


"""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     :   str			    =			logging.getLogger(__name__)


def _lowerCamelCase	(		_UpperCamelCase=2    , _UpperCamelCase=3    , _UpperCamelCase=16    , _UpperCamelCase = 10    , _UpperCamelCase = 2      ):

			'''simple docstring'''




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

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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]
			for epoch in range(_UpperCamelCase      ):
						# Train quickly
						model.train()
						for batch in dataloader:
									__lowerCAmelCase    ,	__lowerCAmelCase				    =			batch
									__lowerCAmelCase				    =			model(_UpperCamelCase      )
									__lowerCAmelCase				    =			torch.nn.functional.mse_loss(_UpperCamelCase    , _UpperCamelCase      )
									accelerator.backward(_UpperCamelCase      )
									optimizer.step()
									optimizer.zero_grad()
						rands.append(random.random()      )  # Introduce some randomness
						if scheduler is not None:
									scheduler.step()
			return rands
class   _UpperCamelCase  (   nn.Module		):
							'''simple docstring'''







							def __init__(     self	):
										super().__init__()
										__lowerCAmelCase				    =			nn.Parameter(torch.randn(1	)	)
										__lowerCAmelCase				    =			nn.Parameter(torch.randn(1	)	)



							def 			snake_case				(     self    ,   __a	):
										return x * self.a + self.b
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(42	)
													__lowerCAmelCase				    =			DummyModel()
													__lowerCAmelCase				    =			torch.optim.Adam(params=model.parameters()    ,   lr=1e-3	)
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			dummy_dataloaders()
													__lowerCAmelCase				    =			ProjectConfiguration(total_limit=1    ,   project_dir=__a    ,   automatic_checkpoint_naming=__a	)
													# Train baseline
													__lowerCAmelCase				    =			Accelerator(project_config=__a	)
													__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__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 			snake_case				(     self	):
										with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(42	)
													__lowerCAmelCase				    =			DummyModel()
													__lowerCAmelCase				    =			torch.optim.Adam(params=model.parameters()    ,   lr=1e-3	)
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			dummy_dataloaders()
													# Train baseline
													__lowerCAmelCase				    =			Accelerator()
													__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			accelerator.prepare(
													    __a    ,   __a    ,   __a    ,   __a	)
													# Save initial
													__lowerCAmelCase				    =			os.path.join(__a    ,   "initial"	)
													accelerator.save_state(__a	)
													((__lowerCAmelCase)    ,	(__lowerCAmelCase))				    =			model.a.item(), model.b.item()
													__lowerCAmelCase				    =			optimizer.state_dict()
													__lowerCAmelCase				    =			train(3    ,   __a    ,   __a    ,   __a    ,   __a	)
													((__lowerCAmelCase)    ,	(__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    ,	__lowerCAmelCase				    =			dummy_dataloaders()
													__lowerCAmelCase				    =			Accelerator()
													__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			accelerator.prepare(
													    __a    ,   __a    ,   __a    ,   __a	)
													accelerator.load_state(__a	)
													((__lowerCAmelCase)    ,	(__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)    ,	(__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 			snake_case				(     self	):
										with tempfile.TemporaryDirectory() as tmpdir:
													set_seed(42	)
													__lowerCAmelCase				    =			DummyModel()
													__lowerCAmelCase				    =			torch.optim.Adam(params=model.parameters()    ,   lr=1e-3	)
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			dummy_dataloaders()
													__lowerCAmelCase				    =			ProjectConfiguration(automatic_checkpoint_naming=__a	)

													# Train baseline
													__lowerCAmelCase				    =			Accelerator(project_dir=__a    ,   project_config=__a	)
													__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			accelerator.prepare(
													    __a    ,   __a    ,   __a    ,   __a	)
													# Save initial
													accelerator.save_state()
													((__lowerCAmelCase)    ,	(__lowerCAmelCase))				    =			model.a.item(), model.b.item()
													__lowerCAmelCase				    =			optimizer.state_dict()
													__lowerCAmelCase				    =			train(3    ,   __a    ,   __a    ,   __a    ,   __a	)
													((__lowerCAmelCase)    ,	(__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    ,	__lowerCAmelCase				    =			dummy_dataloaders()
													__lowerCAmelCase				    =			ProjectConfiguration(iteration=1    ,   automatic_checkpoint_naming=__a	)
													__lowerCAmelCase				    =			Accelerator(project_dir=__a    ,   project_config=__a	)
													__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			accelerator.prepare(
													    __a    ,   __a    ,   __a    ,   __a	)
													accelerator.load_state(os.path.join(__a    ,   "checkpoints"    ,   "checkpoint_0"	)	)
													((__lowerCAmelCase)    ,	(__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)    ,	(__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 			snake_case				(     self	):
										__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 			snake_case				(     self	):
										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=0.9_9	)
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			dummy_dataloaders()
													__lowerCAmelCase				    =			ProjectConfiguration(automatic_checkpoint_naming=__a	)
													# Train baseline
													__lowerCAmelCase				    =			Accelerator(project_dir=__a    ,   project_config=__a	)
													__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__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 			snake_case				(     self	):
										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 			snake_case				(     self	):
										__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__":
	A     :   Optional[Any]			    =			"/tmp/accelerate/state_checkpointing"
	A     :   Tuple			    =			DummyModel()
	A     :   List[str]			    =			torch.optim.Adam(params=model.parameters(), lr=1e-3)
	A     :   Optional[Any]			    =			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
	A    ,    A     :   Dict			    =			dummy_dataloaders()
	A     :   Dict			    =			ProjectConfiguration(automatic_checkpoint_naming=True)
	# Train baseline
	A     :   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)
	A    ,    A    ,    A    ,    A    ,    A     :   Optional[Any]			    =			accelerator.prepare(
	    model, optimizer, train_dataloader, valid_dataloader, scheduler
	)
	A    ,    A     :   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:
		A     :   Union[str, Any]			    =			group["params"][0].device
		break
	assert param_device.type == accelerator.device.type
	A     :   Dict			    =			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     :   Union[str, 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:
		A     :   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()





from __future__ import annotations

from math import pi, sqrt




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   float	):
		"""simple docstring"""







		if inductance <= 0:
				raise ValueError("Inductance cannot be 0 or negative"	)

		elif capacitance <= 0:
				raise ValueError("Capacitance cannot be 0 or negative"	)

		else:
				return (
				    "Resonant frequency",
				    float(1 / (2 * pi * (sqrt(inductance * capacitance	)))	),
				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
