'''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


_A								=   logging.getLogger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_=2  ,       SCREAMING_SNAKE_CASE_=3  ,       SCREAMING_SNAKE_CASE_=16  ,       SCREAMING_SNAKE_CASE_ = 10  ,       SCREAMING_SNAKE_CASE_ = 2							):

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

     lowercase_					:       Dict     				=					get_dataset(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					get_dataset(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)
     lowercase_					:       List[str]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)
     return (train_dataloader, valid_dataloader)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=None							):
     lowercase_					:       Any     				=					[]
     for epoch in range(SCREAMING_SNAKE_CASE_							):
          # Train quickly
          model.train()
          for batch in dataloader:
               lowercase_				,lowercase_					:       List[str]     				=					batch
               lowercase_					:       Dict     				=					model(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Dict     				=					torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               accelerator.backward(SCREAMING_SNAKE_CASE_							)
               optimizer.step()
               optimizer.zero_grad()
          rands.append(random.random()							)  # Introduce some randomness
          if scheduler is not None:
               scheduler.step()
     return rands


class       UpperCAmelCase__  (       nn.Module			):






   """simple docstring"""
   def __init__(self				)    ->     Tuple:
        super().__init__()
        lowercase_					:       Any     				=					nn.Parameter(torch.randn(1				)				)
        lowercase_					:       Any     				=					nn.Parameter(torch.randn(1				)				)





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



class       UpperCAmelCase__  (       unittest.TestCase			):






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

             # Train partially
             set_seed(42				)
             lowercase_					:       Optional[int]     				=					DummyModel()
             lowercase_					:       List[Any]     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_				,lowercase_					:       Dict     				=					dummy_dataloaders()
             lowercase_					:       Optional[int]     				=					Accelerator()
             lowercase_				,lowercase_				,lowercase_				,lowercase_					:       Optional[Any]     				=					accelerator.prepare(
                 _a       ,				_a       ,				_a       ,				_a				)
             accelerator.load_state(_a				)
             ((lowercase_)				,(lowercase_))					:       List[Any]     				=					model.a.item(), model.b.item()
             lowercase_					:       str     				=					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_					:       int     				=					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_))					:       Any     				=					model.a.item(), model.b.item()
             lowercase_					:       str     				=					optimizer.state_dict()
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
             self.assertEqual(_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42				)
             lowercase_					:       str     				=					DummyModel()
             lowercase_					:       str     				=					torch.optim.Adam(params=model.parameters()       ,				lr=1e-3				)
             lowercase_				,lowercase_					:       int     				=					dummy_dataloaders()
             lowercase_					:       List[str]     				=					ProjectConfiguration(automatic_checkpoint_naming=_a				)

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

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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




