





'''simple docstring'''

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

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

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


lowercase__						:      int	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Union[str, Any]=2, lowercase :					List[str]=3, lowercase :					Optional[int]=16, lowercase :					int = 10, lowercase :					int = 2				)		->		int:





     """simple docstring"""

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

     _UpperCamelCase       =	get_dataset(lowercase				)
     _UpperCamelCase       =	get_dataset(lowercase				)
     _UpperCamelCase       =	DataLoader(lowercase, shuffle=lowercase, batch_size=lowercase, num_workers=4				)
     _UpperCamelCase       =	DataLoader(lowercase, shuffle=lowercase, batch_size=lowercase, num_workers=4				)
     return (train_dataloader, valid_dataloader)







def  a__    (    lowercase :					str, lowercase :					int, lowercase :					Union[str, Any], lowercase :					List[Any], lowercase :					Optional[Any], lowercase :					Tuple=None				)		->		Optional[Any]:





     """simple docstring"""

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

            '''simple docstring'''




            super().__init__()
            _UpperCamelCase       =	nn.Parameter(torch.randn(1					)					)
            _UpperCamelCase       =	nn.Parameter(torch.randn(1					)					)







       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		str					)   ->					List[str]:

            '''simple docstring'''




            return x * self.a + self.b

class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Optional[int]					)   ->					Union[str, Any]:

            '''simple docstring'''




            with tempfile.TemporaryDirectory() as tmpdir:
                 set_seed(42					)
                 _UpperCamelCase       =	DummyModel()
                 _UpperCamelCase       =	torch.optim.Adam(params=model.parameters()	,			lr=1e-3					)
                 _UpperCamelCase   ,    _UpperCamelCase       =	dummy_dataloaders()
                 _UpperCamelCase       =	ProjectConfiguration(total_limit=1	,			project_dir=lowerCAmelCase__	,			automatic_checkpoint_naming=lowerCAmelCase__					)
                 # Train baseline
                 _UpperCamelCase       =	Accelerator(project_config=lowerCAmelCase__					)
                 _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
                     lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 # 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]					)   ->					Optional[int]:

            '''simple docstring'''




            with tempfile.TemporaryDirectory() as tmpdir:
                 set_seed(42					)
                 _UpperCamelCase       =	DummyModel()
                 _UpperCamelCase       =	torch.optim.Adam(params=model.parameters()	,			lr=1e-3					)
                 _UpperCamelCase   ,    _UpperCamelCase       =	dummy_dataloaders()
                 # Train baseline
                 _UpperCamelCase       =	Accelerator()
                 _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
                     lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 # Save initial
                 _UpperCamelCase       =	os.path.join(lowerCAmelCase__	,			'''initial'''					)
                 accelerator.save_state(lowerCAmelCase__					)
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()
                 _UpperCamelCase       =	train(3	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()

                 # Train partially
                 set_seed(42					)
                 _UpperCamelCase       =	DummyModel()
                 _UpperCamelCase       =	torch.optim.Adam(params=model.parameters()	,			lr=1e-3					)
                 _UpperCamelCase   ,    _UpperCamelCase       =	dummy_dataloaders()
                 _UpperCamelCase       =	Accelerator()
                 _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
                     lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 accelerator.load_state(lowerCAmelCase__					)
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

                 _UpperCamelCase       =	train(2	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 # Save everything
                 _UpperCamelCase       =	os.path.join(lowerCAmelCase__	,			'''checkpoint'''					)
                 accelerator.save_state(lowerCAmelCase__					)

                 # Load everything back in and make sure all states work
                 accelerator.load_state(lowerCAmelCase__					)
                 test_rands += train(1	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					int:

            '''simple docstring'''




            with tempfile.TemporaryDirectory() as tmpdir:
                 set_seed(42					)
                 _UpperCamelCase       =	DummyModel()
                 _UpperCamelCase       =	torch.optim.Adam(params=model.parameters()	,			lr=1e-3					)
                 _UpperCamelCase   ,    _UpperCamelCase       =	dummy_dataloaders()
                 _UpperCamelCase       =	ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__					)

