

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       lowercase					(	__A :					Union[str, Any] ,    __A :					int ,    __A :					Dict ,    __A :					Optional[int]		)				->			str:





    '''simple docstring'''




    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       lowercase					(	__A :					str ,    __A :					List[str] ,    __A :					Tuple ,    __A :					Dict ,    __A :					Tuple=True		)				->			Tuple:





    '''simple docstring'''




    model.train()
    snake_case							:		List[Any]        =		model(__A		)
    snake_case							:		int        =		F.mse_loss(__A ,    target.to(output.device		)		)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(__A		)







def       lowercase					(	__A :					List[str] ,    __A :					int=False		)				->			Union[str, Any]:





    '''simple docstring'''




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







def       lowercase					(	__A :					List[Any]		)				->			Any:





    '''simple docstring'''




    snake_case      ,		snake_case      ,		snake_case							:		Union[str, Any]        =		get_training_setup(__A		)
    # Use a single batch
    snake_case      ,		snake_case							:		str        =		next(iter(__A		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        snake_case      ,		snake_case							:		List[Any]        =		accelerator.gather((ddp_input, ddp_target)		)
        snake_case      ,		snake_case							:		int        =		input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(__A ,    __A ,    __A ,    __A		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(__A		):
                step_model(__A ,    __A ,    __A ,    __A		)
        else:
            # Sync grads
            step_model(__A ,    __A ,    __A ,    __A		)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(__A ,    __A ,    __A ,    __A		)
        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		)
        snake_case							:		Tuple        =		ddp_input[torch.randperm(len(__A		)		)]







def       lowercase					(	__A :					Optional[int]		)				->			List[str]:





    '''simple docstring'''




    snake_case      ,		snake_case      ,		snake_case							:		str        =		get_training_setup(__A		)
    # Use a single batch
    snake_case      ,		snake_case							:		Any        =		next(iter(__A		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        snake_case      ,		snake_case							:		List[str]        =		accelerator.gather((ddp_input, ddp_target)		)
        snake_case      ,		snake_case							:		Optional[int]        =		input.to(accelerator.device		), target.to(accelerator.device		)
        # Perform our initial ground truth step in non "DDP"
        step_model(__A ,    __A ,    __A ,    __A		)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(__A		):
                step_model(__A ,    __A ,    __A ,    __A		)
        else:
            # Sync grads
            step_model(__A ,    __A ,    __A ,    __A		)

        # 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		)
        snake_case							:		Optional[int]        =		ddp_input[torch.randperm(len(__A		)		)]







def       lowercase					(	__A :					Union[str, Any]=False ,    __A :					Any=False		)				->			Tuple:





    '''simple docstring'''




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

        # 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(__A		) - 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		)
        snake_case							:		Tuple        =		ddp_input[torch.randperm(len(__A		)		)]
    GradientState._reset_state()







def       lowercase					(	__A :					Any=False ,    __A :					str=False		)				->			str:





    '''simple docstring'''




    snake_case							:		Any        =		Accelerator(
        split_batches=__A ,    dispatch_batches=__A ,    gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		int        =		get_training_setup(__A ,    __A		)
    for iteration, batch in enumerate(__A		):
        snake_case      ,		snake_case							:		Optional[Any]        =		batch.values()
        # Gather the distributed inputs and targs for the base model
        snake_case      ,		snake_case							:		List[Any]        =		accelerator.gather((ddp_input, ddp_target)		)
        snake_case      ,		snake_case							:		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(__A ,    __A ,    __A ,    __A ,    __A		)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__A		)):
            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(__A		):
            step_model(__A ,    __A ,    __A ,    __A		)
            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"""
        snake_case							:		List[str]        =		(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__A		))
        if accelerator.num_processes > 1:
            check_model_parameters(__A ,    __A ,    __A ,    __A		)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1337 + iteration		)
    GradientState._reset_state()







def       lowercase					(	)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		List[str]        =		Accelerator()

    snake_case							:		Dict        =		RegressionDataset(length=80		)
    snake_case							:		Tuple        =		DataLoader(__A ,    batch_size=16		)
    snake_case							:		Tuple        =		RegressionDataset(length=96		)
    snake_case							:		Optional[Any]        =		DataLoader(__A ,    batch_size=16		)
    snake_case      ,		snake_case							:		Any        =		accelerator.prepare(__A ,    __A		)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(__A		):
        assert id(accelerator.gradient_state.active_dataloader		) == id(__A		)
        if iteration < len(__A		) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(__A		):
                    assert id(accelerator.gradient_state.active_dataloader		) == id(__A		)
                    if batch_num < len(__A		) - 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       lowercase					(	)				->			List[str]:





    '''simple docstring'''




    snake_case							:		str        =		Accelerator()
    snake_case							:		Dict        =		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(__A		)
    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(__A		)
    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(__A ,    __A		)

    # 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(__A ,    __A		)







def       lowercase					(	__A :					Optional[int]		)				->			Union[str, Any]:





    '''simple docstring'''




    main()


if __name__ == "__main__":
     main()







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





