




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	:						int				=     logging.getLogger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase=2,			lowerCamelCase=3,			lowerCamelCase=16,			lowerCamelCase = 10,			lowerCamelCase = 2   ):

  def get_dataset(lowerCamelCase   ):
    lowercase    :Optional[int]      	=						torch.randn(batch_size * n_batches,			1   )
    return TensorDataset(__A,			a * x + b + 0.1 * torch.randn(batch_size * n_batches,			1   )   )

  lowercase    :Dict      	=						get_dataset(__A   )
  lowercase    :Tuple      	=						get_dataset(__A   )
  lowercase    :Any      	=						DataLoader(__A,			shuffle=__A,			batch_size=__A,			num_workers=4   )
  lowercase    :Any      	=						DataLoader(__A,			shuffle=__A,			batch_size=__A,			num_workers=4   )
  return (train_dataloader, valid_dataloader)



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None   ):
  lowercase    :Optional[int]      	=						[]
  for epoch in range(__A   ):
    # Train quickly
    model.train()
    for batch in dataloader:
      lowercase	,    lowercase    :Optional[Any]      	=						batch
      lowercase    :Tuple      	=						model(__A   )
      lowercase    :Optional[Any]      	=						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    __lowerCAmelCase       (							nn.Module):






       def __init__(   self:					Union[str, Any]				):
         super().__init__()
         lowercase    :List[Any]      	=						nn.Parameter(torch.randn(1				)				)
         lowercase    :Optional[int]      	=						nn.Parameter(torch.randn(1				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					Optional[int]				):
         return x * self.a + self.b







class    __lowerCAmelCase       (							unittest.TestCase):






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

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

           lowercase    :int      	=						train(2       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__				)
           # Save everything
           lowercase    :Optional[int]      	=						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__				)
           ((lowercase)	,    (lowercase))    :List[Any]      	=						model.a.item(), model.b.item()
           lowercase    :str      	=						optimizer.state_dict()
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42				)
           lowercase    :Dict      	=						DummyModel()
           lowercase    :int      	=						torch.optim.Adam(params=model.parameters()       ,					lr=1e-3				)
           lowercase	,    lowercase    :Tuple      	=						dummy_dataloaders()
           lowercase    :Optional[Any]      	=						ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__				)

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

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

           lowercase    :Optional[int]      	=						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__				)
           ((lowercase)	,    (lowercase))    :Any      	=						model.a.item(), model.b.item()
           lowercase    :Optional[Any]      	=						optimizer.state_dict()
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)
           self.assertEqual(lowerCAmelCase__       ,					lowerCAmelCase__				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Optional[Any]      	=						torch.tensor([1, 2, 3]				)
         lowercase    :List[Any]      	=						torch.tensor([2, 3, 4]				)
         lowercase    :Optional[int]      	=						DummyModel()
         lowercase    :Tuple      	=						torch.optim.Adam(net.parameters()				)
         lowercase    :Optional[int]      	=						Accelerator()
         with self.assertRaises(lowerCAmelCase__				) as ve:
           accelerator.register_for_checkpointing(lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__				)
         lowercase    :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 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42				)
           lowercase    :str      	=						DummyModel()
           lowercase    :Optional[Any]      	=						torch.optim.Adam(params=model.parameters()       ,					lr=1e-3				)
           lowercase    :List[Any]      	=						torch.optim.lr_scheduler.StepLR(lowerCAmelCase__       ,					step_size=1       ,					gamma=0.99				)
           lowercase	,    lowercase    :int      	=						dummy_dataloaders()
           lowercase    :List[str]      	=						ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__				)
           # Train baseline
           lowercase    :int      	=						Accelerator(project_dir=lowerCAmelCase__       ,					project_config=lowerCAmelCase__				)
           lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Optional[Any]      	=						accelerator.prepare(
               lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__				)
           # Save initial
           accelerator.save_state()
           lowercase    :List[str]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42				)
           lowercase    :List[Any]      	=						DummyModel()
           lowercase    :Optional[int]      	=						ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__       ,					total_limit=2				)
           # Train baseline
           lowercase    :List[str]      	=						Accelerator(project_dir=lowerCAmelCase__       ,					project_config=lowerCAmelCase__				)
           lowercase    :Optional[Any]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Tuple      	=						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__				)]
         execute_subprocess_async(lowerCAmelCase__       ,					env=os.environ.copy()				)


if __name__ == "__main__":
     _UpperCAmelCase	:						Any				=     "/tmp/accelerate/state_checkpointing"
     _UpperCAmelCase	:						Any				=     DummyModel()
     _UpperCAmelCase	:						Optional[int]				=     torch.optim.Adam(params=model.parameters(), lr=1E-3)
     _UpperCAmelCase	:						int				=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
     _UpperCAmelCase			,			_UpperCAmelCase	:						Tuple				=     dummy_dataloaders()
     _UpperCAmelCase	:						int				=     ProjectConfiguration(automatic_checkpoint_naming=True)
     # Train baseline
     _UpperCAmelCase	:						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)
     _UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase	:						List[Any]				=     accelerator.prepare(
         model, optimizer, train_dataloader, valid_dataloader, scheduler
     )
     _UpperCAmelCase			,			_UpperCAmelCase	:						List[str]				=     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	:						int				=     group["params"][0].device
          break
     assert param_device.type == accelerator.device.type
     _UpperCAmelCase	:						List[str]				=     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	:						Optional[int]				=     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	:						Tuple				=     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 collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       UpperCAmelCase__  (     ):
  lowercase	,    lowercase    :Union[str, Any]      	=						9, 14  # noqa: F841
  lowercase    :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],
  ]

  lowercase    :Optional[Any]      	=						defaultdict(lowerCamelCase   )
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]   )
    adjancency[nodea].append([nodea, cost]   )

  lowercase    :Optional[int]      	=						mst(lowerCamelCase   )

  lowercase    :Any      	=						[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowercase    :Any      	=						tuple(answer[:2]   )
    lowercase    :str      	=						tuple(edge[::-1]   )
    assert edge in result or reverse in result



