


"""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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''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__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=True     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


					set_seed(42     )
					__SCREAMING_SNAKE_CASE		     =       RegressionModel()
					__SCREAMING_SNAKE_CASE		     =       deepcopy(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       RegressionDataset(length=80     )
					__SCREAMING_SNAKE_CASE		     =       DataLoader(lowerCAmelCase_						,		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(lowerCAmelCase_						,		lr_lambda=lambda lowerCAmelCase_     : epoch**0.65     )
										__SCREAMING_SNAKE_CASE		     =       LambdaLR(lowerCAmelCase_						,		lr_lambda=lambda lowerCAmelCase_     : epoch**0.65     )
					# Make a copy of `model`
					if sched:
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       accelerator.prepare(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       accelerator.prepare(lowerCAmelCase_						,		lowerCAmelCase_     )
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_training_setup(lowerCAmelCase_     )
					# Use a single batch
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       next(iter(lowerCAmelCase_     )     ).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(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(lowerCAmelCase_     ):
																				step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										else:
															# Sync grads
															step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )

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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_training_setup(lowerCAmelCase_     )
					# Use a single batch
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       next(iter(lowerCAmelCase_     )     ).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(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(lowerCAmelCase_     ):
																				step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										else:
															# Sync grads
															step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )

										# 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     )
										__SCREAMING_SNAKE_CASE		     =       ddp_input[torch.randperm(len(lowerCAmelCase_     )     )]




def      UpperCAmelCase__ (lowerCAmelCase_=False						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Accelerator(
					    split_batches=lowerCAmelCase_						,		dispatch_batches=lowerCAmelCase_						,		gradient_accumulation_steps=2     )
					# Test that context manager behaves properly
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_training_setup(lowerCAmelCase_     )
					for iteration, batch in enumerate(lowerCAmelCase_     ):
										__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(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										# Do "gradient accumulation" (noop)
										with accelerator.accumulate(lowerCAmelCase_     ):
															step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )

										# 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(lowerCAmelCase_     ) - 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     )
										__SCREAMING_SNAKE_CASE		     =       ddp_input[torch.randperm(len(lowerCAmelCase_     )     )]
					GradientState._reset_state()




def      UpperCAmelCase__ (lowerCAmelCase_=False						,		lowerCAmelCase_=False     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Accelerator(
					    split_batches=lowerCAmelCase_						,		dispatch_batches=lowerCAmelCase_						,		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(lowerCAmelCase_						,		lowerCAmelCase_     )
					for iteration, batch in enumerate(lowerCAmelCase_     ):
										__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(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase_     )):
															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(lowerCAmelCase_     ):
															step_model(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
															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(lowerCAmelCase_     ))
										if accelerator.num_processes > 1:
															check_model_parameters(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration     )
					GradientState._reset_state()




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Accelerator()

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




					'''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(lowerCAmelCase_     )
					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(lowerCAmelCase_     )
					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(lowerCAmelCase_						,		lowerCAmelCase_     )

    # 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(lowerCAmelCase_						,		lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()




"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return (data["data"], data["target"])




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		display_labels=lowerCAmelCase_						,		cmap="Blues"						,		normalize="true"						,		)
					plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
					plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
