




"""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 		_snake_case	(   _snake_case				:   Dict=2							,    _snake_case				:   Dict=3							,    _snake_case				:   int=16							,    _snake_case				:   str = 10							,    _snake_case				:   Tuple = 2				)       ->   Union[str, Any]:






      '''simple docstring'''


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

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






def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   Dict							,    _snake_case				:   Optional[int]							,    _snake_case				:   Optional[Any]							,    _snake_case				:   str							,    _snake_case				:   Optional[Any]=None				)       ->   int:






      '''simple docstring'''


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





class 							lowercase_			(	nn.Module				):



 '''simple docstring'''

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



 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			str						):
       return x * self.a + self.b





class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):



       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=_UpperCAmelCase       ,				automatic_checkpoint_naming=_UpperCAmelCase						)
             # Train baseline
             _A     =  Accelerator(project_config=_UpperCAmelCase						)
             _A  ,       _A  ,       _A  ,       _A     =  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			:			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(
                 _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             # Save initial
             _A     =  os.path.join(_UpperCAmelCase       ,				'initial'						)
             accelerator.save_state(_UpperCAmelCase						)
             ((_A)  ,       (_A))     =  model.a.item(), model.b.item()
             _A     =  optimizer.state_dict()
             _A     =  train(3       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             ((_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(
                 _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             accelerator.load_state(_UpperCAmelCase						)
             ((_A)  ,       (_A))     =  model.a.item(), model.b.item()
             _A     =  optimizer.state_dict()
             self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)
             self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)
             self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

 def      lowerCAmelCase_				(					self			:			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(automatic_checkpoint_naming=_UpperCAmelCase						)

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

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

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _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(_UpperCAmelCase						) as ve:
             accelerator.register_for_checkpointing(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
       _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			:			Optional[Any]						):



       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(_UpperCAmelCase       ,				step_size=1       ,				gamma=0.99						)
             _A  ,       _A     =  dummy_dataloaders()
             _A     =  ProjectConfiguration(automatic_checkpoint_naming=_UpperCAmelCase						)
             # Train baseline
             _A     =  Accelerator(project_dir=_UpperCAmelCase       ,				project_config=_UpperCAmelCase						)
             _A  ,       _A  ,       _A  ,       _A  ,       _A     =  accelerator.prepare(
                 _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             # Save initial
             accelerator.save_state()
             _A     =  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			:			Union[str, Any]						):



       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42						)
             _A     =  DummyModel()
             _A     =  ProjectConfiguration(automatic_checkpoint_naming=_UpperCAmelCase       ,				total_limit=2						)
             # Train baseline
             _A     =  Accelerator(project_dir=_UpperCAmelCase       ,				project_config=_UpperCAmelCase						)
             _A     =  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			:			Union[str, Any]						):
       _A     =  ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__						)]
       execute_subprocess_async(_UpperCAmelCase       ,				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=0.9_9)
       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							      =     accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       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()







from __future__ import annotations

from collections.abc import MutableSequence



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if len(SCREAMING_SNAKE_CASE      ) != degree + 1:
							raise ValueError(
							    'The number of coefficients should be equal to the degree + 1.'      )

				SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						degree





	def __add__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				if self.degree > polynomial_a.degree:
							SCREAMING_SNAKE_CASE_								=						self.coefficients[:]
							for i in range(polynomial_a.degree + 1      ):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree      ,     SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						polynomial_a.coefficients[:]
							for i in range(self.degree + 1      ):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def __sub__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				return self + polynomial_a * Polynomial(0      ,     [-1]      )





	def __neg__(							self      )  ->					Polynomial:




				"""simple docstring"""



				return Polynomial(self.degree      ,     [-c for c in self.coefficients]      )





	def __mul__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1      ):
							for j in range(polynomial_a.degree + 1      ):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int | float:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for i in range(self.degree + 1      ):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for i in range(self.degree      ,     -1      ,     -1      ):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]      )      )
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x^" + str(SCREAMING_SNAKE_CASE      )

				return polynomial





	def __repr__(							self      )  ->					str:




				"""simple docstring"""



				return self.__str__()





	def 						A_(							self      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * self.degree
				for i in range(self.degree      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + 2)
				SCREAMING_SNAKE_CASE_								=						constant
				for i in range(self.degree + 1      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1      ,     SCREAMING_SNAKE_CASE      )





	def __eq__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1      ):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				return not self.__eq__(SCREAMING_SNAKE_CASE      )
