



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  UpperCamelCase						(       _A			:				List[str]			,							_A			:				str			,							_A			:				Dict			,							_A			:				Tuple    )->       int:





						"""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  UpperCamelCase						(       _A			:				int			,							_A			:				Optional[int]			,							_A			:				Dict			,							_A			:				List[Any]			,							_A			:				int=True    )->       str:





						"""simple docstring"""





						model.train()
						A__							=       model(_A    )
						A__							=       F.mse_loss(_A			,							target.to(output.device    )    )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(_A    )
def  UpperCamelCase						(       _A			:				str			,							_A			:				List[Any]=False    )->       str:





						"""simple docstring"""





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





						"""simple docstring"""





						A__						,       A__						,       A__							=       get_training_setup(_A    )
						# Use a single batch
						A__						,       A__							=       next(iter(_A    )    ).values()
						for iteration in range(3    ):
												# Gather the distributed inputs and targs for the base model
												A__						,       A__							=       accelerator.gather((ddp_input, ddp_target)    )
												A__						,       A__							=       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    )
												A__							=       ddp_input[torch.randperm(len(_A    )    )]
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[str]:





						"""simple docstring"""





						A__						,       A__						,       A__							=       get_training_setup(_A    )
						# Use a single batch
						A__						,       A__							=       next(iter(_A    )    ).values()
						for iteration in range(3    ):
												# Gather the distributed inputs and targs for the base model
												A__						,       A__							=       accelerator.gather((ddp_input, ddp_target)    )
												A__						,       A__							=       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    )
												A__							=       ddp_input[torch.randperm(len(_A    )    )]
def  UpperCamelCase						(       _A			:				Tuple=False			,							_A			:				Any=False    )->       int:





						"""simple docstring"""





						A__							=       Accelerator(
						    split_batches=_A			,							dispatch_batches=_A			,							gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						A__						,       A__						,       A__							=       get_training_setup(_A    )
						for iteration, batch in enumerate(_A    ):
												A__						,       A__							=       batch.values()
												# Gather the distributed inputs and targs for the base model
												A__						,       A__							=       accelerator.gather((ddp_input, ddp_target)    )
												A__						,       A__							=       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    )
												A__							=       ddp_input[torch.randperm(len(_A    )    )]
						GradientState._reset_state()
def  UpperCamelCase						(       _A			:				int=False			,							_A			:				Optional[int]=False    )->       int:





						"""simple docstring"""





						A__							=       Accelerator(
						    split_batches=_A			,							dispatch_batches=_A			,							gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						A__						,       A__						,       A__						,       A__						,       A__						,       A__						,       A__							=       get_training_setup(_A			,							_A    )
						for iteration, batch in enumerate(_A    ):
												A__						,       A__							=       batch.values()
												# Gather the distributed inputs and targs for the base model
												A__						,       A__							=       accelerator.gather((ddp_input, ddp_target)    )
												A__						,       A__							=       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"""
												A__							=       (((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  UpperCamelCase						(       )->       Optional[int]:





						"""simple docstring"""





						A__							=       Accelerator()

						A__							=       RegressionDataset(length=80    )
						A__							=       DataLoader(_A			,							batch_size=16    )
						A__							=       RegressionDataset(length=96    )
						A__							=       DataLoader(_A			,							batch_size=16    )
						A__						,       A__							=       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  UpperCamelCase						(       )->       int:





						"""simple docstring"""





						A__							=       Accelerator()
						A__							=       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  UpperCamelCase						(       _A			:				List[str]    )->       Optional[Any]:





						"""simple docstring"""





						main()


if __name__ == "__main__":
						main()










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None
						if token is not None:
												A__							=       {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

						A__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		with zipfile.ZipFile(_A    ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(_A    ):
																																				# read the file
																																				with z.open(_A    ) as f:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





