




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_									=	logging.getLogger(__name__)




def 					lowerCamelCase__       (						A__       :					Union[str, Any]=2  ,			A__       :					List[Any]=3  ,			A__       :					Optional[int]=16  ,			A__       :					int = 10  ,			A__       :					int = 2	):





       '''simple docstring'''



       def get_dataset(A__       :					Union[str, Any]	):
              __lowerCamelCase   =						torch.randn(batch_size * n_batches  ,			1	)
              return TensorDataset(A__  ,			a * x + b + 0.1 * torch.randn(batch_size * n_batches  ,			1	)	)

       __lowerCamelCase   =						get_dataset(A__	)
       __lowerCamelCase   =						get_dataset(A__	)
       __lowerCamelCase   =						DataLoader(A__  ,			shuffle=A__  ,			batch_size=A__  ,			num_workers=4	)
       __lowerCamelCase   =						DataLoader(A__  ,			shuffle=A__  ,			batch_size=A__  ,			num_workers=4	)
       return (train_dataloader, valid_dataloader)




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[str]  ,			A__       :					Optional[Any]  ,			A__       :					Tuple  ,			A__       :					Dict  ,			A__       :					Union[str, Any]=None	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for epoch in range(A__	):
              # Train quickly
              model.train()
              for batch in dataloader:
                     __lowerCamelCase,   __lowerCamelCase   =						batch
                     __lowerCamelCase   =						model(A__	)
                     __lowerCamelCase   =						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: Dict   ):
             super().__init__()
             __lowerCamelCase   =						nn.Parameter(torch.randn(1   )   )
             __lowerCamelCase   =						nn.Parameter(torch.randn(1   )   )




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





class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: int   ):
             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=UpperCamelCase_    ,       automatic_checkpoint_naming=UpperCamelCase_   )
                    # Train baseline
                    __lowerCamelCase   =						Accelerator(project_config=UpperCamelCase_   )
                    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    # Save initial
                    accelerator.save_state()

                    # Save second state
                    accelerator.save_state()
                    self.assertEqual(len(os.listdir(accelerator.project_dir   )   )    ,       1   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             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(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    # Save initial
                    __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """initial"""   )
                    accelerator.save_state(UpperCamelCase_   )
                    ((__lowerCamelCase),   (__lowerCamelCase))   =						model.a.item(), model.b.item()
                    __lowerCamelCase   =						optimizer.state_dict()
                    __lowerCamelCase   =						train(3    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    ((__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(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    accelerator.load_state(UpperCamelCase_   )
                    ((__lowerCamelCase),   (__lowerCamelCase))   =						model.a.item(), model.b.item()
                    __lowerCamelCase   =						optimizer.state_dict()
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )

                    __lowerCamelCase   =						train(2    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    # Save everything
                    __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """checkpoint"""   )
                    accelerator.save_state(UpperCamelCase_   )

                    # Load everything back in and make sure all states work
                    accelerator.load_state(UpperCamelCase_   )
                    test_rands += train(1    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    ((__lowerCamelCase),   (__lowerCamelCase))   =						model.a.item(), model.b.item()
                    __lowerCamelCase   =						optimizer.state_dict()
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             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=UpperCamelCase_   )

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

                    __lowerCamelCase   =						train(2    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    # Save everything
                    accelerator.save_state()

                    # Load everything back in and make sure all states work
                    accelerator.load_state(os.path.join(UpperCamelCase_    ,       """checkpoints"""    ,       """checkpoint_1"""   )   )
                    test_rands += train(1    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    ((__lowerCamelCase),   (__lowerCamelCase))   =						model.a.item(), model.b.item()
                    __lowerCamelCase   =						optimizer.state_dict()
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __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(UpperCamelCase_   ) as ve:
                    accelerator.register_for_checkpointing(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
             __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 			lowerCAmelCase__						(				self: Optional[int]   ):
             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(UpperCamelCase_    ,       step_size=1    ,       gamma=0.99   )
                    __lowerCamelCase,   __lowerCamelCase   =						dummy_dataloaders()
                    __lowerCamelCase   =						ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_   )
                    # Train baseline
                    __lowerCamelCase   =						Accelerator(project_dir=UpperCamelCase_    ,       project_config=UpperCamelCase_   )
                    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(
                        UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    # Save initial
                    accelerator.save_state()
                    __lowerCamelCase   =						scheduler.state_dict()
                    train(3    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertNotEqual(UpperCamelCase_    ,       scheduler.state_dict()   )

                    # Load everything back in and make sure all states work
                    accelerator.load_state(os.path.join(UpperCamelCase_    ,       """checkpoints"""    ,       """checkpoint_0"""   )   )
                    self.assertEqual(UpperCamelCase_    ,       scheduler.state_dict()   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             with tempfile.TemporaryDirectory() as tmpdir:
                    set_seed(42   )
                    __lowerCamelCase   =						DummyModel()
                    __lowerCamelCase   =						ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_    ,       total_limit=2   )
                    # Train baseline
                    __lowerCamelCase   =						Accelerator(project_dir=UpperCamelCase_    ,       project_config=UpperCamelCase_   )
                    __lowerCamelCase   =						accelerator.prepare(UpperCamelCase_   )
                    # Save 3 states:
                    for _ in range(11   ):
                           accelerator.save_state()
                    self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase_    ,       """checkpoints"""    ,       """checkpoint_0"""   )   )   )
                    self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_    ,       """checkpoints"""    ,       """checkpoint_9"""   )   )   )
                    self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_    ,       """checkpoints"""    ,       """checkpoint_10"""   )   )   )




      @require_cuda
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						["""torchrun""", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
             execute_subprocess_async(UpperCamelCase_    ,       env=os.environ.copy()   )


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



import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   input_matrix.conj().T						)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

				while not convergence:
								# Multiple matrix by the vector.
								__A   			= np.dot(a_		,   a_						)
								# Normalize the resulting output vector.
								__A   			= w / np.linalg.norm(a_						)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__A   			= vector.conj().T if is_complex else vector.T
								__A   			= np.dot(a_		,   np.dot(a_		,   a_						)						)

								# Check convergence.
								__A   			= np.abs(lambda_ - lambda_previous						) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
