

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			:					Any						=			logging.getLogger(__name__)





def 	__a     (      A__			:  str=2			,  A__			:  str=3			,  A__			:  Optional[int]=16			,  A__			:  int = 10			,  A__			:  int = 2       ):

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

    SCREAMING_SNAKE_CASE										=   get_dataset(A__       )
    SCREAMING_SNAKE_CASE										=   get_dataset(A__       )
    SCREAMING_SNAKE_CASE										=   DataLoader(A__			,  shuffle=A__			,  batch_size=A__			,  num_workers=4       )
    SCREAMING_SNAKE_CASE										=   DataLoader(A__			,  shuffle=A__			,  batch_size=A__			,  num_workers=4       )
    return (train_dataloader, valid_dataloader)





def 	__a     (      A__			:  Any			,  A__			:  List[str]			,  A__			:  int			,  A__			:  str			,  A__			:  Optional[Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []
    for epoch in range(A__       ):
        # Train quickly
        model.train()
        for batch in dataloader:
            SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   batch
            SCREAMING_SNAKE_CASE										=   model(A__       )
            SCREAMING_SNAKE_CASE										=   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 			_SCREAMING_SNAKE_CASE (   nn.Module		):







   '''simple docstring'''




   def __init__(       self					:			Dict			):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.randn(1			)			)
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.randn(1			)			)





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       return x * self.a + self.b





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			List[str]			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(total_limit=1     , project_dir=__lowerCamelCase     , automatic_checkpoint_naming=__lowerCamelCase			)
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   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					:			List[Any]			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator()
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save initial
           SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , "initial"			)
           accelerator.save_state(__lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           SCREAMING_SNAKE_CASE										=   train(3     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()

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

           SCREAMING_SNAKE_CASE										=   train(2     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save everything
           SCREAMING_SNAKE_CASE										=   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			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Dict			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(automatic_checkpoint_naming=__lowerCamelCase			)

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

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

           SCREAMING_SNAKE_CASE										=   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			)
           ((SCREAMING_SNAKE_CASE)							,	(SCREAMING_SNAKE_CASE))										=   model.a.item(), model.b.item()
           SCREAMING_SNAKE_CASE										=   optimizer.state_dict()
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   torch.tensor([1, 2, 3]			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([2, 3, 4]			)
       SCREAMING_SNAKE_CASE										=   DummyModel()
       SCREAMING_SNAKE_CASE										=   torch.optim.Adam(net.parameters()			)
       SCREAMING_SNAKE_CASE										=   Accelerator()
       with self.assertRaises(__lowerCamelCase			) as ve:
           accelerator.register_for_checkpointing(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
       SCREAMING_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      _snake_case		(       self					:			Tuple			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   torch.optim.Adam(params=model.parameters()     , lr=1e-3			)
           SCREAMING_SNAKE_CASE										=   torch.optim.lr_scheduler.StepLR(__lowerCamelCase     , step_size=1     , gamma=0.99			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   dummy_dataloaders()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(automatic_checkpoint_naming=__lowerCamelCase			)
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_dir=__lowerCamelCase     , project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   accelerator.prepare(
               __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)
           # Save initial
           accelerator.save_state()
           SCREAMING_SNAKE_CASE										=   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					:			Any			):
       with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42			)
           SCREAMING_SNAKE_CASE										=   DummyModel()
           SCREAMING_SNAKE_CASE										=   ProjectConfiguration(automatic_checkpoint_naming=__lowerCamelCase     , total_limit=2			)
           # Train baseline
           SCREAMING_SNAKE_CASE										=   Accelerator(project_dir=__lowerCamelCase     , project_config=__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   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					:			int			):
       SCREAMING_SNAKE_CASE										=   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__			)]
       execute_subprocess_async(__lowerCamelCase     , env=os.environ.copy()			)


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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)