


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')


class 	_a							:

							def __init__(							self:     List[str]   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     bool = False							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				scheduler
									lowercase__						=				optimizers if isinstance(UpperCamelCase_   ,						(list, tuple)							) else [optimizers]
									lowercase__						=				split_batches
									lowercase__						=				step_with_optimizer
									lowercase__						=				GradientState()

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						*UpperCamelCase_:     Optional[int]   ,						**UpperCamelCase_:     Optional[Any]							)      ->       Dict:





									"""simple docstring"""







									if not self.step_with_optimizer:
											# No link between scheduler and optimizer -> just step
											self.scheduler.step(*UpperCamelCase_   ,						**UpperCamelCase_							)
											return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
											if self.gradient_state.adjust_scheduler:
													self.scheduler._step_count += 1
											return

									for opt in self.optimizers:
											if opt.step_was_skipped:
													return
									if self.split_batches:
											# Split batches -> the training dataloader batch size is not changed so one step per training step
											self.scheduler.step(*UpperCamelCase_   ,						**UpperCamelCase_							)
									else:
											# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
											# num_processes steps per training step
											lowercase__						=				AcceleratorState().num_processes
											for _ in range(UpperCamelCase_							):
													# Special case when using OneCycle and `drop_last` was not used
													if hasattr(self.scheduler   ,						'''total_steps'''							):
															if self.scheduler._step_count <= self.scheduler.total_steps:
																	self.scheduler.step(*UpperCamelCase_   ,						**UpperCamelCase_							)
													else:
															self.scheduler.step(*UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Any							)      ->       List[str]:





									"""simple docstring"""







									return self.scheduler.get_last_lr()

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       List[str]:





									"""simple docstring"""







									return self.scheduler.state_dict()

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									self.scheduler.load_state_dict(UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Any							)      ->       Optional[Any]:





									"""simple docstring"""







									return self.scheduler.get_lr()


							def 				lowerCamelCase_			(							self:     Any   ,						*UpperCamelCase_:     List[str]   ,						**UpperCamelCase_:     Any							)      ->       Any:





									"""simple docstring"""







									return self.scheduler.print_lr(*UpperCamelCase_   ,						**UpperCamelCase_							)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									self.assertTrue(torch.allclose(gelu_python(UpperCamelCase_							)   ,						torch_builtin(UpperCamelCase_							)							)							)
									self.assertFalse(torch.allclose(gelu_python(UpperCamelCase_							)   ,						gelu_new(UpperCamelCase_							)							)							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				get_activation('''gelu_10'''							)

									lowercase__						=				torch_builtin(UpperCamelCase_							)
									lowercase__						=				geluaa(UpperCamelCase_							)

									lowercase__						=				torch.where(y_gelu_aa < 10.0   ,						1   ,						0							)

									self.assertTrue(torch.max(UpperCamelCase_							).item() == 10.0							)
									self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,						y_gelu_aa * clipped_mask							)							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									get_activation('''gelu'''							)
									get_activation('''gelu_10'''							)
									get_activation('''gelu_fast'''							)
									get_activation('''gelu_new'''							)
									get_activation('''gelu_python'''							)
									get_activation('''gelu_pytorch_tanh'''							)
									get_activation('''linear'''							)
									get_activation('''mish'''							)
									get_activation('''quick_gelu'''							)
									get_activation('''relu'''							)
									get_activation('''sigmoid'''							)
									get_activation('''silu'''							)
									get_activation('''swish'''							)
									get_activation('''tanh'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation('''bogus'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation(UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				1
									lowercase__						=				get_activation('''gelu'''							)
									self.assertEqual(acta.a   ,						1							)
									with self.assertRaises(UpperCamelCase_							):
											lowercase__						=				acta.a


