

"""simple docstring"""



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(UniPCMultistepScheduler,)
    UpperCAmelCase						 =					(("num_inference_steps", 2_5),)

    def 				UpperCamelCase	(     self: Optional[int]    ,						**UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         A__										=  {
             """num_train_timesteps""": 10_00,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """solver_order""": 2,
             """solver_type""": """bh2""",
         }

         config.update(**UpperCamelCase       )
         return config

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: Optional[int]=0    ,						**UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


         A__										=  dict(self.forward_default_kwargs       )
         A__										=  kwargs.pop("""num_inference_steps"""    ,						UpperCamelCase       )
         A__										=  self.dummy_sample
         A__										=  0.1 * sample
         A__										=  [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              A__										=  self.get_scheduler_config(**UpperCamelCase       )
              A__										=  scheduler_class(**UpperCamelCase       )
              scheduler.set_timesteps(UpperCamelCase       )
              # copy over dummy past residuals
              A__										=  dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(UpperCamelCase       )
                   A__										=  scheduler_class.from_pretrained(UpperCamelCase       )
                   new_scheduler.set_timesteps(UpperCamelCase       )
                   # copy over dummy past residuals
                   A__										=  dummy_past_residuals[: new_scheduler.config.solver_order]

              A__			,				A__										=  sample, sample
              for t in range(UpperCamelCase    ,						time_step + scheduler.config.solver_order + 1       ):
                   A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample
                   A__										=  new_scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample

                   assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: List[str]=0    ,						**UpperCamelCase: List[Any]       ):






         """simple docstring"""


         A__										=  dict(self.forward_default_kwargs       )
         A__										=  kwargs.pop("""num_inference_steps"""    ,						UpperCamelCase       )
         A__										=  self.dummy_sample
         A__										=  0.1 * sample
         A__										=  [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
              A__										=  self.get_scheduler_config()
              A__										=  scheduler_class(**UpperCamelCase       )
              scheduler.set_timesteps(UpperCamelCase       )

              # copy over dummy past residuals (must be after setting timesteps)
              A__										=  dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(UpperCamelCase       )
                   A__										=  scheduler_class.from_pretrained(UpperCamelCase       )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(UpperCamelCase       )

                   # copy over dummy past residual (must be after setting timesteps)
                   A__										=  dummy_past_residuals[: new_scheduler.config.solver_order]

              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample
              A__										=  new_scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample

              assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Optional[Any]=None    ,						**UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


         if scheduler is None:
              A__										=  self.scheduler_classes[0]
              A__										=  self.get_scheduler_config(**UpperCamelCase       )
              A__										=  scheduler_class(**UpperCamelCase       )

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

         A__										=  10
         A__										=  self.dummy_model()
         A__										=  self.dummy_sample_deter
         scheduler.set_timesteps(UpperCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
              A__										=  model(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       ).prev_sample

         return sample

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__										=  dict(self.forward_default_kwargs       )

         A__										=  kwargs.pop("""num_inference_steps"""    ,						UpperCamelCase       )

         for scheduler_class in self.scheduler_classes:
              A__										=  self.get_scheduler_config()
              A__										=  scheduler_class(**UpperCamelCase       )

              A__										=  self.dummy_sample
              A__										=  0.1 * sample

              if num_inference_steps is not None and hasattr(UpperCamelCase    ,						"""set_timesteps"""       ):
                   scheduler.set_timesteps(UpperCamelCase       )
              elif num_inference_steps is not None and not hasattr(UpperCamelCase    ,						"""set_timesteps"""       ):
                   A__										=  num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              A__										=  [residual + 0.2, residual + 0.15, residual + 0.10]
              A__										=  dummy_past_residuals[: scheduler.config.solver_order]

              A__										=  scheduler.timesteps[5]
              A__										=  scheduler.timesteps[6]

              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample
              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample

              self.assertEqual(output_a.shape    ,						sample.shape       )
              self.assertEqual(output_a.shape    ,						output_a.shape       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  UniPCMultistepScheduler(**self.get_scheduler_config()       )
         A__										=  self.full_loop(scheduler=UpperCamelCase       )
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         assert abs(result_mean.item() - 0.2_464       ) < 1e-3

         A__										=  DPMSolverSinglestepScheduler.from_config(scheduler.config       )
         A__										=  DEISMultistepScheduler.from_config(scheduler.config       )
         A__										=  DPMSolverMultistepScheduler.from_config(scheduler.config       )
         A__										=  UniPCMultistepScheduler.from_config(scheduler.config       )

         A__										=  self.full_loop(scheduler=UpperCamelCase       )
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         assert abs(result_mean.item() - 0.2_464       ) < 1e-3

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         for timesteps in [25, 50, 1_00, 9_99, 10_00]:
              self.check_over_configs(num_train_timesteps=UpperCamelCase       )

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         self.check_over_configs(thresholding=UpperCamelCase       )
         for order in [1, 2, 3]:
              for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                        for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 thresholding=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						sample_max_value=UpperCamelCase    ,						solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						)

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         for solver_type in ["bh1", "bh2"]:
              for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						)
                        A__										=  self.full_loop(
                            solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						)
                        assert not torch.isnan(UpperCamelCase       ).any(), "Samples have nan numbers"

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         self.check_over_configs(lower_order_final=UpperCamelCase       )
         self.check_over_configs(lower_order_final=UpperCamelCase       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
              self.check_over_forward(num_inference_steps=UpperCamelCase    ,						time_step=0       )

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  self.full_loop()
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         assert abs(result_mean.item() - 0.2_464       ) < 1e-3

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.full_loop(prediction_type="""v_prediction"""       )
         A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

         assert abs(result_mean.item() - 0.1_014       ) < 1e-3

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         A__										=  self.scheduler_classes[0]
         A__										=  self.get_scheduler_config(thresholding=UpperCamelCase    ,						dynamic_thresholding_ratio=0       )
         A__										=  scheduler_class(**UpperCamelCase       )

         A__										=  10
         A__										=  self.dummy_model()
         A__										=  self.dummy_sample_deter.half()
         scheduler.set_timesteps(UpperCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
              A__										=  model(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       ).prev_sample

         assert sample.dtype == torch.floataa





    def 				UpperCamelCase	(     self: int    ,						**UpperCamelCase: Tuple       ):






         """simple docstring"""


         for scheduler_class in self.scheduler_classes:
              A__										=  self.get_scheduler_config(**UpperCamelCase       )
              A__										=  scheduler_class(**UpperCamelCase       )

              scheduler.set_timesteps(scheduler.config.num_train_timesteps       )
              assert len(scheduler.timesteps.unique()       ) == scheduler.num_inference_steps









"""simple docstring"""



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






