


'''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


__magic_name__			=      logging.getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Tuple=2							,						lowerCamelCase  :      Tuple=3							,						lowerCamelCase  :      Tuple=16							,						lowerCamelCase  :      int = 10							,						lowerCamelCase  :      int = 2):

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

      A_						:  str          =				get_dataset(lowerCamelCase)
      A_						:  Optional[Any]          =				get_dataset(lowerCamelCase)
      A_						:  List[Any]          =				DataLoader(lowerCamelCase							,						shuffle=lowerCamelCase							,						batch_size=lowerCamelCase							,						num_workers=4)
      A_						:  Optional[Any]          =				DataLoader(lowerCamelCase							,						shuffle=lowerCamelCase							,						batch_size=lowerCamelCase							,						num_workers=4)
      return (train_dataloader, valid_dataloader)




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      str=None):
      A_						:  List[str]          =				[]
      for epoch in range(lowerCamelCase):
            # Train quickly
            model.train()
            for batch in dataloader:
                  A_   ,					A_						:  int          =				batch
                  A_						:  Optional[int]          =				model(lowerCamelCase)
                  A_						:  str          =				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 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Optional[int] ):

        '''simple docstring'''




        super().__init__()
        A_						:  Optional[Any]          =				nn.Parameter(torch.randn(1 ) )
        A_						:  List[str]          =				nn.Parameter(torch.randn(1 ) )






  def        _a							(							self :       List[Any]	,_a :       int ):

        '''simple docstring'''




        return x * self.a + self.b







class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







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

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  Optional[Any]          =				DummyModel()
              A_						:  Optional[int]          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_   ,					A_						:  int          =				dummy_dataloaders()
              A_						:  int          =				ProjectConfiguration(total_limit=1	,project_dir=_a	,automatic_checkpoint_naming=_a )
              # Train baseline
              A_						:  Optional[Any]          =				Accelerator(project_config=_a )
              A_   ,					A_   ,					A_   ,					A_						:  Union[str, Any]          =				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        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  Optional[Any]          =				DummyModel()
              A_						:  Dict          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_   ,					A_						:  Optional[Any]          =				dummy_dataloaders()
              # Train baseline
              A_						:  List[Any]          =				Accelerator()
              A_   ,					A_   ,					A_   ,					A_						:  Dict          =				accelerator.prepare(
                  _a	,_a	,_a	,_a )
              # Save initial
              A_						:  Dict          =				os.path.join(_a	,"""initial""" )
              accelerator.save_state(_a )
              ((A_)   ,					(A_))						:  Optional[Any]          =				model.a.item(), model.b.item()
              A_						:  Tuple          =				optimizer.state_dict()
              A_						:  List[str]          =				train(3	,_a	,_a	,_a	,_a )
              ((A_)   ,					(A_))						:  Union[str, Any]          =				model.a.item(), model.b.item()
              A_						:  Optional[int]          =				optimizer.state_dict()

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

              A_						:  Tuple          =				train(2	,_a	,_a	,_a	,_a )
              # Save everything
              A_						:  Any          =				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_))						:  List[str]          =				model.a.item(), model.b.item()
              A_						:  Optional[Any]          =				optimizer.state_dict()
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  List[str]          =				DummyModel()
              A_						:  List[str]          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_   ,					A_						:  Dict          =				dummy_dataloaders()
              A_						:  Dict          =				ProjectConfiguration(automatic_checkpoint_naming=_a )

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

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

              A_						:  Optional[int]          =				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_))						:  List[Any]          =				model.a.item(), model.b.item()
              A_						:  Optional[int]          =				optimizer.state_dict()
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.tensor([1, 2, 3] )
        A_						:  str          =				torch.tensor([2, 3, 4] )
        A_						:  str          =				DummyModel()
        A_						:  Tuple          =				torch.optim.Adam(net.parameters() )
        A_						:  List[str]          =				Accelerator()
        with self.assertRaises(_a ) as ve:
              accelerator.register_for_checkpointing(_a	,_a	,_a	,_a )
        A_						:  Union[str, Any]          =				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        _a							(							self :       Tuple ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  List[str]          =				DummyModel()
              A_						:  Any          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  str          =				torch.optim.lr_scheduler.StepLR(_a	,step_size=1	,gamma=0.99 )
              A_   ,					A_						:  List[str]          =				dummy_dataloaders()
              A_						:  Union[str, Any]          =				ProjectConfiguration(automatic_checkpoint_naming=_a )
              # Train baseline
              A_						:  List[Any]          =				Accelerator(project_dir=_a	,project_config=_a )
              A_   ,					A_   ,					A_   ,					A_   ,					A_						:  str          =				accelerator.prepare(
                  _a	,_a	,_a	,_a	,_a )
              # Save initial
              accelerator.save_state()
              A_						:  List[Any]          =				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        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  Any          =				DummyModel()
              A_						:  str          =				ProjectConfiguration(automatic_checkpoint_naming=_a	,total_limit=2 )
              # Train baseline
              A_						:  List[str]          =				Accelerator(project_dir=_a	,project_config=_a )
              A_						:  List[str]          =				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        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  List[str]          =				["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
        execute_subprocess_async(_a	,env=os.environ.copy() )


