import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	(DPMSolverMultistepScheduler,)
     A_					    =	(('num_inference_steps', 25),)



     def 		UpperCamelCase_    (       self					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					{
               '''num_train_timesteps''': 10_00,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
               '''sample_max_value''': 1.0,
               '''algorithm_type''': '''dpmsolver++''',
               '''solver_type''': '''midpoint''',
               '''lower_order_final''': False,
               '''lambda_min_clipped''': -float('''inf'''    ),
               '''variance_type''': None,
           }

           config.update(**A_    )
           return config



     def 		UpperCamelCase_    (       self					,					A_=0					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					dict(self.forward_default_kwargs    )
           _lowerCamelCase           =					kwargs.pop('''num_inference_steps'''					,					A_    )
           _lowerCamelCase           =					self.dummy_sample
           _lowerCamelCase           =					0.1 * sample
           _lowerCamelCase           =					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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



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






           """simple docstring"""
           pass



     def 		UpperCamelCase_    (       self					,					A_=0					,					**A_    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					dict(self.forward_default_kwargs    )
           _lowerCamelCase           =					kwargs.pop('''num_inference_steps'''					,					A_    )
           _lowerCamelCase           =					self.dummy_sample
           _lowerCamelCase           =					0.1 * sample
           _lowerCamelCase           =					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 _lowerCamelCase           =					self.get_scheduler_config()
                 _lowerCamelCase           =					scheduler_class(**A_    )
                 scheduler.set_timesteps(A_    )

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

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

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

                 _lowerCamelCase           =					scheduler.step(A_					,					A_					,					A_					,					**A_    ).prev_sample
                 _lowerCamelCase           =					new_scheduler.step(A_					,					A_					,					A_					,					**A_    ).prev_sample

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



     def 		UpperCamelCase_    (       self					,					A_=None					,					**A_    )			->					List[str]:






           """simple docstring"""
           if scheduler is None:
                 _lowerCamelCase           =					self.scheduler_classes[0]
                 _lowerCamelCase           =					self.get_scheduler_config(**A_    )
                 _lowerCamelCase           =					scheduler_class(**A_    )

           _lowerCamelCase           =					10
           _lowerCamelCase           =					self.dummy_model()
           _lowerCamelCase           =					self.dummy_sample_deter
           scheduler.set_timesteps(A_    )

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

           return sample



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					dict(self.forward_default_kwargs    )

           _lowerCamelCase           =					kwargs.pop('''num_inference_steps'''					,					A_    )

           for scheduler_class in self.scheduler_classes:
                 _lowerCamelCase           =					self.get_scheduler_config()
                 _lowerCamelCase           =					scheduler_class(**A_    )

                 _lowerCamelCase           =					self.dummy_sample
                 _lowerCamelCase           =					0.1 * sample

                 if num_inference_steps is not None and hasattr(A_					,					'''set_timesteps'''    ):
                       scheduler.set_timesteps(A_    )
                 elif num_inference_steps is not None and not hasattr(A_					,					'''set_timesteps'''    ):
                       _lowerCamelCase           =					num_inference_steps

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

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

                 _lowerCamelCase           =					scheduler.step(A_					,					A_					,					A_					,					**A_    ).prev_sample
                 _lowerCamelCase           =					scheduler.step(A_					,					A_					,					A_					,					**A_    ).prev_sample

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



     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






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



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           self.check_over_configs(thresholding=A_    )
           for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=A_					,					prediction_type=A_					,					sample_max_value=A_					,					algorithm_type='''dpmsolver++'''					,					solver_order=A_					,					solver_type=A_					,					)



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






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



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






           """simple docstring"""
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                         if order == 3:
                                               continue
                                   else:
                                         self.check_over_configs(
                                             solver_order=A_					,					solver_type=A_					,					prediction_type=A_					,					algorithm_type=A_					,					)
                                   _lowerCamelCase           =					self.full_loop(
                                       solver_order=A_					,					solver_type=A_					,					prediction_type=A_					,					algorithm_type=A_					,					)
                                   assert not torch.isnan(A_    ).any(), "Samples have nan numbers"



