



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


_snake_case	=						logging.getLogger(__name__)






def        A       (      _lowerCamelCase=2       ,				_lowerCamelCase=3       ,				_lowerCamelCase=16       ,				_lowerCamelCase = 10       ,				_lowerCamelCase = 2						):





							'''simple docstring'''




							def get_dataset(_lowerCamelCase						):
														_lowerCAmelCase  :							Tuple				     =					torch.randn(batch_size * n_batches       ,				1						)
														return TensorDataset(__lowerCAmelCase       ,				a * x + b + 0.1 * torch.randn(batch_size * n_batches       ,				1						)						)

							_lowerCAmelCase  :							Dict				     =					get_dataset(__lowerCAmelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					get_dataset(__lowerCAmelCase						)
							_lowerCAmelCase  :							Any				     =					DataLoader(__lowerCAmelCase       ,				shuffle=__lowerCAmelCase       ,				batch_size=__lowerCAmelCase       ,				num_workers=4						)
							_lowerCAmelCase  :							str				     =					DataLoader(__lowerCAmelCase       ,				shuffle=__lowerCAmelCase       ,				batch_size=__lowerCAmelCase       ,				num_workers=4						)
							return (train_dataloader, valid_dataloader)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					[]
							for epoch in range(__lowerCAmelCase						):
														# Train quickly
														model.train()
														for batch in dataloader:
																					_lowerCAmelCase  :							Optional[int]				     =					batch
																					_lowerCAmelCase  :							Optional[int]				     =					model(__lowerCAmelCase						)
																					_lowerCAmelCase  :							Optional[int]				     =					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 						UpperCAmelCase_					( nn.Module):







							def __init__(     self):

														'''simple docstring'''

														super().__init__()
														_lowerCAmelCase  :							Any				     =					nn.Parameter(torch.randn(1))
														_lowerCAmelCase  :							Tuple				     =					nn.Parameter(torch.randn(1))







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return x * self.a + self.b



class 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     self):

														'''simple docstring'''

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

																					# Save second state
																					accelerator.save_state()
																					self.assertEqual(len(os.listdir(accelerator.project_dir)),    1)







							def      snake_case__   (     self):

														'''simple docstring'''

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

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

																					_lowerCAmelCase  :							Optional[Any]				     =					train(2,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__)
																					# Save everything
																					_lowerCAmelCase  :							Optional[Any]				     =					os.path.join(lowerCamelCase__,    "checkpoint")
																					accelerator.save_state(lowerCamelCase__)

																					# Load everything back in and make sure all states work
																					accelerator.load_state(lowerCamelCase__)
																					test_rands += train(1,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__)
																					(_lowerCAmelCase)  :							Union[str, Any]				     =					model.a.item(), model.b.item()
																					_lowerCAmelCase  :							Dict				     =					optimizer.state_dict()
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)







							def      snake_case__   (     self):

														'''simple docstring'''

														with tempfile.TemporaryDirectory() as tmpdir:
																					set_seed(42)
																					_lowerCAmelCase  :							Optional[Any]				     =					DummyModel()
																					_lowerCAmelCase  :							int				     =					torch.optim.Adam(params=model.parameters(),    lr=1E-3)
																					_lowerCAmelCase  :							Any				     =					dummy_dataloaders()
																					_lowerCAmelCase  :							Union[str, Any]				     =					ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__)

																					# Train baseline
																					_lowerCAmelCase  :							str				     =					Accelerator(project_dir=lowerCamelCase__,    project_config=lowerCamelCase__)
																					_lowerCAmelCase  :							Optional[Any]				     =					accelerator.prepare(
																					    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__)
																					# Save initial
																					accelerator.save_state()
																					(_lowerCAmelCase)  :							Optional[Any]				     =					model.a.item(), model.b.item()
																					_lowerCAmelCase  :							Union[str, Any]				     =					optimizer.state_dict()
																					_lowerCAmelCase  :							Optional[Any]				     =					train(3,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__)
																					(_lowerCAmelCase)  :							Dict				     =					model.a.item(), model.b.item()
																					_lowerCAmelCase  :							Optional[Any]				     =					optimizer.state_dict()

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

																					_lowerCAmelCase  :							str				     =					train(2,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__)
																					# Save everything
																					accelerator.save_state()

