
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    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						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    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase=True				)     ->						List[Any]:
		'''simple docstring'''

		model.train()
		UpperCamelCase						=  model(lowercase				)
		UpperCamelCase						=  F.mse_loss(lowercase					, target.to(output.device				)				)
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(lowercase				)


def    A (      lowercase					, lowercase=False				)     ->						Union[str, Any]:
		'''simple docstring'''

		set_seed(42				)
		UpperCamelCase						=  RegressionModel()
		UpperCamelCase						=  deepcopy(lowercase				)
		UpperCamelCase						=  RegressionDataset(length=80				)
		UpperCamelCase						=  DataLoader(lowercase					, batch_size=16				)
		model.to(accelerator.device				)
		if sched:
				UpperCamelCase						=  AdamW(params=model.parameters()					, lr=1e-3				)
				UpperCamelCase						=  AdamW(params=ddp_model.parameters()					, lr=1e-3				)
				UpperCamelCase						=  LambdaLR(lowercase					, lr_lambda=lambda lowercase				: epoch**0.6_5				)
				UpperCamelCase						=  LambdaLR(lowercase					, lr_lambda=lambda lowercase				: epoch**0.6_5				)
		# Make a copy of `model`
		if sched:
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  accelerator.prepare(lowercase					, lowercase					, lowercase					, lowercase				)
		else:
				UpperCamelCase			,						UpperCamelCase						=  accelerator.prepare(lowercase					, lowercase				)
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

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

				# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
				check_model_parameters(lowercase					, lowercase					, lowercase					, lowercase				)
				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(1_337 + iteration				)
				UpperCamelCase						=  ddp_input[torch.randperm(len(lowercase				)				)]


def    A (      lowercase				)     ->						List[Any]:
		'''simple docstring'''

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

				# 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(1_337 + iteration				)
				UpperCamelCase						=  ddp_input[torch.randperm(len(lowercase				)				)]


def    A (      lowercase=False					, lowercase=False				)     ->						str:
		'''simple docstring'''

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

				# 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(lowercase				) - 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(1_337 + iteration				)
				UpperCamelCase						=  ddp_input[torch.randperm(len(lowercase				)				)]
		GradientState._reset_state()


def    A (      lowercase=False					, lowercase=False				)     ->						List[str]:
		'''simple docstring'''

		UpperCamelCase						=  Accelerator(
		    split_batches=lowercase					, dispatch_batches=lowercase					, gradient_accumulation_steps=2				)
		# Test that context manager behaves properly
		UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  get_training_setup(lowercase					, lowercase				)
		for iteration, batch in enumerate(lowercase				):
				UpperCamelCase			,						UpperCamelCase						=  batch.values()
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase			,						UpperCamelCase						=  accelerator.gather((ddp_input, ddp_target)				)
				UpperCamelCase			,						UpperCamelCase						=  input.to(accelerator.device				), target.to(accelerator.device				)
				# Perform our initial ground truth step in non "DDP"
				model.train()
				ddp_model.train()
				step_model(lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)
				opt.step()

				if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase				)):
						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(lowercase				):
						step_model(lowercase					, lowercase					, lowercase					, lowercase				)
						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						=  (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase				))
				if accelerator.num_processes > 1:
						check_model_parameters(lowercase					, lowercase					, lowercase					, lowercase				)
				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration				)
		GradientState._reset_state()


def    A (      )     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  Accelerator()

		UpperCamelCase						=  RegressionDataset(length=80				)
		UpperCamelCase						=  DataLoader(lowercase					, batch_size=16				)
		UpperCamelCase						=  RegressionDataset(length=96				)
		UpperCamelCase						=  DataLoader(lowercase					, batch_size=16				)
		UpperCamelCase			,						UpperCamelCase						=  accelerator.prepare(lowercase					, lowercase				)
		assert accelerator.gradient_state.active_dataloader is None
		for iteration, _ in enumerate(lowercase				):
				assert id(accelerator.gradient_state.active_dataloader				) == id(lowercase				)
				if iteration < len(lowercase				) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
						if iteration == 1:
								for batch_num, _ in enumerate(lowercase				):
										assert id(accelerator.gradient_state.active_dataloader				) == id(lowercase				)
										if batch_num < len(lowercase				) - 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    A (      )     ->						Optional[int]:
		'''simple docstring'''

		UpperCamelCase						=  Accelerator()
		UpperCamelCase						=  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(lowercase				)
		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(lowercase				)
		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(lowercase					, lowercase				)

    # 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(lowercase					, lowercase				)


def    A (      lowercase				)     ->						Any:
		'''simple docstring'''

		main()


if __name__ == "__main__":
					main()


import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset


@pytest.mark.parametrize('dataset_size'					, [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('input_in_memory_max_size'					, ['default', 0, 100 * 2**20, 900 * 2**20]				)
def    A (      lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		if input_in_memory_max_size != "default":
				monkeypatch.setattr(datasets.config					, 'IN_MEMORY_MAX_SIZE'					, lowercase				)
		UpperCamelCase						=  datasets.config.IN_MEMORY_MAX_SIZE
		if input_in_memory_max_size == "default":
				assert in_memory_max_size == 0
		else:
				assert in_memory_max_size == input_in_memory_max_size
		if dataset_size and in_memory_max_size:
				UpperCamelCase						=  dataset_size < in_memory_max_size
		else:
				UpperCamelCase						=  False
		UpperCamelCase						=  is_small_dataset(lowercase				)
		assert result == expected
