"""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


__lowercase		:		Dict								=				logging.getLogger(__name__)





def  SCREAMING_SNAKE_CASE							(  snake_case=2,   snake_case=3,   snake_case=16,   snake_case = 10,   snake_case = 2):

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

     __snake_case				=				get_dataset(UpperCamelCase__)
     __snake_case				=				get_dataset(UpperCamelCase__)
     __snake_case				=				DataLoader(UpperCamelCase__,   shuffle=UpperCamelCase__,   batch_size=UpperCamelCase__,   num_workers=4)
     __snake_case				=				DataLoader(UpperCamelCase__,   shuffle=UpperCamelCase__,   batch_size=UpperCamelCase__,   num_workers=4)
     return (train_dataloader, valid_dataloader)





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case,   snake_case,   snake_case,   snake_case,   snake_case=None):
     __snake_case				=				[]
     for epoch in range(UpperCamelCase__):
          # Train quickly
          model.train()
          for batch in dataloader:
               __snake_case				=				batch
               __snake_case				=				model(UpperCamelCase__)
               __snake_case				=				torch.nn.functional.mse_loss(UpperCamelCase__,   UpperCamelCase__)
               accelerator.backward(UpperCamelCase__)
               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				:  Dict     )     ->							Dict:
         super().__init__()
         __snake_case				=				nn.Parameter(torch.randn(1     )     )
         __snake_case				=				nn.Parameter(torch.randn(1     )     )







    def 			lowercase       (		self				:  Tuple						,   A_				:  Optional[Any]     )     ->							Any:
         return x * self.a + self.b






class   _A						(    unittest.TestCase       ):




    """simple docstring"""




    def 			lowercase       (		self				:  Tuple     )     ->							Optional[int]:







         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				torch.optim.Adam(params=model.parameters()						,   lr=1E-3     )
              __snake_case				=				dummy_dataloaders()
              __snake_case				=				ProjectConfiguration(total_limit=1						,   project_dir=__SCREAMING_SNAKE_CASE						,   automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE     )
              # Train baseline
              __snake_case				=				Accelerator(project_config=__SCREAMING_SNAKE_CASE     )
              __snake_case				=				accelerator.prepare(
                  __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              # Save initial
              accelerator.save_state()

              # Save second state
              accelerator.save_state()
              self.assertEqual(len(os.listdir(accelerator.project_dir     )     )						,   1     )
    def 			lowercase       (		self				:  Any     )     ->							Tuple:







         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				torch.optim.Adam(params=model.parameters()						,   lr=1E-3     )
              __snake_case				=				dummy_dataloaders()
              # Train baseline
              __snake_case				=				Accelerator()
              __snake_case				=				accelerator.prepare(
                  __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              # Save initial
              __snake_case				=				os.path.join(__SCREAMING_SNAKE_CASE						,   '''initial'''     )
              accelerator.save_state(__SCREAMING_SNAKE_CASE     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()
              __snake_case				=				train(3						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()

              # Train partially
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				torch.optim.Adam(params=model.parameters()						,   lr=1E-3     )
              __snake_case				=				dummy_dataloaders()
              __snake_case				=				Accelerator()
              __snake_case				=				accelerator.prepare(
                  __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              accelerator.load_state(__SCREAMING_SNAKE_CASE     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )

              __snake_case				=				train(2						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              # Save everything
              __snake_case				=				os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoint'''     )
              accelerator.save_state(__SCREAMING_SNAKE_CASE     )

              # Load everything back in and make sure all states work
              accelerator.load_state(__SCREAMING_SNAKE_CASE     )
              test_rands += train(1						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Optional[int]     )     ->							Tuple:







         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				torch.optim.Adam(params=model.parameters()						,   lr=1E-3     )
              __snake_case				=				dummy_dataloaders()
              __snake_case				=				ProjectConfiguration(automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE     )

