



"""simple docstring"""
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Tuple							):
    for param, grad_param in zip(model_a.parameters()     ,       model_b.parameters()							):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad     ,       grad_param.grad							) is False
            ), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad     ,       grad_param.grad							) is True
            ), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Dict=True							):
    model.train()
    _UpperCAmelCase							:  Union[str, Any]											=    model(UpperCamelCase__							)
    _UpperCAmelCase							:  Dict											=    F.mse_loss(UpperCamelCase__     ,       target.to(output.device							)							)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Optional[Any]=False							):
    set_seed(42							)
    _UpperCAmelCase							:  Any											=    RegressionModel()
    _UpperCAmelCase							:  List[str]											=    deepcopy(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    RegressionDataset(length=80							)
    _UpperCAmelCase							:  Optional[Any]											=    DataLoader(UpperCamelCase__     ,       batch_size=16							)
    model.to(accelerator.device							)
    if sched:
        _UpperCAmelCase							:  Optional[Any]											=    AdamW(params=model.parameters()     ,       lr=1E-3							)
        _UpperCAmelCase							:  Optional[int]											=    AdamW(params=ddp_model.parameters()     ,       lr=1E-3							)
        _UpperCAmelCase							:  Union[str, Any]											=    LambdaLR(UpperCamelCase__     ,       lr_lambda=lambda UpperCamelCase__							: epoch**0.65							)
        _UpperCAmelCase							:  Any											=    LambdaLR(UpperCamelCase__     ,       lr_lambda=lambda UpperCamelCase__							: epoch**0.65							)
    # Make a copy of `model`
    if sched:
        _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    else:
        _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    accelerator.prepare(UpperCamelCase__     ,       UpperCamelCase__							)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]							):
    # Test when on a single CPU or GPU that the context manager does nothing
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    get_training_setup(UpperCamelCase__							)
    # Use a single batch
    _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    next(iter(UpperCamelCase__							)							).values()
    for iteration in range(3							):
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.gather((ddp_input, ddp_target)							)
        _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase__							):
                step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        else:
            # Sync grads
            step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        for param, ddp_param in zip(model.parameters()     ,       ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad     ,       ddp_param.grad							), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration							)
        _UpperCAmelCase							:  Union[str, Any]											=    ddp_input[torch.randperm(len(UpperCamelCase__							)							)]





def  lowerCamelCase_						(UpperCamelCase__		:		Dict							):
    # Test on distributed setup that context manager behaves properly
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    get_training_setup(UpperCamelCase__							)
    # Use a single batch
    _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[int]											=    next(iter(UpperCamelCase__							)							).values()
    for iteration in range(3							):
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    accelerator.gather((ddp_input, ddp_target)							)
        _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase__							):
                step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        else:
            # Sync grads
            step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()     ,       ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad     ,       ddp_param.grad							) is False
                ), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad     ,       ddp_param.grad							) is True
                ), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration							)
        _UpperCAmelCase							:  Tuple											=    ddp_input[torch.randperm(len(UpperCamelCase__							)							)]





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]=False     ,       UpperCamelCase__		:		str=False							):
    _UpperCAmelCase							:  List[Any]											=    Accelerator(
        split_batches=UpperCamelCase__     ,       dispatch_batches=UpperCamelCase__     ,       gradient_accumulation_steps=2							)
    # Test that context manager behaves properly
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    get_training_setup(UpperCamelCase__							)
    for iteration, batch in enumerate(UpperCamelCase__							):
        _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    batch.values()
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    accelerator.gather((ddp_input, ddp_target)							)
        _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(UpperCamelCase__							):
            step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()     ,       ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__							) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad     ,       ddp_param.grad							) is True
                ), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad     ,       ddp_param.grad							) is False
                ), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration							)
        _UpperCAmelCase							:  str											=    ddp_input[torch.randperm(len(UpperCamelCase__							)							)]
    GradientState._reset_state()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]=False     ,       UpperCamelCase__		:		Union[str, Any]=False							):
    _UpperCAmelCase							:  List[str]											=    Accelerator(
        split_batches=UpperCamelCase__     ,       dispatch_batches=UpperCamelCase__     ,       gradient_accumulation_steps=2							)
    # Test that context manager behaves properly
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    get_training_setup(UpperCamelCase__     ,       UpperCamelCase__							)
    for iteration, batch in enumerate(UpperCamelCase__							):
        _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    batch.values()
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    accelerator.gather((ddp_input, ddp_target)							)
        _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__							)):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes							):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(UpperCamelCase__							):
            step_model(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
        _UpperCAmelCase							:  List[Any]											=    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__							))
        if accelerator.num_processes > 1:
            check_model_parameters(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration							)
    GradientState._reset_state()





def  lowerCamelCase_						():
    _UpperCAmelCase							:  List[str]											=    Accelerator()

    _UpperCAmelCase							:  Union[str, Any]											=    RegressionDataset(length=80							)
    _UpperCAmelCase							:  Any											=    DataLoader(UpperCamelCase__     ,       batch_size=16							)
    _UpperCAmelCase							:  List[Any]											=    RegressionDataset(length=96							)
    _UpperCAmelCase							:  Union[str, Any]											=    DataLoader(UpperCamelCase__     ,       batch_size=16							)
    _UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    accelerator.prepare(UpperCamelCase__     ,       UpperCamelCase__							)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(UpperCamelCase__							):
        assert id(accelerator.gradient_state.active_dataloader							) == id(UpperCamelCase__							)
        if iteration < len(UpperCamelCase__							) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(UpperCamelCase__							):
                    assert id(accelerator.gradient_state.active_dataloader							) == id(UpperCamelCase__							)
                    if batch_num < len(UpperCamelCase__							) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None





def  lowerCamelCase_						():
    _UpperCAmelCase							:  List[Any]											=    Accelerator()
    _UpperCAmelCase							:  str											=    accelerator.state
    if state.local_process_index == 0:
        print('''**Test `accumulate` gradient accumulation with dataloader break**'''							)
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print('''**Test NOOP `no_sync` context manager**'''							)
        test_noop_sync(UpperCamelCase__							)
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print('''**Test Distributed `no_sync` context manager**'''							)
        test_distributed_sync(UpperCamelCase__							)
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        '''**Test `accumulate` gradient accumulation, '''     ,       F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'     ,       )
                test_gradient_accumulation(UpperCamelCase__     ,       UpperCamelCase__							)

    # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version('''<'''     ,       '''2.0'''							) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''     ,       '''`split_batches=False`, `dispatch_batches=False`**'''     ,       )
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''     ,       F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'     ,       )
                    test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__     ,       UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]							):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


