

"""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 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''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       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=True    ):




			'''simple docstring'''


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

def 	A       (     snake_case__		,				snake_case__=False    ):




			'''simple docstring'''


			set_seed(42    )
			SCREAMING_SNAKE_CASE__								=      RegressionModel()
			SCREAMING_SNAKE_CASE__								=      deepcopy(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      RegressionDataset(length=80    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				batch_size=16    )
			model.to(accelerator.device    )
			if sched:
						SCREAMING_SNAKE_CASE__								=      AdamW(params=model.parameters()		,				lr=1e-3    )
						SCREAMING_SNAKE_CASE__								=      AdamW(params=ddp_model.parameters()		,				lr=1e-3    )
						SCREAMING_SNAKE_CASE__								=      LambdaLR(snake_case__		,				lr_lambda=lambda snake_case__    : epoch**0.65    )
						SCREAMING_SNAKE_CASE__								=      LambdaLR(snake_case__		,				lr_lambda=lambda snake_case__    : epoch**0.65    )
			# Make a copy of `model`
			if sched:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      accelerator.prepare(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      accelerator.prepare(snake_case__		,				snake_case__    )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
						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(13_37 + iteration    )
						SCREAMING_SNAKE_CASE__								=      ddp_input[torch.randperm(len(snake_case__    )    )]

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

						# 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(13_37 + iteration    )
						SCREAMING_SNAKE_CASE__								=      ddp_input[torch.randperm(len(snake_case__    )    )]

def 	A       (     snake_case__=False		,				snake_case__=False    ):




			'''simple docstring'''


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

						# 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(snake_case__    ) - 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(13_37 + iteration    )
						SCREAMING_SNAKE_CASE__								=      ddp_input[torch.randperm(len(snake_case__    )    )]
			GradientState._reset_state()

def 	A       (     snake_case__=False		,				snake_case__=False    ):




			'''simple docstring'''


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

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case__    )):
									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(snake_case__    ):
									step_model(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
									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"""
						SCREAMING_SNAKE_CASE__								=      (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case__    ))
						if accelerator.num_processes > 1:
									check_model_parameters(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(13_37 + iteration    )
			GradientState._reset_state()

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Accelerator()

			SCREAMING_SNAKE_CASE__								=      RegressionDataset(length=80    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				batch_size=16    )
			SCREAMING_SNAKE_CASE__								=      RegressionDataset(length=96    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				batch_size=16    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      accelerator.prepare(snake_case__		,				snake_case__    )
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(snake_case__    ):
						assert id(accelerator.gradient_state.active_dataloader    ) == id(snake_case__    )
						if iteration < len(snake_case__    ) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(snake_case__    ):
															assert id(accelerator.gradient_state.active_dataloader    ) == id(snake_case__    )
															if batch_num < len(snake_case__    ) - 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       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      Accelerator()
			SCREAMING_SNAKE_CASE__								=      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(snake_case__    )
			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(snake_case__    )
			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(snake_case__		,				snake_case__    )

    # 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(snake_case__		,				snake_case__    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			main()


if __name__ == "__main__":
						main()





"""simple docstring"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


