






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


snake_case__   :						Any													=   logging.getLogger(__name__)






def      _snake_case     (__lowercase=2     ,   __lowercase=3     ,   __lowercase=16     ,   __lowercase = 10     ,   __lowercase = 2):

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

   UpperCamelCase_						=  get_dataset(A__)
   UpperCamelCase_						=  get_dataset(A__)
   UpperCamelCase_						=  DataLoader(A__     ,   shuffle=A__     ,   batch_size=A__     ,   num_workers=4)
   UpperCamelCase_						=  DataLoader(A__     ,   shuffle=A__     ,   batch_size=A__     ,   num_workers=4)
   return (train_dataloader, valid_dataloader)






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=None):
   UpperCamelCase_						=  []
   for epoch in range(A__):
      # Train quickly
      model.train()
      for batch in dataloader:
         UpperCamelCase_		,   UpperCamelCase_						=  batch
         UpperCamelCase_						=  model(A__)
         UpperCamelCase_						=  torch.nn.functional.mse_loss(A__     ,   A__)
         accelerator.backward(A__)
         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     ):






  """simple docstring"""





  def __init__(  self			)      ->			List[str]:
     super().__init__()
     UpperCamelCase_						=  nn.Parameter(torch.randn(1			)			)
     UpperCamelCase_						=  nn.Parameter(torch.randn(1			)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:
     return x * self.a + self.b




class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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  _UpperCAmelCase  (  self			)      ->			List[str]:
     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  _UpperCAmelCase  (  self			)      ->			Dict:
     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  _UpperCAmelCase  (  self			)      ->			str:
     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  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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=0.9_9			)
        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  _UpperCAmelCase  (  self			)      ->			int:
     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  _UpperCAmelCase  (  self			)      ->			Dict:
     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__":
 snake_case__   :						Union[str, Any]													=   '/tmp/accelerate/state_checkpointing'
 snake_case__   :						Any													=   DummyModel()
 snake_case__   :						Optional[int]													=   torch.optim.Adam(params=model.parameters(), lr=1E-3)
 snake_case__   :						Any													=   torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
 snake_case__   :						List[Any]													=   dummy_dataloaders()
 snake_case__   :						Union[str, Any]													=   ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 snake_case__   :						Optional[Any]													=   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)
 snake_case__   :						Any													=   accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 snake_case__   :						Optional[int]													=   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:
  snake_case__   :						Dict													=   group['params'][0].device
  break
 assert param_device.type == accelerator.device.type
 snake_case__   :						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:
  snake_case__   :						Optional[Any]													=   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:
  snake_case__   :						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()








from __future__ import annotations

__A			:					str						=			list[tuple[int, int]]

__A			:					Optional[int]						=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__A			:					List[str]						=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			float     , __lowerCamelCase					:			Node | None     , ):
       SCREAMING_SNAKE_CASE										=   pos_x
       SCREAMING_SNAKE_CASE										=   pos_y
       SCREAMING_SNAKE_CASE										=   (pos_y, pos_x)
       SCREAMING_SNAKE_CASE										=   goal_x
       SCREAMING_SNAKE_CASE										=   goal_y
       SCREAMING_SNAKE_CASE										=   g_cost
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   self.calculate_heuristic()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   abs(self.pos_x - self.goal_x			)
       SCREAMING_SNAKE_CASE										=   abs(self.pos_y - self.goal_y			)
       return dx + dy





   def __lt__(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return self.f_cost < other.f_cost





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			tuple[int, int]     , __lowerCamelCase					:			tuple[int, int]			):
       SCREAMING_SNAKE_CASE										=   Node(start[1]     , start[0]     , goal[1]     , goal[0]     , 0     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   Node(goal[1]     , goal[0]     , goal[1]     , goal[0]     , 99999     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   [self.start]
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   False





   def      _snake_case		(       self					:			Optional[Any]			):
       while self.open_nodes:
           # Open Nodes are sorted using __lt__
           self.open_nodes.sort()
           SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(0			)

           if current_node.pos == self.target.pos:
               SCREAMING_SNAKE_CASE										=   True
               return self.retrace_path(__lowerCamelCase			)

           self.closed_nodes.append(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.get_successors(__lowerCamelCase			)

           for child_node in successors:
               if child_node in self.closed_nodes:
                   continue

               if child_node not in self.open_nodes:
                   self.open_nodes.append(__lowerCamelCase			)
               else:
                   # retrieve the best current path
                   SCREAMING_SNAKE_CASE										=   self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase			)			)

                   if child_node.g_cost < better_node.g_cost:
                       self.open_nodes.append(__lowerCamelCase			)
                   else:
                       self.open_nodes.append(__lowerCamelCase			)

       if not self.reached:
           return [self.start.pos]
       return None





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Node			):
       SCREAMING_SNAKE_CASE										=   []
       for action in delta:
           SCREAMING_SNAKE_CASE										=   parent.pos_x + action[1]
           SCREAMING_SNAKE_CASE										=   parent.pos_y + action[0]

           if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__lowerCamelCase			) - 1):
               continue

           if grid[pos_y][pos_x] != 0:
               continue

           successors.append(
               Node(
                   __lowerCamelCase     , __lowerCamelCase     , self.target.pos_y     , self.target.pos_x     , parent.g_cost + 1     , __lowerCamelCase     , )			)
       return successors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Node | None			):
       SCREAMING_SNAKE_CASE										=   node
       SCREAMING_SNAKE_CASE										=   []
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)			)
           SCREAMING_SNAKE_CASE										=   current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
    __A			:					Optional[Any]						=			(0, 0)
    __A			:					Optional[int]						=			(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    print('------')

    __A			:					List[str]						=			GreedyBestFirst(init, goal)
    __A			:					Tuple						=			greedy_bf.search()
    if path:
        for pos_x, pos_y in path:
            __A			:					Optional[Any]						=			2

        for elem in grid:
            print(elem)