






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						:			Tuple     	=					logging.getLogger(__name__)






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

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

  __UpperCAmelCase				:      int	 =    get_dataset(__lowerCamelCase						)
  __UpperCAmelCase				:      Tuple	 =    get_dataset(__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[int]	 =    DataLoader(__lowerCamelCase    ,						shuffle=__lowerCamelCase    ,						batch_size=__lowerCamelCase    ,						num_workers=4						)
  __UpperCAmelCase				:      int	 =    DataLoader(__lowerCamelCase    ,						shuffle=__lowerCamelCase    ,						batch_size=__lowerCamelCase    ,						num_workers=4						)
  return (train_dataloader, valid_dataloader)






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple=None						):
  __UpperCAmelCase				:      Dict	 =    []
  for epoch in range(__lowerCamelCase						):
    # Train quickly
    model.train()
    for batch in dataloader:
      __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    batch
      __UpperCAmelCase				:      str	 =    model(__lowerCamelCase						)
      __UpperCAmelCase				:      Dict	 =    torch.nn.functional.mse_loss(__lowerCamelCase    ,						__lowerCamelCase						)
      accelerator.backward(__lowerCamelCase						)
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()						)  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands
class        a       (					nn.Module  ):



      """simple docstring"""


      def __init__( self   :     Union[str, Any]   )				-> Any:
        super().__init__()
        __UpperCAmelCase				:      Tuple	 =    nn.Parameter(torch.randn(1   )   )
        __UpperCAmelCase				:      str	 =    nn.Parameter(torch.randn(1   )   )




      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     List[str]   )				-> Union[str, Any]:
        return x * self.a + self.b


class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     List[str]   )				-> Tuple:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      List[Any]	 =    DummyModel()
          __UpperCAmelCase				:      Union[str, Any]	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    dummy_dataloaders()
          __UpperCAmelCase				:      int	 =    ProjectConfiguration(total_limit=1						, project_dir=__lowercase						, automatic_checkpoint_naming=__lowercase   )
          # Train baseline
          __UpperCAmelCase				:      Optional[int]	 =    Accelerator(project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          accelerator.save_state()

          # Save second state
          accelerator.save_state()
          self.assertEqual(len(os.listdir(accelerator.project_dir   )   )						, 1   )
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      List[str]	 =    DummyModel()
          __UpperCAmelCase				:      int	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    dummy_dataloaders()
          # Train baseline
          __UpperCAmelCase				:      int	 =    Accelerator()
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          __UpperCAmelCase				:      Any	 =    os.path.join(__lowercase						, """initial"""   )
          accelerator.save_state(__lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[Any]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      int	 =    optimizer.state_dict()
          __UpperCAmelCase				:      str	 =    train(3						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      int	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Tuple	 =    optimizer.state_dict()

          # Train partially
          set_seed(42   )
          __UpperCAmelCase				:      int	 =    DummyModel()
          __UpperCAmelCase				:      Tuple	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      Optional[Any]	 =    Accelerator()
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          accelerator.load_state(__lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[Any]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Tuple	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )

          __UpperCAmelCase				:      Union[str, Any]	 =    train(2						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save everything
          __UpperCAmelCase				:      List[Any]	 =    os.path.join(__lowercase						, """checkpoint"""   )
          accelerator.save_state(__lowercase   )

          # Load everything back in and make sure all states work
          accelerator.load_state(__lowercase   )
          test_rands += train(1						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      Optional[int]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      str	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     int   )				-> Union[str, Any]:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      Any	 =    DummyModel()
          __UpperCAmelCase				:      Dict	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      str	 =    ProjectConfiguration(automatic_checkpoint_naming=__lowercase   )

          # Train baseline
          __UpperCAmelCase				:      Dict	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          accelerator.save_state()
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[str]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Dict	 =    optimizer.state_dict()
          __UpperCAmelCase				:      Tuple	 =    train(3						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      int	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      str	 =    optimizer.state_dict()

          # Train partially
          set_seed(42   )
          __UpperCAmelCase				:      List[Any]	 =    DummyModel()
          __UpperCAmelCase				:      List[str]	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      Tuple	 =    ProjectConfiguration(iteration=1						, automatic_checkpoint_naming=__lowercase   )
          __UpperCAmelCase				:      Any	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          accelerator.load_state(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_0"""   )   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      Tuple	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Optional[Any]	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )

          __UpperCAmelCase				:      Dict	 =    train(2						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save everything
          accelerator.save_state()

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_1"""   )   )
          test_rands += train(1						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[str]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Optional[int]	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Tuple	 =    torch.tensor([1, 2, 3]   )
        __UpperCAmelCase				:      Dict	 =    torch.tensor([2, 3, 4]   )
        __UpperCAmelCase				:      Union[str, Any]	 =    DummyModel()
        __UpperCAmelCase				:      str	 =    torch.optim.Adam(net.parameters()   )
        __UpperCAmelCase				:      str	 =    Accelerator()
        with self.assertRaises(__lowercase   ) as ve:
          accelerator.register_for_checkpointing(__lowercase						, __lowercase						, __lowercase						, __lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    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   :     Dict   )				-> Any:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      int	 =    DummyModel()
          __UpperCAmelCase				:      int	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase				:      Dict	 =    torch.optim.lr_scheduler.StepLR(__lowercase						, step_size=1						, gamma=0.99   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      List[str]	 =    ProjectConfiguration(automatic_checkpoint_naming=__lowercase   )
          # Train baseline
          __UpperCAmelCase				:      Optional[Any]	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          accelerator.save_state()
          __UpperCAmelCase				:      Optional[int]	 =    scheduler.state_dict()
          train(3						, __lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          self.assertNotEqual(__lowercase						, scheduler.state_dict()   )

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




      @require_cuda
      def       UpperCAmelCase ( self   :     Dict   )				-> Optional[Any]:
        __UpperCAmelCase				:      List[str]	 =    ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__   )]
        execute_subprocess_async(__lowercase						, env=os.environ.copy()   )


if __name__ == "__main__":
       a						:			Union[str, Any]     	=					"/tmp/accelerate/state_checkpointing"
       a						:			Any     	=					DummyModel()
       a						:			str     	=					torch.optim.Adam(params=model.parameters(), lr=1e-3)
       a						:			List[str]     	=					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
       a	,a						:			List[Any]     	=					dummy_dataloaders()
       a						:			List[str]     	=					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						:			Dict     	=					accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       a	,a						:			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:
              a						:			Union[str, Any]     	=					group["params"][0].device
              break
       assert param_device.type == accelerator.device.type
       a						:			str     	=					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						:			Dict     	=					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'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



