




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

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

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


lowerCAmelCase_						=			logging.getLogger(__name__)






def  snake_case(    __magic_name__=2			,		__magic_name__=3			,		__magic_name__=16			,		__magic_name__ = 10			,		__magic_name__ = 2   )			->							Dict:





     '''simple docstring'''


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

     lowercase :       Dict       =			get_dataset(__magic_name__   )
     lowercase :       int       =			get_dataset(__magic_name__   )
     lowercase :       List[str]       =			DataLoader(__magic_name__			,		shuffle=__magic_name__			,		batch_size=__magic_name__			,		num_workers=4   )
     lowercase :       Dict       =			DataLoader(__magic_name__			,		shuffle=__magic_name__			,		batch_size=__magic_name__			,		num_workers=4   )
     return (train_dataloader, valid_dataloader)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=None   )			->							Tuple:





     '''simple docstring'''


     lowercase :       List[Any]       =			[]
     for epoch in range(__magic_name__   ):
          # Train quickly
          model.train()
          for batch in dataloader:
               lowercase      ,	lowercase :       Dict       =			batch
               lowercase :       List[str]       =			model(__magic_name__   )
               lowercase :       Optional[Any]       =			torch.nn.functional.mse_loss(__magic_name__			,		__magic_name__   )
               accelerator.backward(__magic_name__   )
               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		:     int				)		->   Optional[Any]:






       """simple docstring"""







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






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






       """simple docstring"""







       return x * self.a + self.b


class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
            set_seed(42				)
            lowercase :       Union[str, Any]       =			DummyModel()
            lowercase :       Union[str, Any]       =			torch.optim.Adam(params=model.parameters()				,						lr=1E-3				)
            lowercase      ,	lowercase :       str       =			dummy_dataloaders()
            lowercase :       List[Any]       =			ProjectConfiguration(total_limit=1				,						project_dir=_A				,						automatic_checkpoint_naming=_A				)
            # Train baseline
            lowercase :       List[Any]       =			Accelerator(project_config=_A				)
            lowercase      ,	lowercase      ,	lowercase      ,	lowercase :       Optional[Any]       =			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  __a      (  self		:     Optional[Any]				)		->   int:






       """simple docstring"""







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

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

            lowercase :       List[str]       =			train(2				,						_A				,						_A				,						_A				,						_A				)
            # Save everything
            lowercase :       Tuple       =			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				)
            ((lowercase)      ,	(lowercase)) :       Optional[Any]       =			model.a.item(), model.b.item()
            lowercase :       Optional[int]       =			optimizer.state_dict()
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Tuple				)		->   Any:






       """simple docstring"""







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

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

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

            lowercase :       Union[str, Any]       =			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				)
            ((lowercase)      ,	(lowercase)) :       Any       =			model.a.item(), model.b.item()
            lowercase :       Optional[int]       =			optimizer.state_dict()
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Dict				)		->   str:






       """simple docstring"""







       lowercase :       List[Any]       =			torch.tensor([1, 2, 3]				)
       lowercase :       List[Any]       =			torch.tensor([2, 3, 4]				)
       lowercase :       Optional[Any]       =			DummyModel()
       lowercase :       Optional[Any]       =			torch.optim.Adam(net.parameters()				)
       lowercase :       Any       =			Accelerator()
       with self.assertRaises(_A				) as ve:
            accelerator.register_for_checkpointing(_A				,						_A				,						_A				,						_A				)
       lowercase :       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  __a      (  self		:     List[Any]				)		->   Any:






       """simple docstring"""







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






       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
            set_seed(42				)
            lowercase :       List[str]       =			DummyModel()
            lowercase :       str       =			ProjectConfiguration(automatic_checkpoint_naming=_A				,						total_limit=2				)
            # Train baseline
            lowercase :       Dict       =			Accelerator(project_dir=_A				,						project_config=_A				)
            lowercase :       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  __a      (  self		:     Any				)		->   Optional[Any]:






       """simple docstring"""