																					# Load everything back in and make sure all states work
																					accelerator.load_state(os.path.join(lowerCamelCase__,    "checkpoints",    "checkpoint_1"))
																					test_rands += train(1,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__)
																					(_lowerCAmelCase)  :							Dict				     =					model.a.item(), model.b.item()
																					_lowerCAmelCase  :							Dict				     =					optimizer.state_dict()
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)
																					self.assertEqual(lowerCamelCase__,    lowerCamelCase__)







							def      snake_case__   (     self):

														'''simple docstring'''

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

														'''simple docstring'''

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

																					# Load everything back in and make sure all states work
																					accelerator.load_state(os.path.join(lowerCamelCase__,    "checkpoints",    "checkpoint_0"))
																					self.assertEqual(lowerCamelCase__,    scheduler.state_dict())







							def      snake_case__   (     self):

														'''simple docstring'''

														with tempfile.TemporaryDirectory() as tmpdir:
																					set_seed(42)
																					_lowerCAmelCase  :							Dict				     =					DummyModel()
																					_lowerCAmelCase  :							int				     =					ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__,    total_limit=2)
																					# Train baseline
																					_lowerCAmelCase  :							Optional[int]				     =					Accelerator(project_dir=lowerCamelCase__,    project_config=lowerCamelCase__)
																					_lowerCAmelCase  :							Union[str, Any]				     =					accelerator.prepare(lowerCamelCase__)
																					# Save 3 states:
																					for _ in range(11):
																												accelerator.save_state()
																					self.assertTrue(not os.path.exists(os.path.join(lowerCamelCase__,    "checkpoints",    "checkpoint_0")))
																					self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,    "checkpoints",    "checkpoint_9")))
																					self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,    "checkpoints",    "checkpoint_10")))







							@require_cuda
							def      snake_case__   (     self):

														'''simple docstring'''

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


if __name__ == "__main__":
						_snake_case	=						"/tmp/accelerate/state_checkpointing"
						_snake_case	=						DummyModel()
						_snake_case	=						torch.optim.Adam(params=model.parameters(), lr=1e-3)
						_snake_case	=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
						_snake_case,		_snake_case	=						dummy_dataloaders()
						_snake_case	=						ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						_snake_case	=						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)
						_snake_case,		_snake_case,		_snake_case,		_snake_case,		_snake_case	=						accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						_snake_case,		_snake_case	=						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:
												_snake_case	=						group["params"][0].device
												break
						assert param_device.type == accelerator.device.type
						_snake_case	=						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:
												_snake_case	=						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:
												_snake_case	=						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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'data2vec-vision'







							def __init__(     self,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.0,    __a=0.0,    __a=0.02,    __a=1E-12,    __a=224,    __a=16,    __a=3,    __a=False,    __a=False,    __a=False,    __a=False,    __a=0.1,    __a=0.1,    __a=True,    __a=[3, 5, 7, 11],    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							Dict				     =					hidden_size
														_lowerCAmelCase  :							List[Any]				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					num_attention_heads
														_lowerCAmelCase  :							str				     =					intermediate_size
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_act
														_lowerCAmelCase  :							int				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Dict				     =					initializer_range
														_lowerCAmelCase  :							List[str]				     =					layer_norm_eps

														_lowerCAmelCase  :							Optional[int]				     =					image_size
														_lowerCAmelCase  :							List[Any]				     =					patch_size
														_lowerCAmelCase  :							Optional[Any]				     =					num_channels
														_lowerCAmelCase  :							str				     =					use_mask_token
														_lowerCAmelCase  :							List[str]				     =					use_absolute_position_embeddings
														_lowerCAmelCase  :							str				     =					use_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					use_shared_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					layer_scale_init_value
														_lowerCAmelCase  :							List[Any]				     =					drop_path_rate
														_lowerCAmelCase  :							Union[str, Any]				     =					use_mean_pooling
														# decode head attributes (semantic segmentation)
														_lowerCAmelCase  :							Tuple				     =					out_indices
														_lowerCAmelCase  :							Tuple				     =					pool_scales
														# auxiliary head attributes (semantic segmentation)
														_lowerCAmelCase  :							Optional[int]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							int				     =					auxiliary_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_num_convs
														_lowerCAmelCase  :							int				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					semantic_loss_ignore_index



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4
