


"""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      a__ (				__lowercase=2     ,			__lowercase=3     ,			__lowercase=16     ,			__lowercase = 10     ,			__lowercase = 2   )	->	Any:

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

				_A					  =			get_dataset(__lowercase   )
				_A					  =			get_dataset(__lowercase   )
				_A					  =			DataLoader(__lowercase     ,			shuffle=__lowercase     ,			batch_size=__lowercase     ,			num_workers=4   )
				_A					  =			DataLoader(__lowercase     ,			shuffle=__lowercase     ,			batch_size=__lowercase     ,			num_workers=4   )
				return (train_dataloader, valid_dataloader)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=None   )	->	Union[str, Any]:
				_A					  =			[]
				for epoch in range(__lowercase   ):
								# Train quickly
								model.train()
								for batch in dataloader:
												_A  ,		_A					  =			batch
												_A					  =			model(__lowercase   )
												_A					  =			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 snake_case      (  nn.Module):







		def __init__(					self      :       int		)       ->						Optional[Any]:





						'''simple docstring'''







						super().__init__()
						_A					  =			nn.Parameter(torch.randn(1		)		)
						_A					  =			nn.Parameter(torch.randn(1		)		)







		def  a_ (					self      :       Optional[int]    ,   a__      :       Dict		)       ->						Tuple:





						'''simple docstring'''







						return x * self.a + self.b





class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       Any		)       ->						str:





						'''simple docstring'''







						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=a__    ,   automatic_checkpoint_naming=a__		)
										# Train baseline
										_A					  =			Accelerator(project_config=a__		)
										_A  ,		_A  ,		_A  ,		_A					  =			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  a_ (					self      :       Dict		)       ->						List[Any]:





						'''simple docstring'''







						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(
										    a__    ,   a__    ,   a__    ,   a__		)
										# Save initial
										_A					  =			os.path.join(a__    ,   "initial"		)
										accelerator.save_state(a__		)
										((_A)  ,		(_A))					  =			model.a.item(), model.b.item()
										_A					  =			optimizer.state_dict()
										_A					  =			train(3    ,   a__    ,   a__    ,   a__    ,   a__		)
										((_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(
										    a__    ,   a__    ,   a__    ,   a__		)
										accelerator.load_state(a__		)
										((_A)  ,		(_A))					  =			model.a.item(), model.b.item()
										_A					  =			optimizer.state_dict()
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)

										_A					  =			train(2    ,   a__    ,   a__    ,   a__    ,   a__		)
										# Save everything
										_A					  =			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__		)
										((_A)  ,		(_A))					  =			model.a.item(), model.b.item()
										_A					  =			optimizer.state_dict()
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)







		def  a_ (					self      :       int		)       ->						int:





						'''simple docstring'''







						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=a__		)

										# Train baseline
										_A					  =			Accelerator(project_dir=a__    ,   project_config=a__		)
										_A  ,		_A  ,		_A  ,		_A					  =			accelerator.prepare(
										    a__    ,   a__    ,   a__    ,   a__		)
										# Save initial
										accelerator.save_state()
										((_A)  ,		(_A))					  =			model.a.item(), model.b.item()
										_A					  =			optimizer.state_dict()
										_A					  =			train(3    ,   a__    ,   a__    ,   a__    ,   a__		)
										((_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=a__		)
										_A					  =			Accelerator(project_dir=a__    ,   project_config=a__		)
										_A  ,		_A  ,		_A  ,		_A					  =			accelerator.prepare(
										    a__    ,   a__    ,   a__    ,   a__		)
										accelerator.load_state(os.path.join(a__    ,   "checkpoints"    ,   "checkpoint_0"		)		)
										((_A)  ,		(_A))					  =			model.a.item(), model.b.item()
										_A					  =			optimizer.state_dict()
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)

										_A					  =			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__		)
										((_A)  ,		(_A))					  =			model.a.item(), model.b.item()
										_A					  =			optimizer.state_dict()
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)
										self.assertEqual(a__    ,   a__		)







		def  a_ (					self      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						_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(a__		) as ve:
										accelerator.register_for_checkpointing(a__    ,   a__    ,   a__    ,   a__		)
						_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  a_ (					self      :       int		)       ->						Optional[int]:





						'''simple docstring'''







						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(a__    ,   step_size=1    ,   gamma=0.9_9		)
										_A  ,		_A					  =			dummy_dataloaders()
										_A					  =			ProjectConfiguration(automatic_checkpoint_naming=a__		)
										# Train baseline
										_A					  =			Accelerator(project_dir=a__    ,   project_config=a__		)
										_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			accelerator.prepare(
										    a__    ,   a__    ,   a__    ,   a__    ,   a__		)
										# Save initial
										accelerator.save_state()
										_A					  =			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  a_ (					self      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







						with tempfile.TemporaryDirectory() as tmpdir:
										set_seed(42		)
										_A					  =			DummyModel()
										_A					  =			ProjectConfiguration(automatic_checkpoint_naming=a__    ,   total_limit=2		)
										# Train baseline
										_A					  =			Accelerator(project_dir=a__    ,   project_config=a__		)
										_A					  =			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  a_ (					self      :       Tuple		)       ->						Dict:





						'''simple docstring'''







						_A					  =			["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"""


def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	str:
				# Return True if there is node that has not iterated.
				_A					  =			[False] * len(__lowercase   )
				_A					  =			[]
				queue.append(__lowercase   )
				_A					  =			True

				while queue:
								_A					  =			queue.pop(0   )
								for ind in range(len(graph[u]   )   ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(__lowercase   )
																_A					  =			True
																_A					  =			u

				return visited[t]






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				# This array is filled by BFS and to store path
				_A					  =			[-1] * (len(__lowercase   ))
				_A					  =			0
				while bfs(__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   ):
								_A					  =			float("Inf"   )
								_A					  =			sink

								while s != source:
												# Find the minimum value in select path
												_A					  =			min(__lowercase     ,			graph[parent[s]][s]   )
												_A					  =			parent[s]

								max_flow += path_flow
								_A					  =			sink

								while v != source:
												_A					  =			parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												_A					  =			parent[v]
				return max_flow


a_       							=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a_							,     a_       							=    0, 5




print(ford_fulkerson(graph, source, sink))