
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


SCREAMING_SNAKE_CASE							:Any										=    logging.getLogger(__name__)


def 						UpperCAmelCase      (     a_=2		,       a_=3		,       a_=1_6		,       a_ = 1_0		,       a_ = 2						)		->     Tuple:







    """simple docstring"""



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

    __A             =			get_dataset(a_						)
    __A             =			get_dataset(a_						)
    __A             =			DataLoader(a_		,       shuffle=a_		,       batch_size=a_		,       num_workers=4						)
    __A             =			DataLoader(a_		,       shuffle=a_		,       batch_size=a_		,       num_workers=4						)
    return (train_dataloader, valid_dataloader)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_		,       a_=None						)		->     Optional[int]:







    """simple docstring"""



    __A             =			[]
    for epoch in range(a_						):
        # Train quickly
        model.train()
        for batch in dataloader:
            __A      ,							__A             =			batch
            __A             =			model(a_						)
            __A             =			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 							UpperCAmelCase				(  nn.Module  ):




 '''simple docstring'''





 def __init__(	self :			List[str]			):
     super().__init__()
     __A             =			nn.Parameter(torch.randn(1			)			)
     __A             =			nn.Parameter(torch.randn(1			)			)







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





class 							UpperCAmelCase				(  unittest.TestCase  ):




 '''simple docstring'''





 def     UpperCamelCase_  (	self :			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=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     UpperCamelCase_  (	self :			Optional[int]			):
     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     UpperCamelCase_  (	self :			List[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()
         __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     UpperCamelCase_  (	self :			Dict			):
     __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     UpperCamelCase_  (	self :			Tuple			):
     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=0.99			)
         __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     UpperCamelCase_  (	self :			Optional[int]			):
     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     UpperCamelCase_  (	self :			str			):
     __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__":
     SCREAMING_SNAKE_CASE							:Optional[Any]										=    '/tmp/accelerate/state_checkpointing'
     SCREAMING_SNAKE_CASE							:Optional[int]										=    DummyModel()
     SCREAMING_SNAKE_CASE							:Optional[Any]										=    torch.optim.Adam(params=model.parameters(), lr=1E-3)
     SCREAMING_SNAKE_CASE							:int										=    torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
     SCREAMING_SNAKE_CASE							:Dict										=    dummy_dataloaders()
     SCREAMING_SNAKE_CASE							:Optional[int]										=    ProjectConfiguration(automatic_checkpoint_naming=True)
     # Train baseline
     SCREAMING_SNAKE_CASE							:Any										=    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)
     SCREAMING_SNAKE_CASE							:List[Any]										=    accelerator.prepare(
         model, optimizer, train_dataloader, valid_dataloader, scheduler
     )
     SCREAMING_SNAKE_CASE							:List[str]										=    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:
          SCREAMING_SNAKE_CASE							:List[Any]										=    group['params'][0].device
          break
     assert param_device.type == accelerator.device.type
     SCREAMING_SNAKE_CASE							: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:
          SCREAMING_SNAKE_CASE							: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:
          SCREAMING_SNAKE_CASE							:Any										=    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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="text-classification"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"text": Value("string"  )}  )
	snake_case_    =					Features({"labels": ClassLabel}  )
	snake_case_    =					"text"
	snake_case_    =					"labels"






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					if self.label_column not in features:
									raise ValueError(f'''Column {self.label_column} is not present in features.'''			)
					if not isinstance(features[self.label_column]							,A			):
									raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.label_schema.copy()
					__A             =			features[self.label_column]
					__A             =			label_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return {
					    self.text_column: "text",
					    self.label_column: "labels",
					}