              # Train baseline
              __snake_case				=				Accelerator(project_dir=__SCREAMING_SNAKE_CASE						,   project_config=__SCREAMING_SNAKE_CASE     )
              __snake_case				=				accelerator.prepare(
                  __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              # Save initial
              accelerator.save_state()
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()
              __snake_case				=				train(3						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()

              # Train partially
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				torch.optim.Adam(params=model.parameters()						,   lr=1E-3     )
              __snake_case				=				dummy_dataloaders()
              __snake_case				=				ProjectConfiguration(iteration=1						,   automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE     )
              __snake_case				=				Accelerator(project_dir=__SCREAMING_SNAKE_CASE						,   project_config=__SCREAMING_SNAKE_CASE     )
              __snake_case				=				accelerator.prepare(
                  __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              accelerator.load_state(os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoints'''						,   '''checkpoint_0'''     )     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )

              __snake_case				=				train(2						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              # Save everything
              accelerator.save_state()

              # Load everything back in and make sure all states work
              accelerator.load_state(os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoints'''						,   '''checkpoint_1'''     )     )
              test_rands += train(1						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              (__snake_case)				=				model.a.item(), model.b.item()
              __snake_case				=				optimizer.state_dict()
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Dict     )     ->							Union[str, Any]:
         __snake_case				=				torch.tensor([1, 2, 3]     )
         __snake_case				=				torch.tensor([2, 3, 4]     )
         __snake_case				=				DummyModel()
         __snake_case				=				torch.optim.Adam(net.parameters()     )
         __snake_case				=				Accelerator()
         with self.assertRaises(__SCREAMING_SNAKE_CASE     ) as ve:
              accelerator.register_for_checkpointing(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
         __snake_case				=				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 			lowercase       (		self				:  str     )     ->							Optional[int]:







         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				torch.optim.Adam(params=model.parameters()						,   lr=1E-3     )
              __snake_case				=				torch.optim.lr_scheduler.StepLR(__SCREAMING_SNAKE_CASE						,   step_size=1						,   gamma=0.99     )
              __snake_case				=				dummy_dataloaders()
              __snake_case				=				ProjectConfiguration(automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE     )
              # Train baseline
              __snake_case				=				Accelerator(project_dir=__SCREAMING_SNAKE_CASE						,   project_config=__SCREAMING_SNAKE_CASE     )
              __snake_case				=				accelerator.prepare(
                  __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              # Save initial
              accelerator.save_state()
              __snake_case				=				scheduler.state_dict()
              train(3						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
              self.assertNotEqual(__SCREAMING_SNAKE_CASE						,   scheduler.state_dict()     )

              # Load everything back in and make sure all states work
              accelerator.load_state(os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoints'''						,   '''checkpoint_0'''     )     )
              self.assertEqual(__SCREAMING_SNAKE_CASE						,   scheduler.state_dict()     )
    def 			lowercase       (		self				:  Any     )     ->							Tuple:







         with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42     )
              __snake_case				=				DummyModel()
              __snake_case				=				ProjectConfiguration(automatic_checkpoint_naming=__SCREAMING_SNAKE_CASE						,   total_limit=2     )
              # Train baseline
              __snake_case				=				Accelerator(project_dir=__SCREAMING_SNAKE_CASE						,   project_config=__SCREAMING_SNAKE_CASE     )
              __snake_case				=				accelerator.prepare(__SCREAMING_SNAKE_CASE     )
              # Save 3 states:
              for _ in range(11     ):
                   accelerator.save_state()
              self.assertTrue(not os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoints'''						,   '''checkpoint_0'''     )     )     )
              self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoints'''						,   '''checkpoint_9'''     )     )     )
              self.assertTrue(os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE						,   '''checkpoints'''						,   '''checkpoint_10'''     )     )     )







    @require_cuda
    def 			lowercase       (		self				:  int     )     ->							Dict:
         __snake_case				=				['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__     )]
         execute_subprocess_async(__SCREAMING_SNAKE_CASE						,   env=os.environ.copy()     )


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

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