                 # Train baseline
                 _UpperCamelCase       =	Accelerator(project_dir=lowerCAmelCase__	,			project_config=lowerCAmelCase__					)
                 _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
                     lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 # Save initial
                 accelerator.save_state()
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()
                 _UpperCamelCase       =	train(3	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()

                 # Train partially
                 set_seed(42					)
                 _UpperCamelCase       =	DummyModel()
                 _UpperCamelCase       =	torch.optim.Adam(params=model.parameters()	,			lr=1e-3					)
                 _UpperCamelCase   ,    _UpperCamelCase       =	dummy_dataloaders()
                 _UpperCamelCase       =	ProjectConfiguration(iteration=1	,			automatic_checkpoint_naming=lowerCAmelCase__					)
                 _UpperCamelCase       =	Accelerator(project_dir=lowerCAmelCase__	,			project_config=lowerCAmelCase__					)
                 _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
                     lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 accelerator.load_state(os.path.join(lowerCAmelCase__	,			'''checkpoints'''	,			'''checkpoint_0'''					)					)
                 ((_UpperCamelCase)   ,    (_UpperCamelCase))       =	model.a.item(), model.b.item()
                 _UpperCamelCase       =	optimizer.state_dict()
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

                 _UpperCamelCase       =	train(2	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 # Save everything
                 accelerator.save_state()

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





       def snake_case__						(							self       :		Any					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	torch.tensor([1, 2, 3]					)
            _UpperCamelCase       =	torch.tensor([2, 3, 4]					)
            _UpperCamelCase       =	DummyModel()
            _UpperCamelCase       =	torch.optim.Adam(net.parameters()					)
            _UpperCamelCase       =	Accelerator()
            with self.assertRaises(lowerCAmelCase__					) as ve:
                 accelerator.register_for_checkpointing(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            _UpperCamelCase       =	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					)   ->					Tuple:

            '''simple docstring'''




            with tempfile.TemporaryDirectory() as tmpdir:
                 set_seed(42					)
                 _UpperCamelCase       =	DummyModel()
                 _UpperCamelCase       =	torch.optim.Adam(params=model.parameters()	,			lr=1e-3					)
                 _UpperCamelCase       =	torch.optim.lr_scheduler.StepLR(lowerCAmelCase__	,			step_size=1	,			gamma=0.99					)
                 _UpperCamelCase   ,    _UpperCamelCase       =	dummy_dataloaders()
                 _UpperCamelCase       =	ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__					)
                 # Train baseline
                 _UpperCamelCase       =	Accelerator(project_dir=lowerCAmelCase__	,			project_config=lowerCAmelCase__					)
                 _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
                     lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 # Save initial
                 accelerator.save_state()
                 _UpperCamelCase       =	scheduler.state_dict()
                 train(3	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                 self.assertNotEqual(lowerCAmelCase__	,			scheduler.state_dict()					)

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





       def snake_case__						(							self       :		List[str]					)   ->					Tuple:

            '''simple docstring'''




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







       @require_cuda
       def snake_case__						(							self       :		Union[str, Any]					)   ->					Any:

            '''simple docstring'''




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


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










'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey







def  a__    (    lowercase :					str				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	img.shape[0], img.shape[1]

     # converting each pixel's color to its negative
     for i in range(lowercase				):
          for j in range(lowercase				):
               _UpperCamelCase       =	[255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
   # read original image
   lowercase__						:      Optional[int]	=    imread('image_data/lena.jpg', 1)

   # convert to its negative
   lowercase__						:      Union[str, Any]	=    convert_to_negative(img)

   # show result image
   imshow('negative of original image', img)
   waitKey(0)
   destroyAllWindows()



