
"""simple docstring"""




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   0
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   3.0







class 			snake_case  (				unittest.TestCase ):

							def _lowercase       (self     ):

									"""simple docstring"""







									self.assertDictEqual(MockClass().to_kwargs()							,							{}     )
									self.assertDictEqual(MockClass(a=2     ).to_kwargs()							,							{'''a''': 2}     )
									self.assertDictEqual(MockClass(a=2							,							b=SCREAMING_SNAKE_CASE_     ).to_kwargs()							,							{'''a''': 2, '''b''': True}     )
									self.assertDictEqual(MockClass(a=2							,							c=2.25     ).to_kwargs()							,							{'''a''': 2, '''c''': 2.25}     )

							@require_cuda
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			GradScalerKwargs(init_scale=10_24							,							growth_factor=2     )
									AcceleratorState._reset_state()
									SCREAMING_SNAKE_CASE_    			=			Accelerator(mixed_precision='''fp16'''							,							kwargs_handlers=[scaler_handler]     )
									print(accelerator.use_fpaa     )
									SCREAMING_SNAKE_CASE_    			=			accelerator.scaler

									# Check the kwargs have been applied
									self.assertEqual(scaler._init_scale							,							10_24.0     )
									self.assertEqual(scaler._growth_factor							,							2.0     )

									# Check the other values are at the default
									self.assertEqual(scaler._backoff_factor							,							0.5     )
									self.assertEqual(scaler._growth_interval							,							20_00     )
									self.assertEqual(scaler._enabled							,							SCREAMING_SNAKE_CASE_     )







							@require_multi_gpu
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__     )]
									execute_subprocess_async(SCREAMING_SNAKE_CASE_							,							env=os.environ.copy()     )


if __name__ == "__main__":
						lowerCAmelCase__				=					DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						lowerCAmelCase__				=					Accelerator(kwargs_handlers=[ddp_scaler])
						lowerCAmelCase__				=					torch.nn.Linear(100, 200)
						lowerCAmelCase__				=					accelerator.prepare(model)

						# Check the values changed in kwargs
						lowerCAmelCase__				=					''
						lowerCAmelCase__				=					model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)

"""simple docstring"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler