamelCase				,		block_out_channels=_UpperCamelCase				,		layers_per_block=_UpperCamelCase				,		act_fn=_UpperCamelCase				,		norm_num_groups=_UpperCamelCase				,		double_z=_UpperCamelCase				,		)

							# pass init params to Decoder
							UpperCAmelCase_    :      Dict            =						Decoder(
							    in_channels=_UpperCamelCase				,		out_channels=_UpperCamelCase				,		up_block_types=_UpperCamelCase				,		block_out_channels=_UpperCamelCase				,		layers_per_block=_UpperCamelCase				,		norm_num_groups=_UpperCamelCase				,		act_fn=_UpperCamelCase				,		)

							UpperCAmelCase_    :      Any            =						nn.Convad(2 * latent_channels				,		2 * latent_channels				,		1   )
							UpperCAmelCase_    :      List[Any]            =						nn.Convad(_UpperCamelCase				,		_UpperCamelCase				,		1   )

							UpperCAmelCase_    :      Any            =						False
							UpperCAmelCase_    :      int            =						False

							# only relevant if vae tiling is enabled
							UpperCAmelCase_    :      Optional[int]            =						self.config.sample_size
							UpperCAmelCase_    :      int            =						(
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size				,		(list, tuple)   )
							    else self.config.sample_size
							)
							UpperCAmelCase_    :      Union[str, Any]            =						int(sample_size / (2 ** (len(self.config.block_out_channels   ) - 1))   )
							UpperCAmelCase_    :      Optional[Any]            =						0.25



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False   )	->							List[str]:
							if isinstance(_UpperCamelCase				,		(Encoder, Decoder)   ):
												UpperCAmelCase_    :      Union[str, Any]            =						value



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase = True   )	->							int:
							UpperCAmelCase_    :      Tuple            =						use_tiling



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self.enable_tiling(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      str            =						True



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							UpperCAmelCase_    :      Optional[int]            =						False



		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def 					__UpperCAmelCase			(							self   )	->							Dict[str, AttentionProcessor]:
							UpperCAmelCase_    :      Optional[int]            =						{}

							def fn_recursive_add_processors(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
												if hasattr(_UpperCamelCase				,		'set_processor'   ):
																	UpperCAmelCase_    :      Optional[int]            =						module.processor

												for sub_name, child in module.named_children():
																	fn_recursive_add_processors(f"{name}.{sub_name}"				,		_UpperCamelCase				,		_UpperCamelCase   )

												return processors

							for name, module in self.named_children():
												fn_recursive_add_processors(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							return processors



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Union[str, Any]            =						len(self.attn_processors.keys()   )

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and len(_UpperCamelCase   ) != count:
												raise ValueError(
												    f"A dict of processors was passed, but the number of processors {len(_UpperCamelCase   )} does not match the"
												    f" number of attention layers: {count}. Please make sure to pass {count} processor classes."   )

							def fn_recursive_attn_processor(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
												if hasattr(_UpperCamelCase				,		'set_processor'   ):
																	if not isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
																						module.set_processor(_UpperCamelCase   )
																	else:
																						module.set_processor(processor.pop(f"{name}.processor"   )   )

												for sub_name, child in module.named_children():
																	fn_recursive_attn_processor(f"{name}.{sub_name}"				,		_UpperCamelCase				,		_UpperCamelCase   )

							for name, module in self.named_children():
												fn_recursive_attn_processor(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							self.set_attn_processor(AttnProcessor()   )



		@apply_forward_hook
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							AutoencoderKLOutput:
							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
												return self.tiled_encode(_UpperCamelCase				,		return_dict=_UpperCamelCase   )

							if self.use_slicing and x.shape[0] > 1:
												UpperCAmelCase_    :      Union[str, Any]            =						[self.encoder(_UpperCamelCase   ) for x_slice in x.split(1   )]
												UpperCAmelCase_    :      Tuple            =						torch.cat(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      List[Any]            =						self.encoder(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.quant_conv(_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						DiagonalGaussianDistribution(_UpperCamelCase   )

							if not return_dict:
												return (posterior,)

							return AutoencoderKLOutput(latent_dist=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							Union[DecoderOutput, torch.FloatTensor]:
							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
												return self.tiled_decode(_UpperCamelCase				,		return_dict=_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						self.post_quant_conv(_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						self.decoder(_UpperCamelCase   )

							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=_UpperCamelCase   )



		@apply_forward_hook
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							Union[DecoderOutput, torch.FloatTensor]:
							if self.use_slicing and z.shape[0] > 1:
												UpperCAmelCase_    :      List[str]            =						[self._decode(_UpperCamelCase   ).sample for z_slice in z.split(1   )]
												UpperCAmelCase_    :      Dict            =						torch.cat(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Any            =						self._decode(_UpperCamelCase   ).sample

							if not return_dict:
												return (decoded,)

							return DecoderOutput(sample=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      Tuple            =						min(a.shape[2]				,		b.shape[2]				,		_UpperCamelCase   )
							for y in range(_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Tuple            =						min(a.shape[3]				,		b.shape[3]				,		_UpperCamelCase   )
							for x in range(_UpperCamelCase   ):
												UpperCAmelCase_    :      int            =						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							AutoencoderKLOutput:
							UpperCAmelCase_    :      Any            =						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)   )
							UpperCAmelCase_    :      Tuple            =						int(self.tile_latent_min_size * self.tile_overlap_factor   )
							UpperCAmelCase_    :      Optional[int]            =						self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							UpperCAmelCase_    :      List[str]            =						[]
							for i in range(0				,		x.shape[2]				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Any            =						[]
												for j in range(0				,		x.shape[3]				,		_UpperCamelCase   ):
																	UpperCAmelCase_    :      Any            =						x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																	UpperCAmelCase_    :      Dict            =						self.encoder(_UpperCamelCase   )
																	UpperCAmelCase_    :      List[str]            =						self.quant_conv(_UpperCamelCase   )
																	row.append(_UpperCamelCase   )
												rows.append(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						[]
							for i, row in enumerate(_UpperCamelCase   ):
												UpperCAmelCase_    :      List[Any]            =						[]
												for j, tile in enumerate(_UpperCamelCase   ):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																						UpperCAmelCase_    :      Dict            =						self.blend_v(rows[i - 1][j]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	if j > 0:
																						UpperCAmelCase_    :      List[str]            =						self.blend_h(row[j - 1]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	result_row.append(tile[:, :, :row_limit, :row_limit]   )
												result_rows.append(torch.cat(_UpperCamelCase				,		dim=3   )   )

							UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_UpperCamelCase				,		dim=2   )
							UpperCAmelCase_    :      List[Any]            =						DiagonalGaussianDistribution(_UpperCamelCase   )

							if not return_dict:
												return (posterior,)

							return AutoencoderKLOutput(latent_dist=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = True   )	->							Union[DecoderOutput, torch.FloatTensor]:
							UpperCAmelCase_    :      str            =						int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)   )
							UpperCAmelCase_    :      Dict            =						int(self.tile_sample_min_size * self.tile_overlap_factor   )
							UpperCAmelCase_    :      Dict            =						self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							UpperCAmelCase_    :      Union[str, Any]            =						[]
							for i in range(0				,		z.shape[2]				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      List[str]            =						[]
												for j in range(0				,		z.shape[3]				,		_UpperCamelCase   ):
																	UpperCAmelCase_    :      List[str]            =						z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																	UpperCAmelCase_    :      Optional[Any]            =						self.post_quant_conv(_UpperCamelCase   )
																	UpperCAmelCase_    :      Tuple            =						self.decoder(_UpperCamelCase   )
																	row.append(_UpperCamelCase   )
												rows.append(_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						[]
							for i, row in enumerate(_UpperCamelCase   ):
												UpperCAmelCase_    :      List[Any]            =						[]
												for j, tile in enumerate(_UpperCamelCase   ):
																	# blend the above tile and the left tile
																	# to the current tile and add the current tile to the result row
																	if i > 0:
																						UpperCAmelCase_    :      Union[str, Any]            =						self.blend_v(rows[i - 1][j]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	if j > 0:
																						UpperCAmelCase_    :      Optional[Any]            =						self.blend_h(row[j - 1]				,		_UpperCamelCase				,		_UpperCamelCase   )
																	result_row.append(tile[:, :, :row_limit, :row_limit]   )
												result_rows.append(torch.cat(_UpperCamelCase				,		dim=3   )   )

							UpperCAmelCase_    :      Dict            =						torch.cat(_UpperCamelCase				,		dim=2   )
							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = False				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		)	->							Union[DecoderOutput, torch.FloatTensor]:
							UpperCAmelCase_    :      Optional[Any]            =						sample
							UpperCAmelCase_    :      Union[str, Any]            =						self.encode(_UpperCamelCase   ).latent_dist
							if sample_posterior:
												UpperCAmelCase_    :      str            =						posterior.sample(generator=_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      int            =						posterior.mode()
							UpperCAmelCase_    :      Dict            =						self.decode(_UpperCamelCase   ).sample

							if not return_dict:
												return (dec,)

							return DecoderOutput(sample=_UpperCamelCase   )














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      lowercase__      (     __snake_case    :							List[Any]=2     ,       __snake_case    :							Union[str, Any]=3     ,       __snake_case    :							Any=16     ,       __snake_case    :							int = 10     ,       __snake_case    :							int = 2  ):


					'''simple docstring'''


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

					UpperCAmelCase_    :      Any            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      str            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					return (train_dataloader, valid_dataloader)




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							str     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Any     ,       __snake_case    :							Tuple=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						[]
					for epoch in range(__snake_case  ):
										# Train quickly
										model.train()
										for batch in dataloader:
															UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						batch
															UpperCAmelCase_    :      List[Any]            =						model(__snake_case  )
															UpperCAmelCase_    :      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   lowerCamelCase      (nn.Module			):







		'''simple docstring'''



		def __init__(							self   )	->							Optional[Any]:
							super().__init__()
							UpperCAmelCase_    :      List[Any]            =						nn.Parameter(torch.randn(1   )   )
							UpperCAmelCase_    :      Optional[int]            =						nn.Parameter(torch.randn(1   )   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[Any]:
							return x * self.a + self.b





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[int]            =						ProjectConfiguration(total_limit=1				,		project_dir=_UpperCamelCase				,		automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Dict            =						Accelerator(project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()

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



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[Any]            =						DummyModel()
												UpperCAmelCase_    :      str            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												UpperCAmelCase_    :      Any            =						os.path.join(_UpperCamelCase				,		'initial'   )
												accelerator.save_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												UpperCAmelCase_    :      Union[str, Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Union[str, Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Any            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      int            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[Any]            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[str]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												UpperCAmelCase_    :      Union[str, Any]            =						os.path.join(_UpperCamelCase				,		'checkpoint'   )
												accelerator.save_state(_UpperCamelCase   )

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



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      Optional[int]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Any            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )

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

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      Any            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(iteration=1				,		automatic_checkpoint_naming=_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      str            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      List[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												accelerator.save_state()

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						torch.tensor([1, 2, 3]   )
							UpperCAmelCase_    :      Any            =						torch.tensor([2, 3, 4]   )
							UpperCAmelCase_    :      Union[str, Any]            =						DummyModel()
							UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(net.parameters()   )
							UpperCAmelCase_    :      Any            =						Accelerator()
							with self.assertRaises(_UpperCamelCase   ) as ve:
												accelerator.register_for_checkpointing(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      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 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_    :      Dict            =						torch.optim.lr_scheduler.StepLR(_UpperCamelCase				,		step_size=1				,		gamma=0.99   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												UpperCAmelCase_    :      Dict            =						scheduler.state_dict()
												train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												self.assertNotEqual(_UpperCamelCase				,		scheduler.state_dict()   )

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[int]            =						DummyModel()
												UpperCAmelCase_    :      Dict            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase				,		total_limit=2   )
												# Train baseline
												UpperCAmelCase_    :      Optional[int]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_    :      str            =						accelerator.prepare(_UpperCamelCase   )
												# Save 3 states:
												for _ in range(1_1   ):
																	accelerator.save_state()
												self.assertTrue(not os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_9'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_10'   )   )   )



		@require_cuda
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[str]            =						['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
							execute_subprocess_async(_UpperCamelCase				,		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=0.9_9)
		__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()






