






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     A    (      _lowercase						):






 """simple docstring"""





 __a			:						Any      =						(DDPMParallelScheduler,)
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							{
           """num_train_timesteps""": 10_00,
           """beta_start""": 0.00_01,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
           """variance_type""": """fixed_small""",
           """clip_sample""": True,
       }

       config.update(**A_ )
       return config
 def   _UpperCAmelCase       (							self ):
       for timesteps in [1, 5, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A_ )
 def   _UpperCAmelCase       (							self ):
       for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,     [0.0_02, 0.02, 0.2, 2] ):
             self.check_over_configs(beta_start=A_			,     beta_end=A_ )
 def   _UpperCAmelCase       (							self ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A_ )
 def   _UpperCAmelCase       (							self ):
       for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=A_ )
 def   _UpperCAmelCase       (							self ):
       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A_ )
 def   _UpperCAmelCase       (							self ):
       self.check_over_configs(thresholding=A_ )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=A_			,     prediction_type=A_			,     sample_max_value=A_			,     )
 def   _UpperCAmelCase       (							self ):
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=A_ )
 def   _UpperCAmelCase       (							self ):
       for t in [0, 5_00, 9_99]:
             self.check_over_forward(time_step=A_ )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Tuple					    =							self.get_scheduler_config()
       UpperCamelCase_				:   str					    =							scheduler_class(**A_ )

       assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_09_79 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1E-5
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Optional[int]					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Dict					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   int					    =							len(A_ )

       UpperCamelCase_				:   Dict					    =							self.dummy_model()
       UpperCamelCase_				:   List[Any]					    =							self.dummy_sample_deter
       UpperCamelCase_				:   Dict					    =							self.dummy_sample_deter + 0.1
       UpperCamelCase_				:   Optional[Any]					    =							self.dummy_sample_deter - 0.1

       UpperCamelCase_				:   List[Any]					    =							samplea.shape[0]
       UpperCamelCase_				:   List[str]					    =							torch.stack([samplea, samplea, samplea]			,     dim=0 )
       UpperCamelCase_				:   Optional[Any]					    =							torch.arange(A_ )[0:3, None].repeat(1			,     A_ )

       UpperCamelCase_				:   Dict					    =							model(samples.flatten(0			,     1 )			,     timesteps.flatten(0			,     1 ) )
       UpperCamelCase_				:   Optional[Any]					    =							scheduler.batch_step_no_noise(A_			,     timesteps.flatten(0			,     1 )			,     samples.flatten(0			,     1 ) )

       UpperCamelCase_				:   List[str]					    =							torch.sum(torch.abs(A_ ) )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 11_53.18_33 ) < 1E-2
       assert abs(result_mean.item() - 0.50_05 ) < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Optional[Any]					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Any					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   Optional[Any]					    =							len(A_ )

       UpperCamelCase_				:   int					    =							self.dummy_model()
       UpperCamelCase_				:   Optional[Any]					    =							self.dummy_sample_deter
       UpperCamelCase_				:   Tuple					    =							torch.manual_seed(0 )

       for t in reversed(range(A_ ) ):
             # 1. predict noise residual
             UpperCamelCase_				:   Tuple					    =							model(A_			,     A_ )

             # 2. predict previous mean of sample x_t-1
             UpperCamelCase_				:   Optional[Any]					    =							scheduler.step(A_			,     A_			,     A_			,     generator=A_ ).prev_sample

             UpperCamelCase_				:   str					    =							pred_prev_sample

       UpperCamelCase_				:   Tuple					    =							torch.sum(torch.abs(A_ ) )
       UpperCamelCase_				:   Optional[Any]					    =							torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
       assert abs(result_mean.item() - 0.33_72 ) < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   int					    =							self.get_scheduler_config(prediction_type="""v_prediction""" )
       UpperCamelCase_				:   Optional[Any]					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   Tuple					    =							len(A_ )

       UpperCamelCase_				:   Union[str, Any]					    =							self.dummy_model()
       UpperCamelCase_				:   int					    =							self.dummy_sample_deter
       UpperCamelCase_				:   int					    =							torch.manual_seed(0 )

       for t in reversed(range(A_ ) ):
             # 1. predict noise residual
             UpperCamelCase_				:   str					    =							model(A_			,     A_ )

             # 2. predict previous mean of sample x_t-1
             UpperCamelCase_				:   Tuple					    =							scheduler.step(A_			,     A_			,     A_			,     generator=A_ ).prev_sample

             UpperCamelCase_				:   int					    =							pred_prev_sample

       UpperCamelCase_				:   Optional[int]					    =							torch.sum(torch.abs(A_ ) )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.mean(torch.abs(A_ ) )

       assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
       assert abs(result_mean.item() - 0.26_31 ) < 1E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Tuple					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   Dict					    =							[1_00, 87, 50, 1, 0]

       scheduler.set_timesteps(timesteps=A_ )

       UpperCamelCase_				:   int					    =							scheduler.timesteps

       for i, timestep in enumerate(A_ ):
             if i == len(A_ ) - 1:
                   UpperCamelCase_				:   List[Any]					    =							-1
             else:
                   UpperCamelCase_				:   Optional[int]					    =							timesteps[i + 1]

             UpperCamelCase_				:   List[Any]					    =							scheduler.previous_timestep(A_ )
             UpperCamelCase_				:   Optional[int]					    =							prev_t.item()

             self.assertEqual(A_			,     A_ )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   List[Any]					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Optional[int]					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   Optional[Any]					    =							[1_00, 87, 50, 51, 0]

       with self.assertRaises(A_			,     msg="""`custom_timesteps` must be in descending order.""" ):
             scheduler.set_timesteps(timesteps=A_ )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Any					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Optional[Any]					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   Optional[Any]					    =							[1_00, 87, 50, 1, 0]
       UpperCamelCase_				:   str					    =							len(A_ )

       with self.assertRaises(A_			,     msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
             scheduler.set_timesteps(num_inference_steps=A_			,     timesteps=A_ )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.scheduler_classes[0]
       UpperCamelCase_				:   Tuple					    =							self.get_scheduler_config()
       UpperCamelCase_				:   Optional[Any]					    =							scheduler_class(**A_ )

       UpperCamelCase_				:   Optional[Any]					    =							[scheduler.config.num_train_timesteps]

       with self.assertRaises(
           A_			,     msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""			,     ):
             scheduler.set_timesteps(timesteps=A_ )














'''simple docstring'''

# flake8: noqa
# Lint as: python3
UpperCamelCase							=[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental






