
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    :	Any						=						logging.getLogger(__name__)


def 			UpperCAmelCase_     (  __lowerCAmelCase=2		,    __lowerCAmelCase=3		,    __lowerCAmelCase=16		,    __lowerCAmelCase = 10		,    __lowerCAmelCase = 2		)       ->  Dict:

    def get_dataset(__lowerCAmelCase		):
        __lowercase						:	Any					=				torch.randn(batch_size * n_batches		,    1		)
        return TensorDataset(__UpperCamelCase		,    a * x + b + 0.1 * torch.randn(batch_size * n_batches		,    1		)		)

    __lowercase						:	Dict					=				get_dataset(__UpperCamelCase		)
    __lowercase						:	Dict					=				get_dataset(__UpperCamelCase		)
    __lowercase						:	Dict					=				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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=None		)       ->  Dict:
    __lowercase						:	Union[str, Any]					=				[]
    for epoch in range(__UpperCamelCase		):
        # Train quickly
        model.train()
        for batch in dataloader:
            __lowercase						:	Any					=				batch
            __lowercase						:	Dict					=				model(__UpperCamelCase		)
            __lowercase						:	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   __lowerCAmelCase		(		nn.Module					):



      """simple docstring"""







      def __init__(					self						:				Union[str, Any] ):
          super().__init__()
          __lowercase						:	str					=				nn.Parameter(torch.randn(1 ) )
          __lowercase						:	Dict					=				nn.Parameter(torch.randn(1 ) )




      def 						snake_case_	(					self						:				Dict ,				_snake_case						:				int ):
          return x * self.a + self.b






class   __lowerCAmelCase		(		unittest.TestCase					):



      """simple docstring"""







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

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




      def 						snake_case_	(					self						:				Any ):
          with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              __lowercase						:	Optional[Any]					=				DummyModel()
              __lowercase						:	List[Any]					=				torch.optim.Adam(params=model.parameters() ,				lr=1E-3 )
              __lowercase						:	Dict					=				dummy_dataloaders()
              __lowercase						:	Optional[Any]					=				ProjectConfiguration(automatic_checkpoint_naming=A_ )

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

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

              __lowercase						:	Optional[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)						:	List[str]					=				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_ )
              self.assertEqual(A_ ,				A_ )




      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	Union[str, Any]					=				torch.tensor([1, 2, 3] )
          __lowercase						:	Any					=				torch.tensor([2, 3, 4] )
          __lowercase						:	Any					=				DummyModel()
          __lowercase						:	Dict					=				torch.optim.Adam(net.parameters() )
          __lowercase						:	str					=				Accelerator()
          with self.assertRaises(A_ ) as ve:
              accelerator.register_for_checkpointing(A_ ,				A_ ,				A_ ,				A_ )
          __lowercase						:	str					=				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 						snake_case_	(					self						:				Any ):
          with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              __lowercase						:	Optional[Any]					=				DummyModel()
              __lowercase						:	str					=				torch.optim.Adam(params=model.parameters() ,				lr=1E-3 )
              __lowercase						:	Optional[int]					=				torch.optim.lr_scheduler.StepLR(A_ ,				step_size=1 ,				gamma=0.99 )
              __lowercase						:	str					=				dummy_dataloaders()
              __lowercase						:	List[str]					=				ProjectConfiguration(automatic_checkpoint_naming=A_ )
              # Train baseline
              __lowercase						:	Optional[int]					=				Accelerator(project_dir=A_ ,				project_config=A_ )
              __lowercase						:	Any					=				accelerator.prepare(
                  A_ ,				A_ ,				A_ ,				A_ ,				A_ )
              # Save initial
              accelerator.save_state()
              __lowercase						:	Union[str, 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 						snake_case_	(					self						:				Optional[Any] ):
          with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              __lowercase						:	List[str]					=				DummyModel()
              __lowercase						:	Any					=				ProjectConfiguration(automatic_checkpoint_naming=A_ ,				total_limit=2 )
              # Train baseline
              __lowercase						:	Optional[int]					=				Accelerator(project_dir=A_ ,				project_config=A_ )
              __lowercase						:	List[Any]					=				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 						snake_case_	(					self						:				List[Any] ):
          __lowercase						:	Optional[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__":
 __lowerCAmelCase    :	Optional[int]						=						'''/tmp/accelerate/state_checkpointing'''
 __lowerCAmelCase    :	List[str]						=						DummyModel()
 __lowerCAmelCase    :	Optional[Any]						=						torch.optim.Adam(params=model.parameters(), lr=1e-3)
 __lowerCAmelCase    :	List[str]						=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
 __lowerCAmelCase    :	Optional[Any]						=						dummy_dataloaders()
 __lowerCAmelCase    :	Any						=						ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 __lowerCAmelCase    :	List[str]						=						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    :	Any						=						accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 __lowerCAmelCase    :	Dict						=						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    :	List[str]						=						group['''params'''][0].device
  break
 assert param_device.type == accelerator.device.type
 __lowerCAmelCase    :	Optional[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:
  __lowerCAmelCase    :	List[str]						=						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    :	Optional[int]						=						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()







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__  :  List[str]								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''maskformer-swin'''

							_a			      = {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(       self     :       Union[str, Any]				,				A_     :       Dict=2_2_4				,				A_     :       Optional[Any]=4				,				A_     :       List[str]=3				,				A_     :       str=9_6				,				A_     :       Optional[Any]=[2, 2, 6, 2]				,				A_     :       Tuple=[3, 6, 1_2, 2_4]				,				A_     :       List[Any]=7				,				A_     :       List[Any]=4.0				,				A_     :       List[str]=True				,				A_     :       Dict=0.0				,				A_     :       int=0.0				,				A_     :       str=0.1				,				A_     :       Optional[int]="gelu"				,				A_     :       List[Any]=False				,				A_     :       int=0.02				,				A_     :       int=1e-5				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				**A_     :       List[Any]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     Dict									= image_size
									lowerCAmelCase_			:     Optional[Any]									= patch_size
									lowerCAmelCase_			:     Tuple									= num_channels
									lowerCAmelCase_			:     Any									= embed_dim
									lowerCAmelCase_			:     List[str]									= depths
									lowerCAmelCase_			:     Union[str, Any]									= len(A_)
									lowerCAmelCase_			:     List[str]									= num_heads
									lowerCAmelCase_			:     Dict									= window_size
									lowerCAmelCase_			:     Optional[int]									= mlp_ratio
									lowerCAmelCase_			:     Dict									= qkv_bias
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[int]									= drop_path_rate
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     str									= use_absolute_embeddings
									lowerCAmelCase_			:     List[str]									= layer_norm_eps
									lowerCAmelCase_			:     int									= initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase_			:     str									= int(embed_dim * 2 ** (len(A_) - 1))
									lowerCAmelCase_			:     Optional[Any]									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     int									= get_aligned_output_features_output_indices(
									    out_features=A_				,				out_indices=A_				,				stage_names=self.stage_names)





