


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       (EulerDiscreteScheduler,)
      UpperCamelCase								=       10





      def        _lowercase				(		self						:					Tuple					,       **__lowerCamelCase						:					int       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     {
             """num_train_timesteps""": 1_1_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }

         config.update(**__lowerCamelCase       )
         return config





      def        _lowercase				(		self						:					List[Any]       )    ->   int:






         """simple docstring"""







         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__lowerCamelCase       )





      def        _lowercase				(		self						:					str       )    ->   str:






         """simple docstring"""







         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					,       [0.0_002, 0.002, 0.02]       ):
            self.check_over_configs(beta_start=__lowerCamelCase					,       beta_end=__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   List[Any]:






         """simple docstring"""







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





      def        _lowercase				(		self						:					int       )    ->   Tuple:






         """simple docstring"""







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





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






         """simple docstring"""







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

         scheduler.set_timesteps(self.num_inference_steps       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

         UpperCAmelCase							=     torch.sum(torch.abs(__lowerCamelCase       )       )
         UpperCAmelCase							=     torch.mean(torch.abs(__lowerCamelCase       )       )

         assert abs(result_sum.item() - 10.0_807       ) < 1e-2
         assert abs(result_mean.item() - 0.0_131       ) < 1e-3





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






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config(prediction_type="""v_prediction"""       )
         UpperCAmelCase							=     scheduler_class(**__lowerCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

         UpperCAmelCase							=     torch.sum(torch.abs(__lowerCamelCase       )       )
         UpperCAmelCase							=     torch.mean(torch.abs(__lowerCamelCase       )       )

         assert abs(result_sum.item() - 0.0_002       ) < 1e-2
         assert abs(result_mean.item() - 2.2_6_7_6e-0_6       ) < 1e-3





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






         """simple docstring"""







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

         scheduler.set_timesteps(self.num_inference_steps					,       device=__lowerCamelCase       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

         UpperCAmelCase							=     torch.sum(torch.abs(__lowerCamelCase       )       )
         UpperCAmelCase							=     torch.mean(torch.abs(__lowerCamelCase       )       )

         assert abs(result_sum.item() - 10.0_807       ) < 1e-2
         assert abs(result_mean.item() - 0.0_131       ) < 1e-3





      def        _lowercase				(		self						:					Dict       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__lowerCamelCase					,       use_karras_sigmas=__lowerCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps					,       device=__lowerCamelCase       )

         UpperCAmelCase							=     torch.manual_seed(0       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         UpperCAmelCase							=     sample.to(__lowerCamelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase							=     scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     model(__lowerCamelCase					,       __lowerCamelCase       )

            UpperCAmelCase							=     scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase       )
            UpperCAmelCase							=     output.prev_sample

         UpperCAmelCase							=     torch.sum(torch.abs(__lowerCamelCase       )       )
         UpperCAmelCase							=     torch.mean(torch.abs(__lowerCamelCase       )       )

         assert abs(result_sum.item() - 124.52_299_499_511_719       ) < 1e-2
         assert abs(result_mean.item() - 0.16_213_932_633_399_963       ) < 1e-3







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu






@require_cpu
class   __lowercase				(  unittest.TestCase  ):





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






         """simple docstring"""







         UpperCAmelCase							=     torch.nn.Linear(1_0					,       1_0       )
         UpperCAmelCase							=     torch.optim.SGD(model.parameters()					,       0.1       )
         UpperCAmelCase							=     Accelerator()
         UpperCAmelCase							=     accelerator.prepare(__lowerCamelCase       )
         try:
            pickle.loads(pickle.dumps(__lowerCamelCase       )       )
         except Exception as e:
            self.fail(F"""Accelerated optimizer pickling failed with {e}"""       )
         AcceleratorState._reset_state()



