


'''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 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_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 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__=True     ):







				"""simple docstring"""





				model.train()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      F.mse_loss(SCREAMING_SNAKE_CASE__							,      target.to(output.device     )     )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(SCREAMING_SNAKE_CASE__     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





				set_seed(42     )
				_SCREAMING_SNAKE_CASE :       int     		=      RegressionModel()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      deepcopy(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Tuple     		=      RegressionDataset(length=80     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      DataLoader(SCREAMING_SNAKE_CASE__							,      batch_size=16     )
				model.to(accelerator.device     )
				if sched:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      AdamW(params=model.parameters()							,      lr=1E-3     )
								_SCREAMING_SNAKE_CASE :       Dict     		=      AdamW(params=ddp_model.parameters()							,      lr=1E-3     )
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      LambdaLR(SCREAMING_SNAKE_CASE__							,      lr_lambda=lambda SCREAMING_SNAKE_CASE__     : epoch**0.6_5     )
								_SCREAMING_SNAKE_CASE :       Dict     		=      LambdaLR(SCREAMING_SNAKE_CASE__							,      lr_lambda=lambda SCREAMING_SNAKE_CASE__     : epoch**0.6_5     )
				# Make a copy of `model`
				if sched:
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       str     		=      accelerator.prepare(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				else:
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      accelerator.prepare(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_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(1337 + iteration     )
								_SCREAMING_SNAKE_CASE :       Any     		=      ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE__     )     )]







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      get_training_setup(SCREAMING_SNAKE_CASE__     )
				# Use a single batch
				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      next(iter(SCREAMING_SNAKE_CASE__     )     ).values()
				for iteration in range(3     ):
								# Gather the distributed inputs and targs for the base model
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Dict     		=      accelerator.gather((ddp_input, ddp_target)     )
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      input.to(accelerator.device     ), target.to(accelerator.device     )
								# Perform our initial ground truth step in non "DDP"
								step_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(SCREAMING_SNAKE_CASE__     ):
																step_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
								else:
												# Sync grads
												step_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_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(1337 + iteration     )
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE__     )     )]







def 			snake_case_   (  SCREAMING_SNAKE_CASE__=False							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      Accelerator(
				    split_batches=SCREAMING_SNAKE_CASE__							,      dispatch_batches=SCREAMING_SNAKE_CASE__							,      gradient_accumulation_steps=2     )
				# Test that context manager behaves properly
				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      get_training_setup(SCREAMING_SNAKE_CASE__     )
				for iteration, batch in enumerate(SCREAMING_SNAKE_CASE__     ):
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Any     		=      batch.values()
								# Gather the distributed inputs and targs for the base model
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[int]     		=      accelerator.gather((ddp_input, ddp_target)     )
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      input.to(accelerator.device     ), target.to(accelerator.device     )
								# Perform our initial ground truth step in non "DDP"
								step_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(SCREAMING_SNAKE_CASE__     ):
												step_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_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(SCREAMING_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(1337 + iteration     )
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE__     )     )]
				GradientState._reset_state()







def 			snake_case_   (  SCREAMING_SNAKE_CASE__=False							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[Any]     		=      Accelerator(
				    split_batches=SCREAMING_SNAKE_CASE__							,      dispatch_batches=SCREAMING_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 :       int     		=      get_training_setup(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				for iteration, batch in enumerate(SCREAMING_SNAKE_CASE__     ):
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[int]     		=      batch.values()
								# Gather the distributed inputs and targs for the base model
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      accelerator.gather((ddp_input, ddp_target)     )
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[int]     		=      input.to(accelerator.device     ), target.to(accelerator.device     )
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
								opt.step()

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







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      Accelerator()

				_SCREAMING_SNAKE_CASE :       List[str]     		=      RegressionDataset(length=80     )
				_SCREAMING_SNAKE_CASE :       int     		=      DataLoader(SCREAMING_SNAKE_CASE__							,      batch_size=16     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      RegressionDataset(length=96     )
				_SCREAMING_SNAKE_CASE :       Dict     		=      DataLoader(SCREAMING_SNAKE_CASE__							,      batch_size=16     )
				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      accelerator.prepare(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(SCREAMING_SNAKE_CASE__     ):
								assert id(accelerator.gradient_state.active_dataloader     ) == id(SCREAMING_SNAKE_CASE__     )
								if iteration < len(SCREAMING_SNAKE_CASE__     ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE__     ):
																				assert id(accelerator.gradient_state.active_dataloader     ) == id(SCREAMING_SNAKE_CASE__     )
																				if batch_num < len(SCREAMING_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 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      Accelerator()
				_SCREAMING_SNAKE_CASE :       List[str]     		=      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(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE__							,      SCREAMING_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(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				main()


if __name__ == "__main__":
				main()








'''simple docstring'''


import unittest

from knapsack import greedy_knapsack as kp





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      [10, 20, 30, 40, 50, 60]
											_SCREAMING_SNAKE_CASE :       List[str]     		=      [2, 4, 6, 8, 10, 12]
											_SCREAMING_SNAKE_CASE :       str     		=      100
											self.assertEqual(kp.calc_profit(__snake_case    ,					__snake_case    ,					__snake_case  )    ,					210  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""max_weight must greater than zero."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""Weight can not be negative."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""Profit can not be negative."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""max_weight must greater than zero."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(
											    __snake_case    ,					"""The length of profit and weight must be same."""  )


if __name__ == "__main__":
				unittest.main()




