

'''simple docstring'''

import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

def 			__UpperCamelCase							(		lowercase__  :					List[str],   lowercase__  :					List[Any]	):






     '''simple docstring'''





     if isinstance(__lowercase,   (list, tuple)	):
          return honor_type(__lowercase,   (move_to_device(__lowercase,   __lowercase	) for t in state)	)
     elif isinstance(__lowercase,   __lowercase	):
          return type(__lowercase	)({k: move_to_device(__lowercase,   __lowercase	) for k, v in state.items()}	)
     elif isinstance(__lowercase,   torch.Tensor	):
          return state.to(__lowercase	)
     return state







class 					lowerCAmelCase       ( torch.optim.Optimizer				):







       def __init__(							self     :	Union[str, Any]  , __lowercase     :	List[str]  , __lowercase     :	List[Any]=True  , __lowercase     :	Tuple=None ):







            """simple docstring"""







            __lowercase     						=optimizer
            __lowercase     						=scaler
            __lowercase     						=AcceleratorState()
            __lowercase     						=GradientState()
            __lowercase     						=device_placement
            __lowercase     						=False
            __lowercase     						=None

            # Handle device placement
            if device_placement:
                 __lowercase     						=self.optimizer.state_dict()
                 if self.accelerator_state.distributed_type == DistributedType.TPU:
                      xm.send_cpu_data_to_device(UpperCamelCase_  , self.accelerator_state.device )
                 else:
                      __lowercase     						=move_to_device(UpperCamelCase_  , self.accelerator_state.device )
                 self.optimizer.load_state_dict(UpperCamelCase_ )







       @property
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            return self.optimizer.state







       @state.setter
       def      snake_case				(							self     :	Any  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=state







       @property
       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            return self.optimizer.param_groups







       @param_groups.setter
       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=param_groups







       @property
       def      snake_case				(							self     :	int ):







            """simple docstring"""







            return self.optimizer.defaults







       @defaults.setter
       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=defaults







       def      snake_case				(							self     :	str  , __lowercase     :	Tuple ):







            """simple docstring"""







            self.optimizer.add_param_group(UpperCamelCase_ )







       def      snake_case				(							self     :	Dict  , __lowercase     :	Dict ):







            """simple docstring"""







            if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
                 xm.send_cpu_data_to_device(UpperCamelCase_  , self.accelerator_state.device )
            self.optimizer.load_state_dict(UpperCamelCase_ )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            return self.optimizer.state_dict()







       def      snake_case				(							self     :	Dict  , __lowercase     :	List[Any]=None ):







            """simple docstring"""







            if self.gradient_state.sync_gradients:
                 __lowercase     						='set_to_none' in inspect.signature(self.optimizer.zero_grad ).parameters
                 if accept_arg:
                      if set_to_none is None:
                           __lowercase     						=False
                      self.optimizer.zero_grad(set_to_none=UpperCamelCase_ )
                 else:
                      if set_to_none is not None:
                           raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.' )
                      self.optimizer.zero_grad()







       def      snake_case				(							self     :	int  , __lowercase     :	List[Any]=None ):







            """simple docstring"""







            if self.gradient_state.sync_gradients:
                 if self.accelerator_state.distributed_type == DistributedType.TPU:
                      __lowercase     						={'closure': closure} if closure is not None else {}
                      xm.optimizer_step(self.optimizer  , optimizer_args=UpperCamelCase_ )
                 elif self.scaler is not None:
                      __lowercase     						=False
                      if self._last_scale is None:
                           # `get_scale` is an async operation requiring full synchronization
                           # on CPU and GPUs before finishing. As a result, we store away
                           # the prior one to reduce the call overhead
                           __lowercase     						=self.scaler.get_scale()
                           __lowercase     						=True
                      self.scaler.step(self.optimizer  , UpperCamelCase_ )
                      self.scaler.update()
                      __lowercase     						=self.scaler.get_scale()
                      if not new_scale:
                           # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                           __lowercase     						=scale_after < self._last_scale
                      __lowercase     						=scale_after

                 else:
                      self.optimizer.step(UpperCamelCase_ )







       def      snake_case				(							self     :	int  , __lowercase     :	Any ):







            """simple docstring"""







            for param_group in self.optimizer.param_groups:
                 __lowercase     						=[parameters_map.get(UpperCamelCase_  , UpperCamelCase_ ) for p in param_group['params']]







       @property
       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            warnings.warn(
                'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
                '`optimizer.step_was_skipped` instead.'  , UpperCamelCase_  , )
            return self._is_overflow







       @property
       def      snake_case				(							self     :	int ):







            """simple docstring"""







            return self._is_overflow







       def __getstate__(							self     :	Dict ):







            """simple docstring"""







            return self.__dict__.copy()







       def __setstate__(							self     :	str  , __lowercase     :	str ):







            """simple docstring"""







            self.__dict__.update(UpperCamelCase_ )





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=scheduler_class(**__lowercase )

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


