



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__	:   str=2	,					snake_case__	:   Any=3	,					snake_case__	:   str=16	,					snake_case__	:   int = 10	,					snake_case__	:   int = 2     )     ->					Optional[Any]:

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

   UpperCamelCase   :  List[Any]						     =     get_dataset(snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     get_dataset(snake_case__     )
   UpperCamelCase   :  List[str]						     =     DataLoader(snake_case__	,					shuffle=snake_case__	,					batch_size=snake_case__	,					num_workers=4     )
   UpperCamelCase   :  Tuple						     =     DataLoader(snake_case__	,					shuffle=snake_case__	,					batch_size=snake_case__	,					num_workers=4     )
   return (train_dataloader, valid_dataloader)
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[int]	,					snake_case__	:   Optional[Any]	,					snake_case__	:   int	,					snake_case__	:   Tuple	,					snake_case__	:   Optional[int]=None     )     ->					Union[str, Any]:
   UpperCamelCase   :  Union[str, Any]						     =     []
   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   :  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     lowerCAmelCase_		(  nn.Module							):
       def __init__(      self	)					->		Optional[int]:
          super().__init__()
          UpperCamelCase   :  List[str]						     =     nn.Parameter(torch.randn(1	)	)
          UpperCamelCase   :  Dict						     =     nn.Parameter(torch.randn(1	)	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return x * self.a + self.b






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

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir	)	),      1	)
       def  snake_case_	(      self	)					->		List[str]:
          with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase   :  Union[str, Any]						     =     DummyModel()
             UpperCamelCase   :  List[str]						     =     torch.optim.Adam(params=model.parameters(),      lr=1e-3	)
             UpperCamelCase     ,					UpperCamelCase   :  Dict						     =     dummy_dataloaders()
             # Train baseline
             UpperCamelCase   :  List[str]						     =     Accelerator()
             UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     accelerator.prepare(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             # Save initial
             UpperCamelCase   :  Dict						     =     os.path.join(SCREAMING_SNAKE_CASE_,      'initial'	)
             accelerator.save_state(SCREAMING_SNAKE_CASE_	)
             ((UpperCamelCase)     ,					(UpperCamelCase))   :  Dict						     =     model.a.item(), model.b.item()
             UpperCamelCase   :  int						     =     optimizer.state_dict()
             UpperCamelCase   :  Optional[int]						     =     train(3,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             ((UpperCamelCase)     ,					(UpperCamelCase))   :  Tuple						     =     model.a.item(), model.b.item()
             UpperCamelCase   :  Optional[int]						     =     optimizer.state_dict()

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

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

             # Load everything back in and make sure all states work
             accelerator.load_state(SCREAMING_SNAKE_CASE_	)
             test_rands += train(1,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             ((UpperCamelCase)     ,					(UpperCamelCase))   :  int						     =     model.a.item(), model.b.item()
             UpperCamelCase   :  int						     =     optimizer.state_dict()
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		str:
          with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase   :  List[Any]						     =     DummyModel()
             UpperCamelCase   :  Optional[Any]						     =     torch.optim.Adam(params=model.parameters(),      lr=1e-3	)
             UpperCamelCase     ,					UpperCamelCase   :  str						     =     dummy_dataloaders()
             UpperCamelCase   :  Any						     =     ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_	)

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

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

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

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_,      'checkpoints',      'checkpoint_1'	)	)
             test_rands += train(1,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             ((UpperCamelCase)     ,					(UpperCamelCase))   :  int						     =     model.a.item(), model.b.item()
             UpperCamelCase   :  str						     =     optimizer.state_dict()
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     torch.tensor([1, 2, 3]	)
          UpperCamelCase   :  Any						     =     torch.tensor([2, 3, 4]	)
          UpperCamelCase   :  Dict						     =     DummyModel()
          UpperCamelCase   :  Tuple						     =     torch.optim.Adam(net.parameters()	)
          UpperCamelCase   :  Optional[Any]						     =     Accelerator()
          with self.assertRaises(SCREAMING_SNAKE_CASE_	) as ve:
             accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[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	)					->		Dict:
          with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase   :  List[Any]						     =     DummyModel()
             UpperCamelCase   :  Tuple						     =     torch.optim.Adam(params=model.parameters(),      lr=1e-3	)
             UpperCamelCase   :  List[str]						     =     torch.optim.lr_scheduler.StepLR(SCREAMING_SNAKE_CASE_,      step_size=1,      gamma=0.99	)
             UpperCamelCase     ,					UpperCamelCase   :  Dict						     =     dummy_dataloaders()
             UpperCamelCase   :  str						     =     ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_	)
             # Train baseline
             UpperCamelCase   :  int						     =     Accelerator(project_dir=SCREAMING_SNAKE_CASE_,      project_config=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Any						     =     accelerator.prepare(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             # Save initial
             accelerator.save_state()
             UpperCamelCase   :  str						     =     scheduler.state_dict()
             train(3,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             self.assertNotEqual(SCREAMING_SNAKE_CASE_,      scheduler.state_dict()	)

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_,      'checkpoints',      'checkpoint_0'	)	)
             self.assertEqual(SCREAMING_SNAKE_CASE_,      scheduler.state_dict()	)
       def  snake_case_	(      self	)					->		Tuple:
          with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase   :  List[str]						     =     DummyModel()
             UpperCamelCase   :  int						     =     ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_,      total_limit=2	)
             # Train baseline
             UpperCamelCase   :  Tuple						     =     Accelerator(project_dir=SCREAMING_SNAKE_CASE_,      project_config=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Any						     =     accelerator.prepare(SCREAMING_SNAKE_CASE_	)
             # Save 3 states:
             for _ in range(11	):
                accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,      'checkpoints',      'checkpoint_0'	)	)	)
             self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,      'checkpoints',      'checkpoint_9'	)	)	)
             self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_,      'checkpoints',      'checkpoint_10'	)	)	)






       @require_cuda
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Tuple						     =     ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
          execute_subprocess_async(SCREAMING_SNAKE_CASE_,      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()








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