if __name__ == "__main__":
     __magic_name__			=      '/tmp/accelerate/state_checkpointing'
     __magic_name__			=      DummyModel()
     __magic_name__			=      torch.optim.Adam(params=model.parameters(), lr=1e-3)
     __magic_name__			=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
     __magic_name__		,     __magic_name__			=      dummy_dataloaders()
     __magic_name__			=      ProjectConfiguration(automatic_checkpoint_naming=True)
     # Train baseline
     __magic_name__			=      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)
     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__			=      accelerator.prepare(
         model, optimizer, train_dataloader, valid_dataloader, scheduler
     )
     __magic_name__		,     __magic_name__			=      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:
          __magic_name__			=      group['params'][0].device
          break
     assert param_device.type == accelerator.device.type
     __magic_name__			=      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:
          __magic_name__			=      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:
          __magic_name__			=      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 json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__magic_name__			=      {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}



class 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       Dict ):

        '''simple docstring'''




        super().__init__()
        A_						:  List[str]          =				torchvision.models.resnetaaa(pretrained=_a )
        A_						:  int          =				list(model.children() )[:-2]
        A_						:  int          =				nn.Sequential(*_a )
        A_						:  Optional[int]          =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






  def        _a							(							self :       str	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				self.pool(self.model(_a ) )
        A_						:  Any          =				torch.flatten(_a	,start_dim=2 )
        A_						:  str          =				out.transpose(1	,2 ).contiguous()
        return out  # BxNx2048







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Optional[Any]	,_a :       Optional[Any]	,_a :       Dict	,_a :       Dict	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Dict          =				[json.loads(_a ) for l in open(_a )]
        A_						:  Optional[int]          =				os.path.dirname(_a )
        A_						:  Optional[Any]          =				tokenizer
        A_						:  Optional[Any]          =				labels
        A_						:  List[Any]          =				len(_a )
        A_						:  str          =				max_seq_length

        A_						:  str          =				transforms






  def __len__(							self :       str ):

        '''simple docstring'''




        return len(self.data )






  def __getitem__(							self :       Tuple	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]	,add_special_tokens=_a ) )
        A_   ,					A_   ,					A_						:  Dict          =				sentence[0], sentence[1:-1], sentence[-1]
        A_						:  Optional[int]          =				sentence[: self.max_seq_length]

        A_						:  Any          =				torch.zeros(self.n_classes )
        A_						:  Tuple          =				1

        A_						:  Optional[Any]          =				Image.open(os.path.join(self.data_dir	,self.data[index]["""img"""] ) ).convert("""RGB""" )
        A_						:  Union[str, Any]          =				self.transforms(_a )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				Counter()
        for row in self.data:
              label_freqs.update(row["""label"""] )
        return label_freqs







def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  List[Any]          =				[len(row["""sentence"""]) for row in batch]
      A_   ,					A_						:  Dict          =				len(lowerCamelCase), max(lowerCamelCase)

      A_						:  Optional[int]          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)
      A_						:  Tuple          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase							,						lowerCamelCase)):
            A_						:  str          =				input_row["""sentence"""]
            A_						:  Tuple          =				1

      A_						:  int          =				torch.stack([row["""image"""] for row in batch])
      A_						:  str          =				torch.stack([row["""label"""] for row in batch])
      A_						:  List[Any]          =				torch.stack([row["""image_start_token"""] for row in batch])
      A_						:  Tuple          =				torch.stack([row["""image_end_token"""] for row in batch])

      return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def      lowerCamelCase					(      ):
      return [
          "Crime",
          "Drama",
          "Thriller",
          "Action",
          "Comedy",
          "Romance",
          "Documentary",
          "Short",
          "Mystery",
          "History",
          "Family",
          "Adventure",
          "Fantasy",
          "Sci-Fi",
          "Western",
          "Horror",
          "Sport",
          "War",
          "Music",
          "Musical",
          "Animation",
          "Biography",
          "Film-Noir",
      ]






def      lowerCamelCase					(      ):
      return transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,						),
          ])