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


if __name__ == "__main__":
 lowerCAmelCase_						=			'/tmp/accelerate/state_checkpointing'
 lowerCAmelCase_						=			DummyModel()
 lowerCAmelCase_						=			torch.optim.Adam(params=model.parameters(), lr=1E-3)
 lowerCAmelCase_						=			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
 lowerCAmelCase_				,					lowerCAmelCase_						=			dummy_dataloaders()
 lowerCAmelCase_						=			ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 lowerCAmelCase_						=			Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 lowerCAmelCase_				,					lowerCAmelCase_				,					lowerCAmelCase_				,					lowerCAmelCase_				,					lowerCAmelCase_						=			accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 lowerCAmelCase_				,					lowerCAmelCase_						=			accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  lowerCAmelCase_						=			group['params'][0].device
  break
 assert param_device.type == accelerator.device.type
 lowerCAmelCase_						=			model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

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

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
 for group in optimizer.param_groups:
  lowerCAmelCase_						=			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 copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase_						=			{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      '''maskformer'''
  _UpperCamelCase       :      Union[str, Any]			=      {'''hidden_size''': '''mask_feature_size'''}
  _UpperCamelCase       :      Dict			=      ['''resnet''', '''swin''']
  _UpperCamelCase       :      Optional[int]			=      ['''detr''']






  def __init__(  self		:     Any				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     float = 0.1				,						_A		:     bool = False				,						_A		:     Optional[Dict] = None				,						_A		:     Optional[Dict] = None				,						_A		:     float = 0.02				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 20.0				,						_A		:     Optional[bool] = None				,						**_A		:     str				,						)		->   str:






       """simple docstring"""







       if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            lowercase :       List[str]       =			SwinConfig(
                image_size=384				,						in_channels=3				,						patch_size=4				,						embed_dim=128				,						depths=[2, 2, 18, 2]				,						num_heads=[4, 8, 16, 32]				,						window_size=12				,						drop_path_rate=0.3				,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,						)

       if isinstance(_A				,						_A				):
            lowercase :       Optional[int]       =			backbone_config.pop('''model_type'''				)
            lowercase :       List[str]       =			CONFIG_MAPPING[backbone_model_type]
            lowercase :       Union[str, Any]       =			config_class.from_dict(_A				)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
                f"""Supported model types: {','.join(self.backbones_supported				)}"""				)

       if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            lowercase :       Any       =			DetrConfig()
       else:
            # verify that the decoder is supported
            lowercase :       Union[str, Any]       =			(
                decoder_config.pop('''model_type'''				) if isinstance(_A				,						_A				) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
                 raise ValueError(
                     f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                     f""" {','.join(self.decoders_supported				)}"""				)
            if isinstance(_A				,						_A				):
                 lowercase :       str       =			CONFIG_MAPPING[decoder_type]
                 lowercase :       Dict       =			config_class.from_dict(_A				)

       lowercase :       Tuple       =			backbone_config
       lowercase :       List[Any]       =			decoder_config
       # main feature dimension for the model
       lowercase :       Optional[int]       =			fpn_feature_size
       lowercase :       List[Any]       =			mask_feature_size
       # initializer
       lowercase :       Union[str, Any]       =			init_std
       lowercase :       Tuple       =			init_xavier_std
       # Hungarian matcher && loss
       lowercase :       List[str]       =			cross_entropy_weight
       lowercase :       int       =			dice_weight
       lowercase :       List[Any]       =			mask_weight
       lowercase :       Tuple       =			use_auxiliary_loss
       lowercase :       Tuple       =			no_object_weight
       lowercase :       int       =			output_auxiliary_logits

       lowercase :       List[Any]       =			self.decoder_config.encoder_attention_heads
       lowercase :       List[Any]       =			self.decoder_config.num_hidden_layers
       super().__init__(**_A				)






  @classmethod
  def  __a      (  cls		:     Optional[int]				,						_A		:     PretrainedConfig				,						_A		:     PretrainedConfig				,						**_A		:     str				)		->   Any:






       """simple docstring"""







       return cls(
           backbone_config=_A				,						decoder_config=_A				,						**_A				,						)






  def  __a      (  self		:     Optional[int]				)		->   Dict[str, any]:






       """simple docstring"""







       lowercase :       str       =			copy.deepcopy(self.__dict__				)
       lowercase :       Optional[Any]       =			self.backbone_config.to_dict()
       lowercase :       List[Any]       =			self.decoder_config.to_dict()
       lowercase :       Optional[int]       =			self.__class__.model_type
       return output