



'''simple docstring'''





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_lowerCAmelCase      						=	TypeVar('''T''')
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return (position - 1) // 2
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return (2 * position) + 1
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return (2 * position) + 2






class        lowerCAmelCase_(				Generic[T]     ):
  '''simple docstring'''




  def __init__(    self      )    ->		None:
      lowerCAmelCase__ :  list[tuple[T, int]]							      =					[]
      lowerCAmelCase__ :  dict[T, int]							      =					{}
      lowerCAmelCase__ :  int							      =					0




  def __len__(    self      )    ->		int:
      return self.elements




  def __repr__(    self      )    ->		str:
      return str(self.heap      )




  def 							UpperCAmelCase_	(    self      )    ->		bool:
      # Check if the priority queue is empty
      return self.elements == 0




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)      )
      lowerCAmelCase__ :  str							      =					self.elements
      self.elements += 1
      self._bubble_up(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		T:
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
          self._swap_nodes(0  ,self.elements - 1      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					self.heap[0]
          self._bubble_down(__UpperCAmelCase      )
      return elem




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Update the weight of the given key
      lowerCAmelCase__ :  int							      =					self.position_map[elem]
      lowerCAmelCase__ :  List[Any]							      =					(elem, weight)
      if position > 0:
          lowerCAmelCase__ :  Dict							      =					get_parent_position(__UpperCAmelCase      )
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.heap[parent_position]
          if parent_weight > weight:
              self._bubble_up(__UpperCAmelCase      )
          else:
              self._bubble_down(__UpperCAmelCase      )
      else:
          self._bubble_down(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      lowerCAmelCase__ :  List[str]							      =					self.position_map[elem]
      if curr_pos == 0:
          return None
      lowerCAmelCase__ :  int							      =					get_parent_position(__UpperCAmelCase      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.heap[curr_pos]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.heap[parent_position]
      if parent_weight > weight:
          self._swap_nodes(__UpperCAmelCase  ,__UpperCAmelCase      )
          return self._bubble_up(__UpperCAmelCase      )
      return None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      lowerCAmelCase__ :  List[Any]							      =					self.position_map[elem]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					self.heap[curr_pos]
      lowerCAmelCase__ :  str							      =					get_child_left_position(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					get_child_right_position(__UpperCAmelCase      )
      if child_left_position < self.elements and child_right_position < self.elements:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Dict							      =					self.heap[child_left_position]
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.heap[child_right_position]
          if child_right_weight < child_left_weight and child_right_weight < weight:
              self._swap_nodes(__UpperCAmelCase  ,__UpperCAmelCase      )
              return self._bubble_down(__UpperCAmelCase      )
      if child_left_position < self.elements:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.heap[child_left_position]
          if child_left_weight < weight:
              self._swap_nodes(__UpperCAmelCase  ,__UpperCAmelCase      )
              return self._bubble_down(__UpperCAmelCase      )
      else:
          return None
      if child_right_position < self.elements:
          lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.heap[child_right_position]
          if child_right_weight < weight:
              self._swap_nodes(__UpperCAmelCase  ,__UpperCAmelCase      )
              return self._bubble_down(__UpperCAmelCase      )
      return None






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Swap the nodes at the given positions
      lowerCAmelCase__ :  str							      =					self.heap[nodea_pos][0]
      lowerCAmelCase__ :  Dict							      =					self.heap[nodea_pos][0]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Tuple							      =					(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      lowerCAmelCase__ :  int							      =					nodea_pos
      lowerCAmelCase__ :  int							      =					nodea_pos







class        lowerCAmelCase_(				Generic[T]     ):
  '''simple docstring'''




  def __init__(    self      )    ->		None:
      lowerCAmelCase__ :  dict[T, dict[T, int]]							      =					{}
      lowerCAmelCase__ :  int							      =					0




  def __repr__(    self      )    ->		str:
      return str(self.connections      )




  def __len__(    self      )    ->		int:
      return self.nodes




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		None:
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
          lowerCAmelCase__ :  Optional[int]							      =					{}
          self.nodes += 1






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		None:
      # Add an edge between 2 nodes in the graph
      self.add_node(__UpperCAmelCase      )
      self.add_node(__UpperCAmelCase      )
      lowerCAmelCase__ :  Any							      =					weight
      lowerCAmelCase__ :  Tuple							      =					weight







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  dict[T, int]							      =					{node: maxsize for node in graph.connections}
    lowerCAmelCase__ :  dict[T, T | None]							      =					{node: None for node in graph.connections}

    lowerCAmelCase__ :  MinPriorityQueue[T]							      =					MinPriorityQueue()
    for node, weight in dist.items():
        priority_queue.push(UpperCamelCase							,    UpperCamelCase			)

    if priority_queue.is_empty():
        return dist, parent

    # initialization
    lowerCAmelCase__ :  List[Any]							      =					priority_queue.extract_min()
    lowerCAmelCase__ :  str							      =					0
    for neighbour in graph.connections[node]:
        if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            lowerCAmelCase__ :  Any							      =					dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(UpperCamelCase							,    dist[neighbour]			)
            lowerCAmelCase__ :  List[str]							      =					node

    # running prim's algorithm
    while not priority_queue.is_empty():
        lowerCAmelCase__ :  Any							      =					priority_queue.extract_min()
        for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                lowerCAmelCase__ :  Optional[int]							      =					dist[node] + graph.connections[node][neighbour]
                priority_queue.update_key(UpperCamelCase							,    dist[neighbour]			)
                lowerCAmelCase__ :  Optional[int]							      =					node
    return dist, parent






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							:       List[Any]     			=      logging.getLogger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				str=2					,    __lowerCAmelCase							:				Optional[int]=3					,    __lowerCAmelCase							:				List[str]=1_6					,    __lowerCAmelCase							:				int = 1_0					,    __lowerCAmelCase							:				int = 2   ):

							def get_dataset(__lowerCAmelCase							:				Dict   ):
														a__             =     torch.randn(batch_size * n_batches					,    1   )
														return TensorDataset(__lowerCAmelCase					,    a * x + b + 0.1 * torch.randn(batch_size * n_batches					,    1   )   )

							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							return (train_dataloader, valid_dataloader)






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







class        snake_case_   (nn.Module      ):
					def __init__(      self   :Any      )					->    Union[str, Any]:
												super().__init__()
												a__             =     nn.Parameter(torch.randn(1      )      )
												a__             =     nn.Parameter(torch.randn(1      )      )



					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :Union[str, Any]      )					->    str:
												return x * self.a + self.b







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(total_limit=1			,project_dir=__snake_case			,automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()

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

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

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

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

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

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

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			accelerator.save_state()

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

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



					@require_cuda
					def  lowerCamelCase__(      self   :Dict      )					->    str:
												a__             =     ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
												execute_subprocess_async(__snake_case			,env=os.environ.copy()      )


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




