


"""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


_snake_case          =							logging.getLogger(__name__)



def   snake_case						(				_a:     Optional[Any]=2		,   _a:     Union[str, Any]=3		,   _a:     int=16		,   _a:     int = 10		,   _a:     int = 2							)->		int:
      '''simple docstring'''


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

      lowerCamelCase__           =		get_dataset(lowercase_							)
      lowerCamelCase__           =		get_dataset(lowercase_							)
      lowerCamelCase__           =		DataLoader(lowercase_		,   shuffle=lowercase_		,   batch_size=lowercase_		,   num_workers=4							)
      lowerCamelCase__           =		DataLoader(lowercase_		,   shuffle=lowercase_		,   batch_size=lowercase_		,   num_workers=4							)
      return (train_dataloader, valid_dataloader)



def   snake_case						(				_a:     Optional[Any]		,   _a:     List[str]		,   _a:     int		,   _a:     int		,   _a:     List[str]		,   _a:     Dict=None							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for epoch in range(lowercase_							):
            # Train quickly
            model.train()
            for batch in dataloader:
                  lowerCamelCase__           =		batch
                  lowerCamelCase__           =		model(lowercase_							)
                  lowerCamelCase__           =		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   _a      (      nn.Module ):
    def __init__( self							:					List[Any]				):
          super().__init__()
          lowerCamelCase__           =		nn.Parameter(torch.randn(1				)				)
          lowerCamelCase__           =		nn.Parameter(torch.randn(1				)				)


    def      _UpperCamelCase       ( self							:					Union[str, Any]							,     SCREAMING_SNAKE_CASE__							:					List[Any]				):
          return x * self.a + self.b





class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42				)
                lowerCamelCase__           =		DummyModel()
                lowerCamelCase__           =		torch.optim.Adam(params=model.parameters()							,     lr=1e-3				)
                lowerCamelCase__           =		dummy_dataloaders()
                lowerCamelCase__           =		ProjectConfiguration(total_limit=1							,     project_dir=A__							,     automatic_checkpoint_naming=A__				)
                # Train baseline
                lowerCamelCase__           =		Accelerator(project_config=A__				)
                lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Optional[Any]				):
          with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42				)
                lowerCamelCase__           =		DummyModel()
                lowerCamelCase__           =		torch.optim.Adam(params=model.parameters()							,     lr=1e-3				)
                lowerCamelCase__           =		dummy_dataloaders()
                # Train baseline
                lowerCamelCase__           =		Accelerator()
                lowerCamelCase__           =		accelerator.prepare(
                    A__							,     A__							,     A__							,     A__				)
                # Save initial
                lowerCamelCase__           =		os.path.join(A__							,     'initial'				)
                accelerator.save_state(A__				)
                (lowerCamelCase__)           =		model.a.item(), model.b.item()
                lowerCamelCase__           =		optimizer.state_dict()
                lowerCamelCase__           =		train(3							,     A__							,     A__							,     A__							,     A__				)
                (lowerCamelCase__)           =		model.a.item(), model.b.item()
                lowerCamelCase__           =		optimizer.state_dict()

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

                lowerCamelCase__           =		train(2							,     A__							,     A__							,     A__							,     A__				)
                # Save everything
                lowerCamelCase__           =		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__				)
                (lowerCamelCase__)           =		model.a.item(), model.b.item()
                lowerCamelCase__           =		optimizer.state_dict()
                self.assertEqual(A__							,     A__				)
                self.assertEqual(A__							,     A__				)
                self.assertEqual(A__							,     A__				)
                self.assertEqual(A__							,     A__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          with tempfile.TemporaryDirectory() as tmpdir:
                set_seed(42				)
                lowerCamelCase__           =		DummyModel()
                lowerCamelCase__           =		torch.optim.Adam(params=model.parameters()							,     lr=1e-3				)
                lowerCamelCase__           =		dummy_dataloaders()
                lowerCamelCase__           =		ProjectConfiguration(automatic_checkpoint_naming=A__				)

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

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

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


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


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def   snake_case						(				_a:     Optional[Any]							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[-1] * len(_a							)

      def dfs(_a:     Any		,   _a:     Optional[int]							):
            lowerCamelCase__           =		True
            lowerCamelCase__           =		c
            for u in graph[v]:
                  if not visited[u]:
                        dfs(_a		,   1 - c							)

      for i in range(len(_a							)							):
            if not visited[i]:
                  dfs(_a		,   0							)

      for i in range(len(_a							)							):
            for j in graph[i]:
                  if color[i] == color[j]:
                        return False

      return True


# Adjacency list of graph
_snake_case          =							{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}






print(check_bipartite_dfs(graph))