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






           """simple docstring"""
           self.check_over_configs(lower_order_final=A_    )
           self.check_over_configs(lower_order_final=A_    )



     def 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           self.check_over_configs(lambda_min_clipped=-float('''inf'''    )    )
           self.check_over_configs(lambda_min_clipped=-5.1    )



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           self.check_over_configs(variance_type=A_    )
           self.check_over_configs(variance_type='''learned_range'''    )



     def 		UpperCamelCase_    (       self    )			->					List[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=A_					,					time_step=0    )



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					self.full_loop()
           _lowerCamelCase           =					torch.mean(torch.abs(A_    )    )

           assert abs(result_mean.item() - 0.3301    ) < 1E-3



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.full_loop(thresholding=A_					,					dynamic_thresholding_ratio=0.87					,					sample_max_value=0.5    )
           _lowerCamelCase           =					torch.mean(torch.abs(A_    )    )

           assert abs(result_mean.item() - 1.1364    ) < 1E-3



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					self.full_loop(prediction_type='''v_prediction'''    )
           _lowerCamelCase           =					torch.mean(torch.abs(A_    )    )

           assert abs(result_mean.item() - 0.2251    ) < 1E-3



     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					self.full_loop(prediction_type='''v_prediction'''					,					use_karras_sigmas=A_    )
           _lowerCamelCase           =					torch.mean(torch.abs(A_    )    )

           assert abs(result_mean.item() - 0.2096    ) < 1E-3



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           _lowerCamelCase           =					DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
           _lowerCamelCase           =					self.full_loop(scheduler=A_    )
           _lowerCamelCase           =					torch.mean(torch.abs(A_    )    )

           assert abs(result_mean.item() - 0.3301    ) < 1E-3

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

           _lowerCamelCase           =					self.full_loop(scheduler=A_    )
           _lowerCamelCase           =					torch.mean(torch.abs(A_    )    )

           assert abs(result_mean.item() - 0.3301    ) < 1E-3



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					self.scheduler_classes[0]
           _lowerCamelCase           =					self.get_scheduler_config(thresholding=A_					,					dynamic_thresholding_ratio=0    )
           _lowerCamelCase           =					scheduler_class(**A_    )

           _lowerCamelCase           =					10
           _lowerCamelCase           =					self.dummy_model()
           _lowerCamelCase           =					self.dummy_sample_deter.half()
           scheduler.set_timesteps(A_    )

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

           assert sample.dtype == torch.floataa





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






           """simple docstring"""
           for scheduler_class in self.scheduler_classes:
                 _lowerCamelCase           =					self.get_scheduler_config(**A_    )
                 _lowerCamelCase           =					scheduler_class(**A_    )

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

from accelerate import Accelerator, DistributedType



class 			UpperCamelCase					:

     '''simple docstring'''

     def __enter__(       self    )			->					int:






           """simple docstring"""
           if self.enabled:
                 _lowerCamelCase           =					self.model.no_sync()
                 self.model_sync_obj.__enter__()

           return self



     def __exit__(       self					,					A_					,					A_					,					A_    )			->					Union[str, Any]:






           """simple docstring"""
           if self.enabled:
                 # Average all models on exit
                 self._sync_and_avg_model_params()
                 self.model_sync_obj.__exit__(A_					,					A_					,					A_    )



     def __init__(       self					,					A_					,					A_					,					A_					,					A_ = True    )			->					List[str]:






           """simple docstring"""
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                 raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
           _lowerCamelCase           =					enabled and accelerator.distributed_type != DistributedType.NO
           _lowerCamelCase           =					0
           if self.enabled:
                 _lowerCamelCase           =					accelerator
                 _lowerCamelCase           =					model
                 _lowerCamelCase           =					local_sgd_steps



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






           """simple docstring"""
           self.num_steps += 1
           if not self.enabled:
                 return

           if self.num_steps % self.local_sgd_steps == 0:
                 self._sync_and_avg_model_params()





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






           """simple docstring"""
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                 for param in self.model.parameters():
                       _lowerCamelCase           =					self.accelerator.reduce(param.data					,					reduction='''mean'''    )