"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__lowerCamelCase							 =     pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
__lowerCamelCase							 =     dataset.iloc[:, 1:2].values
__lowerCamelCase							 =     dataset.iloc[:, 2].values


__lowerCamelCase ,      __lowerCamelCase ,      __lowerCamelCase ,      __lowerCamelCase							 =     train_test_split(X, y, test_size=0.2, random_state=0)


__lowerCamelCase							 =     PolynomialFeatures(degree=4)
__lowerCamelCase							 =     poly_reg.fit_transform(X)
__lowerCamelCase							 =     LinearRegression()
pol_reg.fit(X_poly, y)






def      UpperCAmelCase    (		):






							"""simple docstring"""





							plt.scatter(UpperCamelCase__				,			UpperCamelCase__				,			color='red' )
							plt.plot(UpperCamelCase__				,			pol_reg.predict(poly_reg.fit_transform(UpperCamelCase__ ) )				,			color='blue' )
							plt.title('Truth or Bluff (Linear Regression)' )
							plt.xlabel('Position level' )
							plt.ylabel('Salary' )
							plt.show()


if __name__ == "__main__":
				viz_polymonial()

				# Predicting a new result with Polymonial Regression
				pol_reg.predict(poly_reg.fit_transform([[5.5]]))
				# output should be 132148.43750003






"""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    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""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    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=True ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





							set_seed(42 )
							A__		   =							RegressionModel()
							A__		   =							deepcopy(UpperCamelCase__ )
							A__		   =							RegressionDataset(length=80 )
							A__		   =							DataLoader(UpperCamelCase__				,			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(UpperCamelCase__				,			lr_lambda=lambda UpperCamelCase__ : epoch**0.6_5 )
														A__		   =							LambdaLR(UpperCamelCase__				,			lr_lambda=lambda UpperCamelCase__ : epoch**0.6_5 )
							# Make a copy of `model`
							if sched:
														A__   ,     A__   ,     A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
							else:
														A__   ,     A__		   =							accelerator.prepare(UpperCamelCase__				,			UpperCamelCase__ )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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

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






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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

														# 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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





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

														# 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(UpperCamelCase__ ) - 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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]
							GradientState._reset_state()






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__				,			UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														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(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )
														opt.step()

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






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Accelerator()

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





							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(UpperCamelCase__ )
							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(UpperCamelCase__ )
							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(UpperCamelCase__				,			UpperCamelCase__ )

    # 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(UpperCamelCase__				,			UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
				main()





